NCAR/TN-256 + STRNCAR TECHNICAL NOTE

June 1985

ADMP-85-3

The NCAR Eulerian Regional AcidDeposition Model

The NCAR Acid Deposition Modeling Project

NATIONAL CENTER FOR ATMOSPHERIC RESEARCHBOULDER, COLORADO

U

AIIAcid Deption odeling PectDcpo/ition modeling Project

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CONTENTS

Page

Preface . . . . . . . . . . .* . . . . . . . . . . .. .* * * * . vii

1. PROJECT SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. METEOROLOGY . . . . . . . . . . . . . . . . . . . . . . . .. 12

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2 Mesoscale Model Development . . . . . . . . . . . . . . . . . . 122.2.1 SURFACE . .. . . . . . . . . . . . . . . . . . . . . 142.2.2 DATAMAP . . . . . . . . . . . . . . . . ... 142.2.3 RAWINS .......................... 142.2.4 FLOWIN . . . . . . . . . . .. . . . . . . . . . . . . .. 15

a. DATAFLOW1 . . . . . . . . . . . . . . . . . . . . . . 15b. Initialization .............. .. . . 15c. Boundary Conditions ................ 18

2.2.5 The Mesoscale Model (MM4) ................ 182.2.6 FLOWOUT . . . . . . . . . . . . . . . . . . . . . . . . . 212.2.7 VERIFICATION ......... ..................... 212.2.8 FILTER ......................... 222.2.9 TRAJECTORY ....................... 222.2.10 MODAS .......................... 242.2.11 BUDGET . . . ...................... 24

2.3 Model ing Research ....................... 242.3.1 Uncertainty in Meteorological Transport Data and

Mesoscale Predictability ................ 252.3.2 Boundary Layer Modeling ................. 30

a. PBL Structure and Dynamics ............. 30b. PBL Parameterization ................ 31

2.3.3 Parameterization of Cumulus Convection ......... 312.3.4 Selection of Synoptic Case Studies for Preliminary

Testing of the RADM ................... 312.3.5 Analysis and Simulation of OSCAR Case IV ........ 332.3.6 Calculation of Atmospheric Trajectories ......... 392.3.7 Acid Deposition and Three-Dimensional Transport . . . . . 42

a. Experimental Design ................. 42b. Data Interpretation ................. 42

2.3.8 Tests of the High-Resolution Version of MM4 over Colorado 51a. Synoptic Discussion and Model Description . . . . . . 51b. Model Simulations . . ............ .. 51

2.3.9 Vertical Diffusion Experiments ............. 53

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3. CHEMISTRY . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 59

3.2 Interactive Chemistry Simulation Model (ICSM) ......... 60

3.3 Gas Phase Chemistry ...................... 623.3.1 Description of the Complex Chemical Models ....... 643.3.2 The RADM Chemical Mechanism (Version 1) ......... 653.3.3 Comparison of the RADM Mechanism with Smog Chamber Data . 693.3.4 Comparison of the Chemical Mechanisms .......... 833.3.5 Discussion of Results ................. 85

3.4 Aqueous Chemistry and Cloud Processes ............. 923.4.1 Precipitation Cloud Model ................ 983.4.2 Aqueous Chemistry Model ................. 101

a. Nucleation Effects ................. 101b. Aqueous Equilibrium of Trace Gases ......... 102c. Aqueous Chemical Reactions ............. 106

3.4.3 Integration and Implementation ............. 109

3.5 Dry Deposition . . . . . . . . . . . . . . . . . . . . . . ... 1093.5.1 Resistance Model of Deposition ............. 109

a. Calculation of Aerodynamic Resistance ........ 114b. Calculation of Sublayer Resistance ......... 116c. Estimating Surface Resistance ............ 116

3.5.2 Land Use . . . . . . . . . . . . . . . . . . . . . . . .1163.5.3 Deposition Velocities for Other Trace Gases ....... 1163.5.4 Particulate Sulfate Deposition ............. 119

4. SYSTEM INTEGRATION AND VALIDATION .................. 121

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . .121

4.2 The Transport Deposition Model of the RADM ........... 1224.2.1 Grid Structure and Finite Difference Methods ...... 1234.2.2 Tracer Tests with Artificial Flow Fields ........ 1264.2.3 A Three-Day Simulation Using MM4 Meteorology and

All Components of RADM Except Wet Scavenging ...... 1264.2.4 Conclusions and Future Developments ........... 130

4.3 Data Analysis . . . .. . . . . . . . . . . . . ... . . . . . 1344.3.1 RADM Data Needs ..................... 134

a. Episode Selection and Averaging ........... 135b. RADM Initiation and Execution ............ 135c. RADM Evaluation ................... 135

4.3.2 Analysis of OSCAR Data ... 136

4.4 A Study on the Source-Receptor Relationship in an Euler-ian RADM . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

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5. ENGINEERING MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . 163

5.1 Introduction . . . . . . . . . . 163

5.2 Policy and Assessment Variables and Model Requirements ..... 166

5.3 Concepts for "Engineering" Models ............... 167

5.4 Recommendations for Technical Studies . . . . . . . . . .... 169

6. REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

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PREFACE

The NCAR Acid Deposition Modeling Project is an interdisciplinary projectfunded primarily by the U. S. Environmental Protection Agency (EPA) with co-

funding by the National Science Foundation (NSF). The principal objective of

this project is to develop an Eulerian regional acid deposition model suitablefor assessing source-receptor relationships. The initial task is the con-

struction of a modeling framework integrating an existing and evolving meso-scale meteorological model with a new transport and chemical transformationmodel. This report summarizes progress from May 1983 to December 1984. In anutshell, the project is on schedule and, in selected areas, ahead of the ori-ginal schedule (NCAR, 1983d). The mesoscale meteorological model has been re-coded and is now about three times faster than before. Several scientificstudies have been done using this state-of-the-art model. First-generationsubmodels of gas phase chemistry and aqueous phase chemistry with simple par-

ameterizations of subgrid-scale cloud processing and dry deposition have beencompleted. Whenever possible, these submodels have been evaluated with orcompared to available experimental data and found to be performing well. The

submodels have been integrated into a first-generation Eulerian long-rangetransport and acid deposition model. A first run with the full modeling sys-tem using one set of Oxidant-Scavenging Characteristics of April Rains (OSCAR)data has been completed. The full set of OSCAR data is being analyzed in pre-

paration for model evaluations. We have completed two exhaustive studies onthe possible chemical reactions and aqueous phase reactions and evaluated theavailable data for reaction rate coefficients. We have also developed an an-alysis technique for studying source-receptor relationships within the Euler-ian modeling framework. These studies and others are covered in this report.

This research has been funded by EPA as part of the National Acid Pre-cipitation Assessment Program under Interagency Agreement DW930144-01-1 withNSF and covers the period May 1, 1983 to December 31, 1984. It is the collec-

tive effort of the members of the NCAR Acid Deposition Modeling Project, but Iwould also like to acknowledge the contributions of all of those who have pro-

vided help and guidance to the project since its inception in May 1983. Myspecial thanks go to Dr. Jack Shreffler, our original EPA Project Officer, forhis support; to Dr. John F. Clarke, our current EPA Project Officer, for his

patience and his confidence that we would do what we promised; to Dr. GregoryJ. McRae of Carnegie-Mellon University for providing much-needed help at theright times; to the members of our technical working groups (listed on page 4

of this report) for their timely input and advice on developments along theway; to Dr. Wilmot N. Hess, Director of NCAR, for his enthusiastic support ofour work from the beginning; to the National Oceanic and Atmospheric Adminis-tration Aeronomy Laboratory participants for their excellent contributions to

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our work; and to Ms. Daloris Flaming, the project's Administrative Assistant,for her dedication, patience, and resourcefulness in maintaining the produc-tivity of the project.

Julius S. ChangProject Director

May 1985

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SECTION 1

PROJECT SUMMARY

The NCAR Acid Deposition Modeling Project (ADMP) is an interdisciplinaryproject funded primarily by the Environmental Protection Agency (EPA) with co-funding by the National Science Foundation (NSF) and the National Center forAtmospheric Research (NCAR). The principal objective of this project is todevelop an Eulerian regional acid deposition model suitable for assessingsource-receptor relationships. The initial task is the construction of a mod-eling framework integrating an existing and evolving mesoscale meteorologicalmodel with a new transport and chemical transformation model. All componentsof this modeling system are expected to include state-of-the-art representa-tions of the relevant physical and chemical processes. Much of the requireddevelopment will be based on contributions from a broad cross section of thescientific community.

The project is a follow-up to an earlier ten-month study evaluating ex-isting modeling efforts and recommending a desirable design and managementplan for a comprehensive modeling system. The findings of the earlier studyare contained in two companion reports (NCAR, 1983a and 1983b). At this time,ADMP has fourteen full-time staff, with another thirteen part-time partici-pants from the scientific divisions of NCAR (Atmospheric Analysis and Predic-tion (AAP), Atmospheric Chemistry (ACD), and Advanced Study Program (ASP)) andthe Aeronomy Laboratory of the National Oceanic and Atmospheric Administration(NOAA). Technical collaborations with researchers from universities and otherinstitutions have also been developed and are expected to increase signifi-cantly in the coming year.

The administrative and scientific components of ADMP are:

(1) ADMP Project Office (Julius Chang, director)

e Overall project management

* Development of external interactions with universities, EPA,federal laboratories, and other institutions

* Technical working groups

(2) Mesoscale Meteorological Modeling (Richard Anthes, leader)

* Mesoscale model development

* Selection of synoptic cases

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* Model sensitivity studies

* Uncertainty in mesoscale model simulations

* Model initialization scheme

* Convective planetary boundary layer parameterization

e Verification of mesoscale model

(3) Chemistry of Acid Transformation and Deposition (JackCalvert, leader)

* Gas-phase chemistry module development

* Aqueous-phase chemistry development

* Subgrid-scale processes

* Chemical kinetics simulation model

(4) System Integration and Validation (Julius Chang, leader)

* Overall modeling system structure

* Techniques for analysis of source-receptor relationships

* Concepts for model validation and sensitivity analysis

* Database development

(5) "Engineering" Models (Julius Chang, leader)

* Synthesis of the detailed understanding developed from thecomprehensive Regional Acid Deposition Model (RADM)

* Construction of simple mathematical approximations to all therelevant submodels to facilitate the operation of the "engi-neering" models

* Development of fast-turnaround models ("engineering" models),each of which represents a limited view of the comprehensiveRADM

Table 1.1 lists all the participating members of the project. ADMP hasalso organized five technical working groups covering the following areas:dynamic modeling; clouds, radiation, and precipitation; gas and liquid phasechemistry; heterogeneous processes and deposition; and sensitivity analysisand databases. These working groups are designed for information exchange andenhancement of direct hands-on cooperation between ADMP staff and other re-searchers. Table 1.2 lists the current members of these working groups. Themembership of these working groups will evolve as time goes on according to

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Table 1.1NCAR Acid Deposition Modeling Project Participants

Full-Time Members

Richard BrostJulius ChangJohn del CorralDaloris FlamingHal Hildebrand (student)Debra Hopkins (student)Hsiao-ming HsuIvar IsaksenAnna Kanski (student)Bill KuoMarina SkumanichDarlene Steward (student)Bill StockwellChris Walcek

Part-Time Members

Richard AnthesLinda BathDavid BaumhefnerThomas BettgeJack CalvertRalph CiceroneRonald ErricoPhil HaagensonPaul ette Mi ddl etonStacy WaltersJohn WyngaardE.-Y. Hsie (NOAA)Shaw Liu (NOAA)

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Table 1.2NCAR Acid Deposition Modeling Project Working Group Members

Working Group 1: Dynamic ModelingDr. Gregory R. CarmichaelDr. Robert G. LambDr. Akula VenkatramDr. Thomas T. WarnerDr. Richard A. Brost, ADMP representativeDr. Ying-Hwa Kuo, ADMP representativeDr. John C. Wyngaard, ADMP representative

Working Group 2: Clouds and PrecipitationDr. Terry L. ClarkDr. William R. CottonDr. Jake HalesDr. Harold D. OrvilleDr. Hsiao-ming Hsu, ADMP representative

Working Group 3: Gas andDr. Roger AtkinsonDr. William L. ChameidesDr. Michael R. HoffmanDr. Jennifer A. LoganDr. John SeinfeldDr. Gary Z. WhittenDr. Jack G. Calvert, ADMPDr. William R. Stockwell,

Aqueous Phase Chemistry

representativeADMP representative

Working Group 4: Heterogeneous Processes and DepositionDr. Thomas E. GraedelMr. Bruce B. HicksDr. Alan C. LloydDr. Volker A. MohnenDr. Christopher Walcek, ADMP representative

Working Group 5: Sensitivity Analysis and DatabasesMs. Carmen M. BenkovitzDr. Gregory J. McRaeDr. Richard S. StolarskiDr. Douglas M. WhelpdaleDr. Julius S. Chang, ADMP representativeDr. Paulette B. Middleton, ADMP representative

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the needs of ADMP and the continuing interests of the members.

The above organizational structure is designed according to the followingconcept of a modeling system for assessment:

Detailed Physical Submodels

The Comprehensive Regional Acid Deposition Model (RADM)

"Engineering" Models s

Assessment <

Many of the detailed physical submodels are being developed by other re-searchers funded by the National Acid Precipitation Assessment Program (NAPAP)

(see NAPAP, 1984a and 1984b). When appropriate, NCAR staff also contribute tothis aspect of the modeling program so as to bridge gaps in development sched-ules. NCAR staff also contribute to the basic sciences supporting these sub-models such as gas phase and aqueous phase chemistry models and in-cloud

transformation and transport processes. The bulk of the ADMP at NCAR focuseson the development of the research oriented comprehensive regional acid depo-

sition model (RADM). Fig. 1.1 summarizes the structure of the RADM. RADM is

to incorporate the current and evolving state of knowledge of atmospheric pro-cesses on a regional scale as related to acid deposition. The computer model

structures are so designed as to be easily modifiable to meet various researchand application needs.

The mesoscale meteorological model (dynamical model) uses input data from

surface stations, radiosondes, satellites, and aircraft for variables such aspressure, temperature, horizontal winds, water vapor, cloud cover, and preci-

pitation. After suitable processing of the input data, this dynamical model

computes a set of time-varying 3-D fields of primary meteorological variablessuch as wind, temperature, pressure, water vapor content, and precipitationwith considerably more detail than the initial input data. These variables

can then be used to generate other diagnostic variables that are of direct use

to the acid deposition model. Section 2 of this report summarizes the current

status of the meteorology group. It provides an overview of the new mesoscalemodel (MM4) and the major studies that have been carried out with the model.

Since the basics of this model have already been published in the reviewed

literature, the model descriptions are limited to the new structure and pro-cesses.

In addition to the above meteorological variables, chemical data on reac-

tion rates, atmospheric concentrations, and natural and man-made emissions are

needed as input to the acid deposition model. Section 4 describes the new

acid deposition model of the RADM (or the transport and transformation model).

Since this is a new model with new submodels, it is 'discussed in some detail.The same treatment has been given to the two first-generation chemistry sub-

models in Section 3. The new work on OSCAR chemistry precipitation data anal-

ysis is also described, along with a brief summary of the study of a source-receptor analysis technique for Eulerian models. The recently operationalInteractive Chemistry Simulation Model (ICSM) is also summarized in Section 3.

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Acid Deposilton Model

Fig. 1.1 The Comprehensive Regional Acid Deposition Model (RADM)

__

A separate user manual is in the process of being edited, and a draft versionis in use by some users.

This model, with its comprehensive description of all the currently re-cognized major physical and chemical processes, makes RADM a suitable tool forresearch and a scientifically defensible application model. However, for pol-icy and assessment studies, the RADM would need to be streamlined for computa-tional efficiency and to have the representation of the physical and chemicaldetails suitably simplified. A synthesis of the comprehensive RADM modelingresult would be required, not only to enhance our understanding of the rele-vant physical processes, but also to construct a set of fast-turnaround "en-gineering" models for application or as an introduction to the RADM.

Following several discussions with EPA staff and other interested re-searchers, a workshop was organized to formalize the concepts and the expect-ations for these types of models. The result of this workshop is included inthis report as Section 5. The "engineering" model development project hasbeen funded and is now part of the NCAR Acid Deposition Modeling Project.

Table 1.3 provides a quick guide to the progress of this project. As canbe seen, for the most part the project is on schedule or ahead of the originalschedule. The letter P indicates our estimate on where the tasks are relativeto the work plan. Where appropriate, the specific actions giving rise to theestimates are noted. We were behind schedule on the ICSM development, but nowICSM (version 1) is operational. The only tasks that require added attentionare sensitivity analysis and examination of subgrid-scale processes. Theseareas will receive additional support in the coming year.

The following list highlights some of the year's significant accomplish-ments by participants in ADMP:

e The computer program for the mesoscale meteorological model was re-written, with as much as a factor-of-three gain in execution speed,depending on the options used.

* Basic results from our large-eddy simulation experiments led to newconcepts on planetary boundary layer modules for large-scale modelssuch as the RADM.

* Studies based on the OSCAR field experiments determined that the ex-isting synoptic network and observational frequency are not adequatefor accurate calculations of long-range transport during episodicevents.

* A first-generation Eulerian long-range transport and acid depositionmodel was assembled and has been undergoing extensive evaluation.

* First-generation submodels of gas phase chemistry and aqueous phasechemistry with simple parameterizations of subgrid-scale cloud proces-sing and dry deposition were completed.

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* A first complete examination of OSCAR experiment chemistry data wasdocumented.

* A promising new approach to source-receptor analysis for Eulerian mod-els using specific carrier frequencies as signals from each source wasdeveloped.

These and other results will be described in the following sections ofthis report.

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Table 1.3Summary of the Technical Plan*

1983 1984 1985 1986

3rd Qtr./4th Qtr. 1st Qtr./2nd Qtr./3rd Qtr./4th Qtr. 1st Qtr./2nd Qtr./3rd Qtr./4th Qtr. 1st Qtr./2nd Qtr./3rd Qtr./4th Qtr.

1. System Integration andValidation

A. Preliminary Acid Task Group C-25, C-26, C-27Deposition Model xDevelopment

B. Acid DepositionModel Evaluationand Expansion

C. Numerical Experimentwith the ModelingSystem

D. Conceptual Develop-ment for ValidationProcedure and Sen-sitivity Analysis

E. Sensitivity Analysis

P (first-generation model complete)

Task Group C-6x p---P

xp (started)

Task Group C-33, C-34, C-36, C-40 h ~~X

p XTask Group C-33, C-34, C-36, C-40,

F. Selected Applica-tions for Assessment

G. Documentation ofComplete ModelingSystem

H. Database Preparation

Task Group C-33, C-34, C-40Task Group I

x -x

Task Groups A and B, Task Group C-9, C-21, C-39, C-40, Task Group Dx------- ---------------------- P (OSCAR data analysis)

Table 1.3(continued)

1983 1984 1985 1986

3rd Qtr./4th Qtr. 1st Qtr./2nd Qtr./3rd Qtr./4th Qtr. 1st Qtr./2nd Qtr./3rd Qtr./4th Qtr. 1st Qtr./2nd Qtr./3rd Qtr./4th Qtr.

2. Meteorological Model

A. Mesoscale ModelDevelopment

B. Selection of Synop-tic Cases

C. Initial Testingof All Components

D. Simulation ofSynoptic Cases(Production Runs)

E. Analysis ofProduction Runs

F. Documentation ofMesoscale Model forADMP

Task Group C-1, C-2, C-3, C-4, C-5 P (MM4 frozen)

Task Group C-23 (completed)

x p x

A - ------------------ - - -xpx

x - x

x -- p

3. Chemistry of Acid Trans-formation and Deposition

A. Gas-Phase ChemistryModule Development x Task Group C-ll, C-15, C-35 * P (first-generation module)

B. Aqueous-PhaseChemistry Module x Task Group C-11, C-13Devel opment P (first-generation module)

I.

---

Table 1.3(continued)

1983 1984 1985 1986

3rd Qtr./4th Qtr. 1st Qtr./2nd Qtr./3rd Qtr./4th Qtr. 1st Qtr./2nd Qtr./3rd Qtr./4th Qtr. 1st Qtr./2nd Qtr./3rd Qtr./4th Qtr.

C. Scavenging Pro-cesses

D. Subgrid-ScaleProcesses

E. Chemical KineticsSimulation ModelDevelopment

F. Documentationof ChemicalSubmodul es

4. Related Activities

A. Technical WorkingGroups

B. Modeling Symposium

Task Group C-14, C-16, C-17, C-18, C-19, C-24, C-32, Task Group Dx--- --- ,--,--, P (first-generation wet and dry deposition module)

P x Task Group C-1, C-2, C-3, C-7P Ix

x P (first-generation model)

P (draft copy of user's guide for ICSM) x

Organized x (first meeting) (second meeting)

x (AGU, Dec. 1984)

*The x's indicate starting and completion points while the arrows indicate only the desired completion date with some expectation of further refinrments and developments. The Task Groups and projects are numbered as in NAPAP (1983).

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SECTION 2

METEOROLOGY

2.1 Introduction

The main objectives of the meteorology group of the NCAR Acid DepositionModeling Project are the development of a state-of-the-art mesoscale modelingsystem and the study of the meteorological aspects of the overall model uncer-tainties. In the model uncertainties study (which is closely related to theproblem of mesoscale predictability), we plan to conduct about twelve simula-tions on twelve selected cases, as will be discussed in Section 2.3.4. Thisreport summarizes the efforts of the meteorology group during the past year.It includes work done at NCAR and work done at Penn State under a subcontract.Our efforts can be classified into two categories: model development and mod-eling research. Since the beginning of the project, considerable improvementhas been made to the meteorological (dynamic) model and associated processingprograms. In addition, we have made progress on several important scientificresearch problems related to mesoscale predictability, boundary layer model-ing, air pollution meteorology, and long-range transport. These will be dis-cussed in detail in the following sections.

2.2 Mesoscale Model Development

The meteorological model is based on the mesoscale model originally de-veloped at Penn State (Anthes and Warner, 1978). This model has evolved as acomplex modeling system, including the model itself and both pre- and post-processing programs. Fig. 2.1 shows the structure of the mesoscale meteorolo-gical modeling system. Each box represents a separate major program. Somesecondary auxiliary programs are not included in this diagram. Program DATA-MAP reads in the National Meteorological Center (NMC) global analysis or theNCAR CFM (Community Forecast Model) analysis and interpolates the global dataset to the mesoscale model grid. RAWINS then enhances the global analysis byrawinsonde observations through a successive correction scheme. The objectiveanalysis is performed on isobaric surfaces. The program SURFACE includes twoprograms, TERRAIN and MAPBKG. TERRAIN reads in the topography and land usedata from tapes archived at NCAR and interpolates them to the model grid.MAPBKG prepares the map background for the model domain. FLOWIN, which in-cludes three subprograms, DATAFLOWI, INITIALIZATION, and BOUNDARY, performsthe interpolations from isobaric surfaces to the o surfaces, vertical modeinitialization, and prepares lateral boundary conditions (LBC) from the anal-yses for the mesoscale model.

The MM4 (Mesoscale Model Version 4) dynamic model then carries out themodel integration using the initial and boundary conditions prepared by FLOW-

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NMC glooal or NCAR CFManiv¥sis initialization

ertrain dataat 1 ° or 10mmin. resolution

Rawinsondedata orother oos

NEROSland usedata

Outout data results every 1 2 h

Comoute heat. moistureand vorticitt budgets

Fig. 2.1 Mesoscale Model Flowchart

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IN. The model output follows one of three paths depending on how the data areto be used. The first path is through FLOWOUT and produces microfilm outputand data files for the verification package. The second is through the FILTERand PROCESSOR to drive the acid deposition transport and chemical model. Thethird path is through TRAJECTORY, MODAS, and BUDGET to compute air parcel tra-jectories, do interactive analyses, and prepare heat, moisture, and vorticitybudgets.

2.2.1 SURFACE

Penn State produced two programs, MAPBKG and TERRAIN, included in theprogram SURFACE. MAPBKG was expanded to plot the rawinsonde stations withinthe model domain, overlay the grid mesh used by the model, and plot the masterand nested domain on the same map background.

The TERRAIN program has been expanded to process topography and land usedata at four different resolutions: 5 min, 10 min, 30 min, and 1 degree oflatitude. The land use data will be largely based on the 1-degree data pre-pared by NASA. However, the land use categories throughout the United Statesare based on the NEROS data wherever available. Penn State also prepared alook-up table to relate the surface parameters to land-use categories. Twodifferent analysis schemes are implemented in TERRAIN for the analysis oftopography, a Cressman-type scheme, and a bilinear interpolation scheme.

2.2.2 DATAMAP

The program DATAMAP, developed by Phil Haagenson, interpolates the NMCglobal analysis (routinely archived at NCAR) to the MM4 grid. The global an-alysis provides wind, temperature, and water vapor mixing ratio at ten stand-ard levels from 1000 to 100 mb. Unfortunately, the global data are not avail-able before 1976, and some are occasionally missing. Haagenson developed asubprogram called DATAMISSING to make up the missing data by linear interpola-tion in time between existing 12-h analyses. Another subprogram called SORT-NMC presorts the global analysis. These programs are self-documented, and auser's guide is available from Haagenson.

In order to examine the potential mesoscale forecast skill, it will benecessary to generate MM4 boundary conditions with a large-scale model. Forresearch purposes, that model will be a version of the NCAR CFM (CommunityForecast Model). Since the MM4 and CFM grids differ substantially, RonaldErrico has developed software to interpolate data from the CFM to MM4 grids.The software is internally documented with instructions on how to use it andexplanations as to the functions of each routine.

2.2.3 RAWINS

The RAWINS program enhances the analyses of DATAMAP by analyzing the raw-insonde observations with successive corrections (Cressman, 1959). For thewind fields, a weighting function depending on wind direction and wind speedis used (Benjamin and Seaman, 1985). An objective sounding check based on thevertical consistency of the sounding, originally developed by Rainer Bleck,has been implemented by Haagenson. Penn State developed an improved code for

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the Cressman scheme which reduces the execution time of RAWINS to one-thirdits original time.

Haagenson and Debra Hopkins extensively tested RAWINS and DATAMAP. Theyfound that first guesses provided by DATAMISSING, when no global analysis wasavailable for a particular time, were relatively accurate. They also foundthat RAWINS provides a reasonable analysis over the continental United Stateswhen no first guess is available. The analysis quality is not as good overthe ocean when a first guess is not available.

Haagenson developed a program called NESTED to interpolate the analysisfrom RAWINS to a finer mesh for the one-way or two-way interacting nested gridoptions of the mesoscale model. The program is self-documented. E.-Y. Hsieshowed that the two-way interacting nested grid is essential for small domain(500 km x 500 km) simulations.

2.2.4 FLOWIN

a. DATAFLOW1

Haagenson is responsible for most of the improvements in DATAFLOW1.DATAFLOW1 requires a large amount of core storage because of its handling oflarge three-dimensional data sets. Earlier versions did not use the fullamount of available core, which made DATAFLOW1 more expensive than necessarybecause of the costly I/O waiting. We have installed a new I/O routine forDATAFLOW1 which enables the program to use the maximum core memory availableon the CRAY 1-A; this has reduced the cost of DATAFLOW1 to one-third of itsoriginal cost.

We have improved the calculation of geopotential height in DATAFLOW1 byusing virtual temperature instead of temperature in the hydrostatic equation.The final analysis of geopotential is considerably improved and is quite com-parable with the independently derived operational NMC analysis.

b. Initialization

When initiated from analyzed, but uninitialized, data, the 4MM4 variablesare rather noisy in time. In particular, during the first several hours, thesurface pressure tendencies are approximately five times observed values.This behavior is common to all primitive equation models (Kuo and Anthes,1984a). Errico developed an initialization procedure for MM4 based on theprinciples of nonlinear normal mode initialization to suppress the modelnoise.

The noise problem plagued Richardson's (1922) first attempt to use primi-tive equations to forecast weather. It was not until 1948 that meteorologistsbegan to understand that the cause of the problem was initial dynamic imbal-ances leading to gravity-wave propagation and generation. Many procedures toreduce the initial imbalance have been developed since then, but the most suc-cessful, with the least drawbacks, have been the normal mode techniques ofBaer, Tribbia, and Machenhauer.

15

The specific method used with the MM4 follows closely that of Bourke andMcGregor (1983; their A scheme). Although they do not describe it as such,the procedure determines a hydrostatic geopotential, vorticity, and wind di-vergence that satisfies forms of the quasi-geostrophic w equation and theclassical (Charney) nonlinear balance equation, with the constraint that theprocedure leaves the field of linearized potential vorticity unaltered. Theprocedure contains nonlinear terms not formerly included, which act to createa better balance and simpler methods of solution. However, the most signifi-cant difference between the present and earlier balance procedures appears tobe the inclusion of the potential vorticity constraint. Earlier methods useda simpler vorticity constraint.

The initialization procedure requires the inversion of a three-dimen-sional differential operator. Fortunately, the nature of the operator is suchthat solutions are separable in vertical and horizontal coordinates. The ver-tical structures are determined as the normal modes of the linear operator inthe model's thermodynamic equation. Corresponding to each vertical structureis an equivalent depth. The set of equivalent depths is then used to definesystems of shallow water equations coupled through nonlinear and diabaticterms. These equations completely describe the model, but are in a simplerform to facilitate the application of Machenhauer's (1977) method.

Since the equations are nonlinear, solutions are determined through iter-ation. The simplest iteration method, as applied here, is known as Machen-hauer's method. This method converges sufficiently well, so that for some MM4boundary conditions the initialized fields result in less noise (surface pres-sure tendencies reduced by a factor of 4) with almost no change to the fore-cast, except for the removal of very high frequency oscillations. The resultsof forecasting with and without balanced initial conditions appear in Fig.2.2. This figure shows a forecast of surface pressure at one point in themiddle of the mesoscale model with an 80 km grid. The solid line is for thenoninitialized case, and the dashed line for the initialized case. Note thatthe high frequency component present in the former case is absent in the ini-tialized case. Also note that the low frequency component is unchanged.

There are still many problems of scientific interest regarding initiali-zation. It remains to be demonstrated if initialization can produce betterforecasts as well as reduce noise. If a better forecast is to be obtained, itwill be necessary to invent iteration methods with better convergence proper-ties than Machenhauer's scheme. More important for present purposes, thisinitialization procedure must be substantially modified if it is to be used toreduce noise in the MM4 with a Perkey-Kreitzberg (1976) boundary condition.Fortunately, the formalism of this initialization procedure allows easy anddirect analysis of this problem.

The initialization software package includes a program for determiningthe vertical structure functions, for initializing MM4 with input data sets,for emulating the model for testing purposes, and for running the various pro-grams in an operational model. The code is internally documented with numer-ous instructions, comments, and explanations.

16

976

974

973

972

a 971

a. 970

b.

m 969

968

967

Coto%

0 2 4 6 8 10 12 14

HOURS FROM START

Fig. 2.2 Surface pressure as a function of time atone point in simulation without initialization(solid line) and with initialization (dottedline).

17

16

c. Boundary Conditions

Hsie developed a boundary condition program, BDYOUT, which interpolatesthe analyses at consecutive times from FLOWIN to provide the boundary condi-tions for MM4. The program can process both the analyses from the observa-tions and the forecast of a large-scale model such as the CFM or a coarse meshversion of MM4. When the forecast from the coarse mesh model is used, the MM4can be nested within the coarse mesh model in a one-way interacting mode.

2.2.5 The Mesoscale Model (MM4)

A major accomplishment during the past year has been Hsie's recoding ofthe PSU/NCAR mesoscale model. The new version of the mesoscale model (Version4, MM4) is more user-friendly and computationally efficient than the old ver-sion. It requires about one-third the computational time of the old version,depending on the options used. The new model is written in modular form,i.e., each physical component is written in a separate subroutine. (See Figs.2.3 and 2.4 for the model flow diagram. For a more detailed description ofeach subroutine, a complete documentation for the model is under preparation.)It is relatively easy to modify the model if different physical parameteriza-tions or computational schemes are desired. All the options and parametersneeded by the model are in one subroutine (SUBROUTINE PARAM). The new versionis an in-core model. All the three-dimensional variables are stored in themain computer memory. Thus, a large computer is needed to run the model.

MM4 includes the following options and physical parameterizations:

(1) PBL (Planetary Boundary Layer) parameterizations(a) Frictionless(b) Blackadar (high resolution) PBL model (Zhang and Anthes, 1982)(c) Bulk-aerodynamic PBL

(2) Moisture cycle(a) Dry(b) Nonconvective precipitation(c) Anthes-Kuo-type cumulus parameterization scheme (Anthes, 1977)(d) Explicit scheme containing prognostic equations for cloud water

and rain water (Hsie et al., 1984)

(3) Surface processes(a) No sensible or latent heat fluxes from the ground(b) Sensible and latent heat fluxes from the ground computed with

constant surface characteristics(c) Sensible and latent heat fluxes from the ground computed with

variable surface characteristics

(4) Ground temperature(a) Specified(b) Predicted without cloud effects(c) Predicted with cloud effects

Two major options were also added in the model:

18

Fig. 2.3 Model Flow Diagram of MM4

19

} Forecast

O"N

1I

o O^ itnl itAf %d- Xr

2

-41LW

L Physicalrw - , _ .

_ NumericalSubroutines

Fig. 2.4 Model Flow Diagram of MM4 to Tendand Its Subroutines

20

I

-..O

F- rocesses

40m,

01#14,

_ _

JMao

(1) H.-R. Chang of Penn State developed a two-way interacting nestedgrid option. A nested domain can be put into the main forecastdomain. The coarse-mesh forecast provides the boundary conditionsneeded in the nested domain. The results from the fine-meshforecast feed back into the coarse-mesh domain. Some preliminaryresults are shown in Section 2.3.8.

(2) Hsie developed an explicit treatment of clouds and precipitation op-tion. Two more prognostic equations for cloud water and rain waterwere included. The new model has the capability of forecastingcloud water and rain water explicitly instead of using a cumulusparameterization scheme. During the year, ADMP staff also made sig-nificant improvements in the horizontal diffusion calculation, theBlackadar PBL model, the bulk aerodynamic PBL model, the radiationparameterization, and the cumulus parameterization.

Sarah Gille, a part-time student assistant, prepared partial documenta-tion for MM4. This partial documentation includes three main chapters. Chap-ter Two explains all the parameters needed for the dimension statements.Chapter Three explains all the variables in the COMMON blocks. Chapter Fourlists all the subroutines and describes the purpose and arguments needed inthe subroutines. Two more chapters are needed to show the users how to runthe model and define the parameters.

2.2.6 FLOWOUT

In order to understand mesoscale dynamics and model results regardingmesoscale predictability, it is important to have a method for determiningenergy spectra. The problems of determining spectra on the MM4 grid are thatthe grid is nonuniform and aperiodic. The aperiodicity may be alleviated byremoving an appropriately defined field of linear trends. The nonuniformityof the grid may be ignored under most conditions. Errico (1985) demonstratedthat these solutions to the MM4 grid problems yield useful results. The soft-ware for spectrally decomposing MM4 fields is internally documented, with de-scriptions of its many options and procedures. This documentation also in-cludes instructions for its use.

2.2.7 VERIFICATION

Tom Bettge developed a comprehensive verification package which will beused to compare objectively the predictions from the meteorological model withobserved atmospheric states. There are three major components which comprisethe verification package, and they allow for (1) verification against radio-sonde station data, (2) verification against analyzed data (i.e., griddeddata), and (3) precipitation verification. The package is designed in such amanner that it can accommodate data from a number of different sources, per-form interpolation to a common grid, and compute verification statistics basedupon the component being utilized. At the present time, the package can pro-cess forecasts from the MM4 and the NMC limited-area fine-mesh model (LFM), aswell as observed station and analyzed data from RAWINS. A number of meteoro-logical variables (representative of mass, momentum, and moisture) at severallevels of constant pressure are verified via common statistical measures

21

(i.e., RMS and SI scores). Both the forecast and observed fields can bemapped on the appropriate continental background as well as the difference(forecast minus observed) fields.

In order to compare the various scales of motion in the forecast and ob-served flow regimes, two types of scale decomposition have been incorporated.For station data and forecast data which have been interpolated to station lo-cations, a structure function is computed (Barnes and Lilly, 1976). For anal-yzed observed and forecast data, a two-dimensional Fourier decomposition (Er-rico, 1985) is utilized.

Precipitation is verified by the commonly used threat and bias scores(Anthes, 1983) for various threshold amounts. In addition, the spatial corre-lation between the observed and forecast precipitation fields is computed, aswell as the correlation of various spatial lags between the two.

The verification package is virtually complete. The remaining additionsare ones which require coding for specific, yet at this time unknown, datasets. The documentation is not yet complete, nor is a test in a fully opera-tional mode.

2.2.8 FILTER

As discussed in Section 2.2.4(b), the uninitialized output from MM4 isquite noisy--contaminated with high-frequency inertia-gravity waves. An al-ternative method to initialization filters the model output to remove thehigh-frequency oscillations. Marina Skumanich developed a filter program toremove the high-frequency noise in the model simulation. It is based on themethod discussed by Kuo and Anthes (1984a). We remove all the oscillationswithin periods of less than 6 h. The trajectory computation, using both fil-tered and nonfiltered model output, has shown that the filter has very littleimpact on the computed model trajectories (Kuo et al., 1985).

Anna Kanski and Bill Kuo conducted a 24 h test of the FILTER on the SE-SAME III case. They compared the results of the filtered and unfiltered modelsimulation and found that the filter had very little effect on the mesoscalemeteorological circulations within the model domain (Fig. 2.5). The major im-pact of filter is on the divergence and vertical motion of the model simula-tion. The filtered vertical motion, divergence, and pressure tendency showconsiderably higher consistency with the observed and simulated meteorologicalphenomena.

2.2.9 TRAJECTORY

The trajectory program computes model trajectories using the output fromthe mesoscale model. It was originally written for the CDC7600. Penn Statemodified the code to enable its execution on the CRAY. Kuo and Skumanich mod-ified the trajectory program to process both observed and model data with var-ious simplifying assumptions--isobaric, isosigma, and isentropic. This isdone by replacing the vertical motion with those that are consistent with thesimplifying assumptions.

22

NNN flTT F \ I

11" I.'^. ,' \ , J .j J ,i \ \ x " ",/ ' " '

- -' /"" " ..' . '- / J I} .' · / \ ' - " ",\ 'r. '-,

$ "\ · -' . \ \ / · " ' \ \ \ ' "· ' ."// .' %,, J ' " _ 0

/ '" ~,,- ./ """. . " ' ,%~.,).1.\I/ \ ' - - - 1 - - j

(a) unfiltered

_ _ . ' .,,', ' \f ( r ;. 7'% ;,. ; J i( ' \ \ J N

rA\87 . , \ \ T.

[,, x'^/.. N r 4, , , . w x IJ .- J" I J , '

(b) f iltered^ '

Fig. 2.5 The effect of time filter on model forecast.(a) unfiltered, (b) filtered wind field ata = 0.95 for the 12-h test of the SESAME IIIcase. Dashed lines are isotachs in ms\-.

23

N

/

2.2.10 MODAS

Hopkins modified the interactive graphic analysis program developed in1979 by Joe Klemp of NCAR to process cloud data to process mesoscale data.This program allows the user flexibility and control in creating plots inter-actively. Data from the MM4 are transferred from the CRAY to the VAX 11/780for use with this program. The capabilities include the ability to make two-dimensional contours, maps, vertical cross sections, wind vectors, thermodyna-mic diagrams, and three-dimensional perspectives from any location. It allowsthe user control over the details of the plots in terms of color, overlays,map backgrounds, and many other aspects.

From the fields available from the MM4, the analysis program allows di-agnostic variables, such as divergence, vertical motions, and sea level pres-sure, to be calculated and plotted. Subroutines to calculate other variablescan easily be added. This graphic analysis package is well documented with auser's guide.

2.2.11 BUDGET

The BUDGET programs are based on the heat and moisture budgets discussedin Kuo and Anthes (1984b). Wendell Nuss of NCAR wrote a vorticity budget pro-gram which computes vorticity budgets on both isobaric and a coordinates.

2.3 Modeling Research

Our research over the past year has focused on a number of problems re-lated to acid deposition modeling. First, Anthes et al. (1985) conducted apreliminary study on mesoscale predictability to help plan the model uncer-tainty study and to understand the sensitivity of model simulations to varia-tions in initial and boundary conditions. John Wyngaard proposed a new way ofparameterizing the convective boundary layer based on the results from large-eddy simulations. Kuo and Anthes (1984c) carried out a semi-prognostic testof several variations of the Kuo-type cumulus parameterization scheme. Thesetests help improve the cumulus parameterization schemes used by the mesoscalemodel. Haagenson conducted a climatological survey of the weather patternsover the eastern United States and classified the weather patterns into sev-eral synoptic categories. He then selected twelve cases during the last threeyears for the model-uncertainty study based on his climatological survey. Thesynoptic classification can be used to estimate the annual acid depositionfrom the results of the transport and chemical model.

Kuo and Haagenson carried out preliminary synoptic analyses and modelsimulations on Case IV of the OSCAR '81 experiment. Paulette Middleton stud-ied the chemical aspects of this case, which will be the first test case ofour complete acid deposition model, including meteorology, transport, chemis-try, and deposition. Kuo et al. (1985) performed a theoretical study on theaccuracy of the trajectory models using the model simulation of the OSCARcase. The objective of the study is to understand the uncertainties of long-range transport calculations using the atmospheric data. Haagenson et al.(1985) used the field data from an Acid Precipitation Experiment conducted in1979 to study the relationship between precipitation acidity and three-dimen-

24

sional transport associated with synoptic scale cyclones. Hsie experimentedwith the simulation of an air pollution episode on a meso-3 scale over Colo-rado. Summaries of the above mentioned research are given in the followingsections.

2.3.1 Uncertainty in Meteorological Transport Data and Mesoscale Predicta-bility

When completed, the RADM will be able to provide estimates of changes intotal acid deposition at any point in the domain as a function of changes inemissions anywhere in the domain over various time periods (e.g., days,months, seasons, years). It is important to realize that such estimates willcontain an inherent uncertainty, or expected error, and the statistical signi-ficance of any predicted change in deposition requires knowledge of this un-certainty. As indicated in Fig. 2.6, many factors contribute to the uncer-tainty, including errors in the initial meteorological and chemical data,errors in the emissions, and errors in the model associated with the numericaland physical approximations. A meaningful use of the RADM requires estimatesof the uncertainty contributed by each of these sources of error.

A closely related topic to uncertainty in the three-dimensional meteoro-logical data used to compute the transport and diffusion of the chemical spe-cies in the RADM is the question of mesoscale predictability. It is wellknown that, even with a perfect global numerical weather prediction model (ahypothetical model that represents all physical processes exactly as theyoccur in the real atmosphere), there is an inherent limit in time to the pre-dictability of the atmosphere. This limit exists because the atmosphere cannever be observed completely and accurately on all scales of motion. If atmo-spheric motions and processes were linear, errors at one scale would not betransferred to other scales and (at least theoretically) the observable scalesof motion would have much greater predictability than they do at present.However, the nonlinear nature of atmospheric processes allows energy exchangesamong all scales of motion, so that uncertainty or error in any one scale willeventually contaminate all scales. Because of the existence of atmosphericinstabilities, any error, no matter how small, will eventually grow and con-taminate even a perfect model's forecast.

Most studies of atmospheric predictability have considered synoptic andplanetary scales of motion. In these studies, predictability has referred tothe growth of small differences in the structure of two nearly identical atmo-spheric states. The rate of divergence of pairs of solutions of a numericalmodel which have initial conditions very close to each other is often called"predictability error growth" and is used to estimate the limits to atmospher-ic predictability. Predictability error growth varies with horizontal scale,seasons, geographic location, and synoptic pattern (see review by Anthes,1984b). However, typical estimates for global models indicate a limit topredictability of 10-14 days.

In contrast to the numerous predictability studies using global models,there have been no published systematic studies of predictability error growthrates in limited-area (regional) models. From theoretical studies of turbu-lence and nonlinear energy transfer from small to larger scales, it has been

25

SOURCES OF UNCERTAINTY IN THEREGIONAL ACID DEPOSITION MODEL

MET

AQM 'TRANSPORTDIFFUSIONTRANSFORMATIONREMOVAL __ I

CONCENTRATIONSPACE

Fig. 2.6 Schematic diagram illustrating sources of uncertainty in the RADM.

26

argued that the predictability for mesoscale motions should be less than thatof global scales (see review and discussion by Anthes et al., 1985). Fromthese arguments, one might expect the growth rate of initial errors to befaster in high-resolution limited-area models than in global models.

In order to estimate the uncertainty in the calculation of the three-di-mensional transport of chemical species in the RADM, it is important to deter-mine the uncertainty in the predicted winds that arises from the growth of in-itial errors in the mesoscale meteorological model. This section summarizessome initial predictability studies using a preliminary version (MM4) of themeteorological model that will be used in the RADM. The experiments and re-sults are discussed in considerable detail by Anthes (1984b), Anthes et al.(1985), and Anthes and Kuo (1985).

In the four predictability experiments summarized here, simulations withslightly different initial conditions are integrated for 72 h, and the differ-ences in the pair of solutions are examined. The perturbations are introducedby first adding random perturbations to the relative vorticity field and thencalculating consistent perturbations in the model wind and temperature fields,using the balance equation. This method, which is described in detail by An-thes et al. (1985), projects most of the perturbation energy onto the slow,meteorological modes of the model. The four synoptic cases are summarized inTable 2.1.

This section summarizes the temporal behavior of initial differences inthe MM4. Other important questions such as forecast accuracy and effect ofvarious physical parameterizations are under study but are not discussed here.Therefore, the emphasis is on the temporal evolution of the root-mean-square(RMS) differences in several variables calculated over all grid points on theinterior portion of the domain. This interior grid consists of 38 points inthe north-south direction and 53 points in the east-west direction. All lay-ers are included in the calculation of RMS differences, with data in eachlayer weighted by the mass of that layer.

A somewhat surprising result of these initial predictability experimentsis that the initial differences (or uncertainties) showed little or no growthover the 72-h period. Fig. 2.7 shows the temporal behavior of the RMS errorsfor the horizontal wind components (u and v), surface pressure (ps), groundtemperature (Tg), mixing ratio (q), temperature (T), total rainfall (R), andconvective rainfall (Rc) for the OSCAR case (Table 2.1). Only the accumu-lated rainfall differences, which start out as zero, show any growth over the72-h period. This behavior is typical of all four cases.

Thus, in contrast to the behavior of global models, this preliminarystudy indicates that in at least some cases small random errors or differencesin the initial condition of a limited-area model do not grow significantlyover a 72-h period when the lateral boundary conditions (LBC) are the same.In four cases, nearly identical 72-h simulations are obtained from slightlydifferent initial conditions. In all cases, similar meso-a scale (200-2,000km) features develop in the simulations. These results indicate that, forthese four cases, there is no significant transfer of initial error energyfrom the smallest scales to the larger scales over the two- to three-day

27

Table 2.1

Cases for Predictability Error Growth Studiesin Limited-Area Models

Case Number Initial Time

1 0000 GMT 10 April 1979

2 0000 GMT 22 April 1979

3 0000 GMT 7 May 1979

4 0000 GMT 23 May 1979

SESAME-I experiment, heavy convec-tive precipitation over midwesternU.S.

Moderate rainfall over northeasternU.S. associated with cyclonic stormsystem (OSCAR Case).

Deep trough with precipitation overwestern U.S., ridge over easternU.S.

Deep trough with heavy precipita-tion over eastern U.S.

28

Remarks

1.5 t (a) -

2.0 -

1.51.00.5

0

1.5 ()1.0

0.50

0.50.40.310.20.I0

0 1002 3040 50 6070 80

t(h)

C.)

0

Wr

0.2

O.I

00.40.30.20.10

0.2

0.1

00.2

I I I I I I I '

(b)

I T

I I 1 1 I I I

(d) )

2•3

0 10203040 O60 0 80

t(h)

Fig. 2.7 Temporal variation of RMS differencesbetween control and perturbed simulationsfor Case 2 (Table 2.1). This case isdescribed more completely by Anthes(1984b). (a) u and v components,(b) mixing ratio and temperature,(c) ground temperature (T ) and surfacepressure, and (d) total rainfall (R)and convective rainfall (Rc).

29

I0

E

0.0%

.0

0.

SL

U

II

0.

I .1 ,I I I I 1

I

period.

An optimistic interpretation of the above results is that errors or un-certainties in the initial conditions of the meteorological data will not growduring 72-h simulations with the RADM and produce a major source of uncertain-ty in the RADM simulations. Further experiments are planned using the final-ized version of the RADM on a larger number of cases to confirm these results,as well as to investigate the uncertainty contributed by other aspects of theRADM system (such as the model's physical parameterizations).

2.3.2 Boundary Layer Modeling

During 1984, John Wyngaard carried out some basic studies of the struc-ture and dynamics of the planetary boundary layer, with special emphasis ontransport processes in convective conditions. Much of this work has its rootsin the large-eddy-simulation (LES) studies done by his colleagues RichardBrost and Chin-Hoh Moeng.

a. PBL Structure and Dynamics

LES data generated by Wyngaard and Brost (1984) showed that verticaltransport of conservative scalar species through the convective PBL could berepresented as the sum of "bottom-up" and "top-down" processes driven by thescalar fluxes at the bottom and top, respectively, of the mixed layer. Theyfound that these two processes had quite different diffusivities, presumablybecause of the underlying asymmetry in the vertical distribution of the buoy-ant forcing of turbulence. This finding invalidates eddy-diffusivity closuresfor scalar transport in the convective PBL.

Moeng and Wyngaard (1984a) used these concepts to represent the field ofa turbulent, conservative scalar as the sum of top-down and bottom-up compo-nents. Through LES studies, they isolated the statistics of each in the con-vective PBL, enabling them to develop a generalized "mixed-layer similarity"hypothesis for a scalar field. Their results indicate that a scalar flux ofentrainment is much more effective than a surface flux in generating scalarfluctuations. The results will be useful in developing a modern theory of thetransport of reactive scalars in the PBL.

Herring and Wyngaard (1984) have begun to use full turbulence simulation(FTS) to study the transport of a simple nonconservative scalar species inconvective turbulence. It is known that a nonconservative (e.g., decaying)species has different transport properties than a conservative one, but littleis known about the nature of these differences. The object of these low Rey-nolds number studies is to assess the effect over a range of time-scale ratios(decay time/eddy turnover time). If it is significant, they will turn to LESto investigate the transport of nonconservative scalars in the PBL.

Moeng and Wyngaard (1984b) began a study of the fluctuating pressurefield in the convective PBL, again using Moeng's LES data base. Breaking thepressure field into components driven by the mean wind shear, turbulence,buoyancy, Coriolis effects, and subgrid-scale processes, they can for thefirst time assess the fidelity of second-order closure parameterizations of

30

these contributions to the pressure covariances. They find that the pressurefield is dominated by buoyancy effects in the convective PBL but that thereseem to be simple and effective rationally based second-order closures for thePBL. The authors will present this work, and that of Herring and Wyngaard, atthe Turbulent Shear Flow Conference at Cornell University in 1985.

b. PBL Parameterization

Using the results of the LES study with Brost, Wyngaard developed a verysimple module for calculating scalar transport in the convective PBL (Wyn-gaard, 1984). Basically an integral model, it uses analytical forms for thescalar profiles and uses the scalar conservation equation to predict the sca-lar fluxes at the bottom and top of the "mixed layer," which need not be well-mixed.

In a recent review paper (Wyngaard, 1985), Wyngaard discussed integralmethods, rate equations for PBL depth, transport closures, and other issuescentral to modern PBL parameterization. He stressed the need for balance inmodel building and described the general features of what he thought would bethe next generation of PBL modules used within larger-scale models.

2.3.3 Parameterization of Cumulus Convection

Currently, the cumulus parameterization scheme utilized in the mesoscalemodel is based on the scheme developed by Kuo (1974) and Anthes (1977). Thisis a widely-used scheme. In order to understand its performance over the mid-latitude, Kuo and Anthes (1984c) conducted semi-prognostic tests of severalvariations of the Kuo-type scheme using the heat and moisture budgets asso-ciated with a mid-latitude convective system. In one semi-prognostic test,observed estimates of advection, surface fluxes, and radiational heating wereused to calculate the large-scale forcing. These observations, combined withthe cumulus parameterization scheme, simulate the effect of convection on thelarge-scale systems. The results can then be directly compared against thosediagnosed from the large-scale budgets.

The results show that the convective rainfall rate is closely related tothe vertical advection of moisture and, to a lesser extent, to the large-scalemoisture convergence. The vertical eddy flux of sensible heat is significantfor clouds with large radii. Both the Kuo (1974) and Anthes (1977) schemesshow considerable skill in reproducing the convective heating profile (Fig.2.8) when a moist adiabat is used to represent cloud temperature and moisturestructure, and the effect of eddy sensible heat flux is estimated by asteady-state cloud model with minimal effects of entrainment. However, whena steady-state cloud model with a small cloud radius is used to estimate thecloud properties, both the Kuo (1974) and Anthes (1977) schemes predict a con-vective heating profile with a considerably lower level of maximum heating.This suggests that entrainment is not important, but the effect of eddy sen-sible heat flux is important in the deep extratropical convective systems.

2.3.4 Selection of Synoptic Case Studies for Preliminary Testing of the RADM

We expect that many, if not all, of the results from these simulations

31

5

5

5

i .0aE

'a

- I 2 3

Fig. 2.8 Observed (solid) and simulated Q1 profiles(normalized) by cumulus parameterizationschemes of Kuo (1974, dotted) and Anthes(1977, dashed) with no entrainment, averagedover area of heavy precipitation and overperiods when the observed rainfall rateis greater than 1 mm day - 1. (a) Withouteddy sensible heat flux, and (b) with eddysensible heat flux.

32

1(0

270

351

431

oa 591

671

752

832

912

.AA%

a,

will vary with season and with synoptic pattern. For the initial testing,Haagenson has chosen three distinct synoptic types known to be important inthe acid deposition problem (Table 2.2). The three basic types are wintertimeprecipitation events, summertime precipitation events, and summertime nonpre-cipitating events. Haagenson is preparing a synoptic discussion of each case.We plan to run all variations of the model on the twelve cases, which involvesabout 156 forecasts. At least two of the cases contain special chemical mea-surements, an OSCAR (Oxidant Scavenging Characteristics of April Rains) case,and a NEROS (Northeast Regional Oxidant Study) case. From this ensemble ofcases, it should be possible to make statistically significant statements re-lated to the scientific objectives of the project.

2.3.5 Analysis and Simulation of OSCAR Case IV

One of the major objectives of the OSCAR 1981 experiment is to investi-gate the acid deposition characteristics associated with synoptic-scale cy-clones. Out of the four cases observed in OSCAR 1981, Case IV is the bestcase for the study of cyclonic development. An intense cyclone travelledthrough the special precipitation chemistry network, producing a significantamount of acid deposition during the period of April 22-25, 1981. This caseis ideal for our acid deposition modeling study, for both meteorological andchemical purposes. It is a meteorologically-interesting case with strong cy-clogenesis and frontogenesis during the three-day period. In addition, goodchemical measurements were obtained.

Fig. 2.9 shows the surface analysis at OOZ April 22, 1981. A stronghigh-pressure system dominates the eastern United States. An intense extra-tropical cyclone is located over the Canadian maritime provinces with a mini-mum pressure of 991 mb. This marine cyclone did not move very much over thethree-day period. A weaker and smaller cyclone, named OSCAR storm here, witha central pressure of 997 mb, is situated over the Dakotas. The surface coldfront associated with the OSCAR storm extends from South Dakota through Ne-braska, western Kansas, and into western Texas. In the upper levels (notshown), a major trough is situated over the Montana-Wyoming area right behindthe surface cyclone.

Over the three-day period, the OSCAR storm sweeps over the northernPlains, deepens, and merges with the marine cyclone (Fig. 2.10). The deepen-ing of the OSCAR storm is expected, considering the positive vorticity advec-tion provided by the upper-level trough that moves with the OSCAR storm.Satellite images (Fig. 2.11) at the end of the period (OOZ April 25, 1981)show an intense frontal cloud band over the western Atlantic coast with adistinct dry tongue behind the cold front.

Using the mesoscale model discussed in Section 2.2.5, Kuo (1985) conduct-ed a three-day simulation of this case. Many important large-scale featuresand mesoscale circulations, including the intensity and movement of the cy-clone, the cold front, frontal rainband, dry tongue behind the cold front, andupper-level and low-level jets, are successfully simulated throughout thethree-day period. Even at the end of the 72-h simulation, the model still de-monstrates considerable accuracy (Fig. 2.12). The simulated OSCAR storm mer-ges with the maritime cyclone, although it is stronger than--and lags behind

33

Table 2.2Classification of Synoptic Weather Types for the RADM Project

Classification

I. Winter precipitation (15 Oct-15 Apr); anyoccurrence where the 24 h area-averaged pre-cipitation > 0.05 in. (the verification areais Ohio, Pennsylvania, New York, and SoutheastCanada).

A. Precipitation occurring mostly in the warmsector of the weather disturbance.

1. Precipitation amount 0.05-0.25 in.2. Precipitation amount 0.25-0.75 in.3. Precipitation amount > 0.75 in.

Case Studies

3-5 Mar 1979*23-25 Dec 197925-27 Nov 1979

B. Precipitation occurring mostly in the coldsector of the weather disturbance.

PrecipitationPrecipitationPrecipitation

amount 0.05-0.25 in.amount 0.25-0.75 in.amount > 0.75 in.

12-14 Jan 1979*20-22 Jan 1979*

II. Summer precipitation (15 Apr-15 Oct); anyoccurrence where at least three stations inthe area report 24 h precipitation > 0.50 in.

A. Precipitation occurring mostly in the warmsector of the weather disturbance

B. Precipitation occurring mostly in the coldsector of the weather disturbance.

22-24 Apr 1981(OSCAR Case)28-30 June 197924-26 July 1979

23-25 May 1979*10-12 Aug 1979*

III. Summer dry (15 Apr-15 Oct); primarily stable airmasses with little or no precipitation for 48 hand the area-averaged surface winds showing asoutherly component for at least the last 24 hof the period.

3-5 August 1979

B. Area average dewpoint > 50 F.

*FGGE periods.(WGNE Forecast Comparison Exps. began 12Z 21 Jan 19791979).

8-10 May 1979*

and 12Z 11 June

34

1.2.3.

A. NEROS Case

Fig. 2.9 The surface analysis of OOZ April 22, 1981.level isobars (contour interval 5 mb).

Solid lines are sea-

35

SfA-LEVEL PRESSURE0000 GMTAPRIL 25, 1981

Fig. 2.10 Surface analysis at OOZ April 25, 1981. Solid lines are sea-levelisobars (contour interval 5 mb).

36

Fig. 2.11 Infrared satellite image at OOZ April 25, 1981.

37

/ 1016'f

\ H1020

Fig. 2.12 72-h forecast sea-level pressure for OOZ April 25, 1981.

38

(by about 400 km)--the observed storm. The 400 mb relative humidity clearlyshows the high humidity associated with the frontal rainband and a distinctdry tongue behind the cold front (Fig. 2.13).

2.3.6 Calculation of Atmospheric Trajectories

Increased concern over regional and international aspects of air pollu-tion has created a need for reliable means to determine the long-range trans-port of pollutants. Air parcel trajectories, useful for revealing source-re-ceptor relationships in atmospheric pollution transport and deposition, areoften used to estimate the long-range transport of chemical species. In prac-tice, the accuracy of trajectory models is limited by the temporal and spatialresolution of the meteorological observations, measurement errors, the analy-sis methods used, and the simplifying assumptions.

Kuo et al. (1985) carried out a series of observing systems simulationexperiments (OSSE) to investigate the uncertainties of trajectory computa-tions. In OSSE, the results from the OSCAR case simulation, as discussed inSection 2.3.5, are assumed to represent the "true" atmosphere at the spatialand temporal resolutions of the mesoscale model. Three-dimensional trajec-tories are first computed from this "reference data set" to represent "true"atmospheric trajectories. An example of 72-h trajectories of air parcels ori-ginating in the planetary boundary layer is shown in Fig. 2.14. The modeldata (the "reference data") are then degraded to mimic observations with lowertemporal and spatial resolutions and to simulate actual measurements routinelyavailable from the National Weather Service (NWS). Trajectory computationsare computed from the degraded data set and compared with the "true" trajec-tories to determine the effects of different spatial and temporal resolutionson the calculations. The same procedures are repeated to investigate the ef-fects of various simplifying assumptions on long-range transport estimates.

Kuo et al. (1985) found that, as the temporal and spatial resolutions ofthe data set were degraded, the accuracy of the trajectory model was quicklydegraded. For the experiment that mimics the data base routinely availablefrom the current observational network with imposed measurement errors, theRMS (root mean square) horizontal displacement errors associated with trajec-tories originating from the planetary boundary layer can be as large as 600 kmafter a three-day integration. This suggests that the current synoptic net-work and current observational frequency is inadequate for accurate calcula-tions of long-range transport of episodic events. Careful comparisons indi-cate that the major limitation of the current network is the 12-h observation-al frequency. The results suggest that improving the observational frequencyof the current network will be more cost-effective than improving the spatiaTresolution.

For the three simplifying assumptions--isobaric, isosigma, and isentro-pic--the isentropic trajectory model is shown to be superior to the other twomodels at both upper and middle levels. In the atmospheric boundary layer,the horizontal transport errors are qualitatively the same for all three mod-els. However, for the vertical transport, the isentropic model is again con-siderably better than the other two models.

39

\-. .'k ..

72 h FORECASTRELATIVE HUMIDITYVT 0000 GMTAPRIL 25, 1981

Fig. 2.13 72-h simulated relative humidity (contour interval 10%) forOOZ April 25, 1981.

40

72-h trajectories of parcels of air originating inthe planetary boundary layer at OOZ April 22, 1981(Kuo et al., 1985).

41

Fig. 2.14-

ii

2.3.7 Acid Deposition and Three-Dimensional Transport

Haagenson et al. (1985) employed a statistical approach in combinationwith an isentropic trajectory model to investigate the extent to which three-dimensional transport of air (in association with S02 concentrations, S02emissions, and different sectors of synoptic-scale cyclones) influences preci-pitation acidity. The data were obtained from the Acid Precipitation Experi-ment (APEX, described by Lazrus et al., 1983) conducted in Indiana, Ohio, NewYork, Pennsylvania, West Virginia, and Kentucky during 1979 and 1980. An in-strumented aircraft and a mobile ground unit provided chemical and meteorolo-gical measurements. Most of the meteorological data, however, were obtainedfrom the National Meteorological Center (NMC).

a. Experimental Design

Most of the precipitation in mid-latitude regions is associated with cy-clonic storms. Different types of precipitation are observed in differentareas of a cyclone (Palmen and Newton, 1969). The idealized structure of amid-latitude cyclonic storm is illustrated in Fig. 2.15 (Lazrus et al., 1983).The cold front and the warm front separate air with different meteorologicalcharacteristics into three sectors (designated as A, B, and C). Carlson(1980) has shown that there are significant differences in flow pattern, ver-tical motion, and air origin among storm sectors of a cyclone. Isentropictrajectories calculated for one of the APEX storms (Fig. 2.16) support Carl-son's conceptual model and illustrate the horizontal and vertical transportdifferences between cyclonic storm sectors.

We use synoptic weather maps, meteorological data from an aircraft andNMC, and three-dimensional trajectories calculated from an isentropic trajec-tory model to classify APEX precipitation chemistry samples according to:different sectors of a storm system, descending or ascending motion, boundarylayer or free tropospheric air, slow or rapid transit, transit from clean orpolluted regions, and the transit path relative to S02 emission.

b. Data Interpretation

Fig. 2.17 is a scatter diagram relating precipitation pH values to themean 36-h transit speed (36-h period preceding sample time) of the sampledparcel. The data set is composed of 82 precipitation samples (22 ground and60 aircraft) obtained in sixteen different days. Twenty-nine were collectedin cyclone Sector A (see Fig. 2.15), 40 were from cyclone Sector B, and thir-teen were collected in Sector C. Twenty-five of the 82 samples were obtainedabove the BL (ten in Sector A and fifteen in Sector B). The mean speeds forthe samples were derived from trajectory analysis using the appropriate 0 foreach trajectory.

The regression line in Fig. 2.17 is a first-order least-squares fit withpH values as the dependent variable. The mean absolute residual (a measure ofthe scatter of the sample set) is equal to 0.20 pH units, and the correlationcoefficient equals 0.70. Intuitively, we expect a positive correlaton betweenpH and transit speed. Slow transit usually implies quasi-stagnant situationswhich often lead to elevated pollutant concentrations and consequently higher

42

NN

Cold

.^Cald Air (B) 000,*--Worm Air

<A) X _ (B). -^ '^.Cold AirJ*)-^ ^-- (B--- (B)_- , -)

/-7 7//~," 77777Warm7Air71--111.~"7.11_.

Idealized cyclone. In middle diagram, curved arrowsare streamlines of surface airflow; continuous pre-cipitation is indicated by the stipled areas and '

denotes rain showers. Different air mass sectorsof the cyclone are designated A, B, and C. Top andbottom diagrams are vertical cross-sections alongdirection of cyclone movement north of its centerand across warm sector south of its center.

43

Fig. 2.15

Z\

-36

r i I I

Fig. 2.16 Surface weather map for 1800 GMT on 8 April 1979 and backwardisentropic trajectories constructed for boundary-layer parcels(solid arrow segments) and above boundary-layer parcel (dashedarrow segments) arriving in the indicated regions on 8 April1979. The bracketed numbers are height coordinates in milli-bars of pressure, arrowheads are given in 12-h increments, andthe negative numbers are total elapsed time. Sfc indicatessurface pressure.

44

4.8

4.6

4.4

4.2

pH 4.0

3.8

3.6

0

s -

O.

00

0 0

* *.00 &

00

0

*00 .

* .10

0

0

0.0

Regression0

0

00

0

0

0

00

0

3.4

3.2

I . 0 MeanCorr.

|Res. =0.20 pH UnitsCoeff. =0.70

r0 5 10 15 20 25

MEAN 36 h TRANSIT SPEED (kts)

Fig. 2.17 Scatter diagram relating precipitation pH values to the mean 36-htransit speed (36-h period preceding sample time) of the sampledparcel.

45

* 0

0

0

0

0

Line

30

I I I I I

I I 5

m

-i

I I I II

a

.

I I I I

acidity. Conversely, rapid transit should enhance dispersion of pollutants.

Whenever possible, samples of dry air feeding into the precipitatingregions were obtained. Fig. 2.18 is a scatter diagram, derived from a subset(44 observations) of the sample array used in Fig. 2.17, that relates the S02concentration (parts per billion by volume) in the dry air samples to the pHvalues of associated precipitation samples. We used trajectory analysis todetermine which samples to couple. Since the residual (0.17) is small and thecorrelation coefficient (-0.70) is relatively large, we can argue, based onthis smaller sample set, that S02 concentrations in dry air feeding into acyclonic storm have a significant influence on precipitation pH.

Fig. 2.19 is a scatter diagram, derived from the sample set used in Fig.2.17, relating pH values to the mean 36-h S02 emission concentration densityassigned to each sample as a function of its trajectory path (36-h periodpreceding sample time). We used the annual emissions distribution (Likens etal., 1979) shown in Fig. 2.20 to derive the emission value.

The residual for the S02 emission array was somewhat larger than that forthe S02 concentration array (0.27 versus 0.17 pH units), and the correlationcoefficient was considerably smaller (-0.48 versus -0.70). Clearly, the rela-tionship between S02 in dry air and precipitation pH is established more de-finitively from S02 concentration data than from annual S02 emission data.

Data presented in Table 2.3 are derived from the synoptic and trajectoryanalyses. The vertical velocity, AP/36 h, was calculated from the time-heightcoordinates of the sampled parcels. Indication of whether above- or within-BLtransit occurred was ascertained from the NMC radiosonde data and height co-ordinates of the trajectory. Data given in ic are stratified according tosample collection location with respect to cyclone sector (refer to Fig.2.15).

Sample parcels that had descended or had been transported mainly abovethe BL exhibit lower acidity and smaller standard deviations about pH than anyother classification category. All of the descending sample parcels were col-lected in Sector A several hundred kilometers south of the cyclone center.Most of their descent occurred during the early part of the 36-h period, priorto transport into the convergence region near the frontal zone.

The isentropic trajectories and synoptic analyses revealed significantdifferences in air flow among sectors of a cyclone. These differences, re-lated in part to the three-dimensionality of air motion, contribute to varia-tions in precipitation acidity. Descending air, often observed in Sector A ofa cyclone, is shown to be associated with low acidity.

Among the cyclone Sectors A, B, and C, the highest mean acidity for APEXprecipitation samples was observed in Sector C. The trajectories for samplescollected in Sector C show slow transit speeds (relative to the other sectors)and air flow that confined the same parcels to transport mainly within the BL.This study indicates that both factors can lead to high acidity. BL transportfor air in Sector C is expected because ascent of the cold air, defining Sec-tor C, would be prevented by the overlying warm air in Sector B (refer to Fig.

46

I

0

r

0

0

Regression Line* 0

* .

00

Mean IRes.I 0.17 pH UnitsCorr. Coeff. -0.70

4.5

SO2 CONCENTRATION (ppb)

Fig. 2.18 Scatter diagram relating precipitation pH values to SO2concentration in the associated dry-air samples.

47

4.8

4.6

4.4

4.2

=0. 4.0

0@$ O6

* 0

* 0

0

0

3.8

3.6

3.4

9.0 75 6.0 3.0 1.5 0

I I

I I I~~~~~~~~~~~~~~~~~~~~~

I I I I

I I I I IA3 I

I.

. !

.s· .

40 0: :.

0

Regression

0

* *

* -

.

0

.

0

0

* 00

00

sl Mean IRes.I=0.27 pH Units -

Corr. Coeff.=-0.48

0

I

350 280 210 140 70

MEAN 36 h S 2 EMISSION CONCENTRATION

Fig. 2.19 Scatter diagram relating precipitation pH values to the mean36-h S02 emission concentration density assigned to the sampledparcel as a function of its trajectory path relative to S02emissions. Units are (103 ton yr-/104 mi2).

48

4.8

4.6

4.4

4.2

a

.

0

0 * 4

pH 4.0

I0

0* 0

Line

0* 0

3.8

3.6

.S 0

0

0

3.4

42tO 0

I I I I -

I

I I I I

-

1

I

I I-1 p *

ANNUAL S02 EMISSIONS(kilotons/lO 4 mi2)

Distribution of S02 emissions (103 ton yr- /l04 mi2 ) in theU. S. in 1975 contoured from data obtained by BrookhavenNational Laboratory (BNL).

49

Fig. 2.20

Table 2.3

Mean pH Values of Precipitation SamplesStratified According to Classification Column (82 Samples)

ClassificationNumber

of SamplesStandard

Mean pH Deviation

Descending air, AP/36 h > 100 mbla -

Remainder of total sample array

13

69

*

48-h transit--mostly above BL 30lb - *

48-h transit--mostly within BL 52

Sector (A)--idealized cyclone 29

ic - Sector (B)--idealized cyclone 40

Sector (C)--idealized cyclone 13

- _ -_ _ _ _ -_ _ _ _ -_

*Boundary Layer

50

4.32

3.98

4.38

3.83

4.07

4.07

3.82

- - - -

0.19

0.39

0.23

0.31

0.46

0.34

0.24

- - - - -_

2.15). The most likely reason for the slower transport speeds in Sector C isthat the air in Sector C is often the remnant of a quasi-stagnant anticyclone.

Field data collected during APEX are used in combination with an isentro-pic trajectory model to relate acidity of ground and aircraft precipitationsamples in the north central and northeastern United States to synoptic fea-tures and three-dimensional transport. Statistical analysis of the data indi-cates that high acidity in precipitation is often associated with slow transitspeed, elevated S02 concentration in the dry air feeding into the precipita-ting regions, and Sector C (cold sector) of a storm. The data show that lowacidity is usually related to rapid transit, descending air, and above-bound-ary-layer transport. The data also suggest that the relationship between SO2in dry air and precipitation pH can be established more definitively from S02concentration data than from annual S02 emission data.

2.3.8 Tests of the High-Resolution Version of MM4 over Colorado

The RADM is being coded in a very general way so that it can be appliedto a variety of air quality problems in any region of the world. Hsie, incollaboration with Shaw Liu, Stuart McKeen, Michael Trainer, and Raja Tallam-raju (all of the Aeronomy Laboratory of the Environmental Research Laborator-ies of NOAA), began a study of air pollution transport over the Colorado re-gion. An important objective of this study is to test the nested grid optionof MM4. A high-resolution domain can be nested within the large domain of theNCAR ADMP in order to resolve detailed flow patterns in the subdomain. Thesmaller the domain the more important the boundary conditions are, so for thefirst test the large domain covers the western United States (2,880 km x 1,800km) and the nested domain (720 km x 600 km) covers Colorado (Fig. 2.21). Thegrid size is 60 km for the coarse mesh and 20 km for the fine mesh. Anotherobjective of this study is to investigate the impact on air quality in Colo-rado from pollutants emitted in the Denver metropolitan area.

a. Synoptic Discussion and Model Description

This first test case may be classified as a summer dry case. The initialconditions are 00 GMT on July 24, 1983. The Colorado area is dominated by ahigh pressure system. No significant weather systems propagate into the nest-ed domain over the next 24 hours. The initial low-level winds over Coloradoshow only large-scale features. The version of the model used in these testsincludes the Blackadar PBL model with fifteen layers in the vertical and theAnthes (1977) cumulus parameterization scheme. It utilizes uniform land usedata and five-minute terrain data over the nested domain.

b. Model Simulations

Three simulations are discussed in the following discussions:

(1) a uniform coarse mesh (60 km) covering the large domain (Fig. 2.21),

(2) a uniform fine mesh (20 km) covering the small domain (Fig. 2.21),and

51

Fig. 2.21 Nested grid domains. The grid size of the large domainis 60 km; the grid size of the small domain is 30 km.

52

-··· · ·.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~1

....... ....

............... .................

............

'

.......... : . :

...

..........

II

4I .. .I . ..

........ t -

I.

.. ... .. . . . . .. . . . .. . . . .:. .. .. . .. . ... .. .

............ .I...

........... ! --........... , .

.......... :

(3) a two-way interacting nested model with both coarse and fine meshes(shown in Fig. 2.21).

All the comparisons are made over the Colorado area. In the uniformcoarse-mesh simulation, the general flow is simulated quite well, with thepossible exception of a strong convergence zone over Colorado (compare Figs.2.22a and 2.22b). We note, however, that the observations are inadequate toresolve this circulation if it existed. In the nested grid simulation (Fig.2.22c), this convergence zone is less pronounced, and the simulation agreesquite well with the analysis.

In the uniform fine-mesh simulation, the winds (both magnitude and direc-tion) are wrong in the upper levels, especially over the east portion of thedomain (Figs. 2.23a and 2.23b). Observations show westerly winds over thenorth portion of the domain and anticyclonic flow over the south portion (Fig.2.23a), while the forecast shows an erroneous strong southerly jet. The er-roneous winds are the result of small errors in specifying the pressure andtemperature on the lateral boundaries of a small domain, as discussed by An-thes and Warner (1978).

The low-level wind simulation is improved over the Colorado area in thelarge domain in the two-way interacting nested grid simulation (Fig. 2.22c)due to the feedback from the fine-mesh simulation. In the nested domain,significant improvements are found for the upper-level wind (Fig. 2.23c).

The above results clearly show that the boundary conditions are very im-portant for small domain simulations (less than 1,000 km along one side). Byusing the two-way interacting nested-grid model, the coarse-mesh simulationcan provide accurate boundary conditions for the fine-mesh domain, and thefeedback from the fine-mesh simulation can improve the large domain fore-cast.

2.3.9 Vertical Diffusion Experiments

The transport and chemistry model (RADM) requires vertical diffusion co-efficients as input from MM4. However, in the free convection regime (unsta-ble vertical stratification) of the Blackadar PBL model, MM4 does not computevertical diffusion coefficients. Instead, the Blackadar PBL model uses a mix-ing scheme based on plume theory to mix scalars in the vertical in the freeconvection regime.

Hsie examined the mixing scheme in the Blackadar PBL model. He put atracer into a one-dimensional version of the PBL model and ran the model for24 hours. Mixing coefficients were generated every hour. The same verticalmixing scheme was used to mix the tracer by using the hourly mixing coeffi-cients. The vertical profile for the tracer was nearly the same in these twosimulations.

Brost proposed a new scheme for computing the vertical diffusion coef-ficients as a function of the sensible heat flux and latent heat flux at thesurface. He is testing this new scheme now. If it can reproduce the verticalprofile of the tracer, the new vertical diffusion coefficients will be adopted

53

· ·iil

. .

J/ J JJ ' °/ ' jy -t

' '\ " J I / J j j '" / . - -'8 v yJ JJ j y.y j ^ /// ^^^ ;u"- _ _ --

_ *b )}J gO v i^< y / / - je,7/ : --- /'

: _ y J J^ ^ )lt- 2/ >Z /J * iV ,;to---

^^*^^y 7 y yv^/^ \<^,^^^--~~~48 P-

, ,k ^ 3'.'J J J .y' / J / '--:/< .332/#2 J / ^ '--- / J/ //_

,_0 ,",,.\ \ J /...,

-

!~~~~~~~~~~~~~~~~~ .i J I I I '~' ,. - ,\;/.- -. i -W " 4 * I I i X - X -X \ k i e -

I*~ ~ Observed

Fig. 2.22a

700 mb Forecast winds (ms'l) OOZ 25 July 1983I

I

I : I I II I I III_ I ~ d IF�I

4...

k

4

, / ,, I I l' \ ', x , . ,,. >..v,,_* - *02| ~ \ \ I ~-'d~ J.o\ / , '1 \ \ \'A,,,,- -

._ , /// / i \ i ^ * jut *y / I_ < < --' * ,1 < -^; ,' / / io 21 ~- 97\,- ./.-.,....,,,v............_, ,,--,·:i J , - . 1 ^ *^L, \ , __,>

_. , - : / !M.t^, · .\' I· ~ ~~~~~~~~~~~~~~~~~~~~~~~~~r -.x\ '\~ Oo

i /- ./ I i.~~' i ,$: ;,' I/ " /L//|i

_ / / 2 o _ y \ \ j \ \ \ / 7.-- X.1

'-y J k I Y4 / i · ·, , , ,¥, I , / I I \ J-'" /'J,£JJ/-/~:-T-<<, t^ ~ .\xx ~\\\x .'' yy,' , \\\\\\ \ \ \

'- i/ i -/'-/1y r .Jx. " 2 \ \ \ \ ~ I /* _,IJ9_

«.0

4f

k,

(

N

I

*.

Uniform Coarse Mesh

Fig. 2.22b

4 % i o7

54

700 mb Observed winds (ms"') OOZ 25 July 1983

700 mb winds 24-h forecast V.T. OOZ 25 July 1983iii._ i i i i ! i i .. li

AnJ J J 4 4

Two-Way Nested Grid

Fig. 2.22c

Fig. 2.22 700 mb winds in ms' . (a) Observed, (b) 24-hforecast using uniform coarse mesh, and (c)24-h forecast using two-way nested grid.A full bar represents 10 ms-1.

55

'm.

-W

44

-/ t ,v / v

H ~~16.4

"%%.~~~~ Y(JY~~~~~d#~~~4 ~~V~~ I. ........

·' `a j )XK/ 1..t;J. 4 1.1~~" ~ ~··;·lb) Je-~L~-o., % ww w W

N %81

J *i 1 \ \9~~~~~~~ -

&mo w·~\\\

-.·- H ··~f·2·

250 mb Observed winds OOZ 25 July 1983i

-_

Observed

Fig. 2.23a

Forecast 250 mb winds OOZ 25 July 1983

) r A j j O JUniform Fine Mesh

Fig. 2.23b

56

-~ =~IOL - - 4%M. %W~~~l Immo'C

' " ' 4 " 1 I . . .. . ./ .. /. . /./ ... . . . . . . . . .../. ./.*I i:

looor -< <0A A ,A 0ijSp %

- AAAAAA-

····················· ·· 0 0· ·

~~~~~· · \ i\ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...........

... .... ... ...

rr, r r- ~~004 ;;4, 00^ .

qWO

qwo

4%00

qftoo

Ivv--�

1.0�,iIi

%W-1

%001

vo�iI

VO

100

O ^ ^ y y j j ^ ^ -"^ <'^' . / ',',i;

- .j ' ......... '. ...

' "-i ' J ' I j,/ # ' ,/ v" . \

i--. J J J J J .'y-Y °. ^^^/^ '

~~~~~~~~~~~~W/ /J.

Ij ji j J J \ ' JJv,'J; /i -,J J J J J IJ \ . i \J/42' J ," i; '

I J Ij / J \ ! 1 J;, ] J ) JI K J! J

·J -^J ..1 ! ,1110 ,,,

J .J , , J J I i . i J' i i 'J.J _,.,) !,J I J.~J J.J . j

I i II

24-h Forecast OOZ 25 July 1983

Nested Grid SimulajtionA IFig. 2.23c

Fig. 2.23 250 mb winds (ms"1) over Colorado at 00 GMT25 July 1983. (a) Observed, (b) forecast usinguniform fine mesh, and (c) forecast using nestedgrid.

57

,-v.-.-.....- ...-.'. i --- -t' .. <;.......Nim r,...

- J/ -/ - ^^ -- ^ - -- ̂': ' ' _ ':''

· / ...J . . /..' -mW - - ' .. .. ...... ...- ". " ..

.1 1/ .L48/ ./ \ / '2 / / / / /

.. I 2 . , _- ......., . .. ......... . . ............ ..... ..... .....

L I

\ %N"-- v __ _ %--OLJq - -" T N - '. ' ,^ ,-^ »^ -^ ̂ -*«i--^ ^ . '"- *^ . Ns.i:~~' ."~". st··ye ..... ·.. ~~·.··.~·· ·. · · y~~~~~m ~~~~~~ ~~~~~ N~~i

f I I I '%\ % % A I

I ·

II

in the transport model (RADM) because it simplifies the numerical procedure inthe transport model.

58

SECTION 3

CHEMISTRY

3.1 Introduction

The main objectives of the chemistry group of the NCAR Acid DepositionModeling Project are the incorporation, modification, and development of chem-istry and deposition submodels for the Regional Acid Deposition Model (RADM).Several of the submodels are being developed by research groups both insideand outside of NCAR. All the primary efforts for the past year are directedtoward the development of a first version of the major submodels. Wheneverpossible, we have used data or concepts originally developed by other researchteams under NAPAP. For the time being, we have also developed several majorsubmodels to facilitate the coding and testing of the transport and depositionmodel. When the more detailed and complete submodels become available, wewill then incorporate them as appropriate. At this time, we have developed a40-species, 80-step homogeneous gas phase mechanism (Stockwell, 1985) whichhas been integrated into the transport and deposition model along with a drydeposition submodel. An aqueous phase chemistry and cloud processes submodelhas also been developed (Walcek, 1985), and is in the process of being inte-grated into the transport and deposition model. Both of these submodels areversion one of a sequence of expected improvements. As more understanding ofthe relevant processes develops and more complete mechanisms become available,we will be updating and replacing the existing model components continuously.Examples of this are the cloud processes submodel currently under developmentat Pacific Northwest Laboratory (Hales, 1984) and the chemistry submodels be-ing developed by Jack Calvert at NCAR. Calvert and colleagues are examiningways of simplifying two very comprehensive gas phase and aqueous phase chemi-cal schemes sufficiently so as to meet the computational constraints of theRADM. The two-pronged program on chemical submodels is essential due to thetime constraints for RADM completion and for scientific completeness. Currentsimple mechanisms are needed to establish the proper model structure and todevelop operational experience on a structurally complete model. The morecomprehensive approach is needed to assure that all essential known processeshave been considered and will be accurately simulated. To facilitate thislast task, we have also developed an Interactive Chemistry Simulation Model(ICSM). This stand-alone model allows a user to formulate and solve the timedependent behavior of a uniformly mixed and interacting chemical system. Thedirect man-machine interaction, program prompting, and error trapping capabil-ities significantly simplify the process of mechanism evaluation and testing.Each of these submodels will be discussed in the following sections. Thelonger-ranged chemical submodel development effort has been described in de-tail in a separate report (Kerr and Calvert, 1984).

59

3.2 Interactive Chemistry Simulation Model (ICSM)

ICSM is a computer program which allows a user to interactively enter achemical kinetics reaction mechanism and to specify the time dependent solu-tions desired with various built-in error and consistency checking procedures.The program will then automatically set up the relevant system of differentialequations and solve them numerically. ICSM relieves the user of the tedioustask involved in changing reaction mechanisms and attending to the programmingdetails usually associated with running a computer simulation. All entriesinto a simulation file are made while viewing informative display screens on aterminal. The files created by a user can be stored by ICSM as a database forthat user. These files can be modified by ICSM in future studies either inpart or in total. This program was developed by Stacy Walters with contribu-tions from Kenton Sieckman and John del Corral.

The first version of ICSM calculates the concentrations of chemical spe-cies for any reaction mechanism at a discrete set of internally selected timescovering a specified time interval. The calculated chemical species are se-parated into two groups--"solution" species and PCE (photochemical equilibri-um) species. The governing equation set for the "solution" species is a setof continuity equations describing the rate of change of the individual spe-cies concentrations. PCE species are determined at all times by algebraicrelations derived from the steady-state solutions of the system, i.e., thebalance between chemical production and loss terms. A third class of "fixed"species is specified by users either as a constant or as a time-varying vari-able determined by user-furnished subroutines.

For each reaction in the chemical mechanism, up to three reactants andthree products may be specified. ICSM assumes there is at least one reactantper reaction in the mechanism. Reactions with products that are not of inter-est and hence ignored by the mechanism are allowed. Three reaction types maybe specified easily: photolysis, Arrhenius, and Troe type reactions. In ad-dition, the user may provide special reaction rate coefficients through user-supplied subroutines.

In a typical working session, the user interacts with ICSM through a se-quence of menu screens on the computer terminal. The basic display screenlayout is shown in Fig. 3.1. Each display screen contains reference infor-mation to inform users of where they are in ICSM. Reference information mayinclude the display screen's name, page number, file number, species lists,and other supplemental information on the activities at hand. The workingspace is used for making entries into a simulation file or for accessingvarious options. It also displays the current status of data files that arebeing manipulated. On some screens, the user is able to access lower levelscreens by selecting options or fields in the working space. Manipulationfunctions that are available for any individual screen are displayed in afunction box. Both system (or program) and data manipulation functions arepossible. Data manipulation functions include INPUT, DELETE, CHANGE, DUPLI-CATE, and REPLACE. A special RESTORE function restores the program to thecondition upon entry of this particular screen. Some of the basic systemfunctions are HALT, RETURN, STOP, move CURSORS, move PAGES, FIND, etc. Oncea function is selected, instructions on how to utilize that function are

60

Fig. 3.1 Basic display screen layout for ICSM

61

Reference Information

Working Space (Data Entry)

Gateway to Lower Level Display Screens

Functions

Current Instructions

Warning and Error Messages

displayed in the current instructions box. When no function is active, ICSMinstructs the user on how to invoke functions and access lower level screens.Finally, the warning and error messages for improper functions or entry aredisplayed at the bottom of the screen.

Normally, a user will interact with the following sequence of screens:

(1) Variable specification: prescribes Species names.

(2) Initial condition: specifies the starting values for each variable.

(3) Chemistry mechanism and reaction rate: establishes the specificchemistry mechanism and related reaction rate coefficients.

(4) Simulation control parameters: defines the simulation time inter-val, time steps or frequency of outputs, and the desired level ofaccuracy.

(5) Simulation output: specifies the type of outputs needed (printed,graph, archive) and the specific variables and formats.

Specific information on each screen and further details on ICSM can befound in a preliminary ICSM documentation. Fig. 3.2 shows the basic structureof the computer program. The numerical calculations are performed with a mod-ified ordinary differential equation solver, LSODI, from the Lawrence Liver-more National Laboratory (LLNL). LSODI, originally written by Alan Hindmarshof LLNL has been altered to solve a set of mixed algebraic and differentialequations simultaneously. At this time, ICSM uses an IBM 4341 to carry outthe interactive screen editing functions and then transfers the whole simu-lation package to a CRAY computer. With some effort, ICSM can be transferredto a VAX-CRAY system in the future.

3.3 Gas Phase Chemistry

The NCAR Regional Acid Deposition Model (RADM) is projected to require onthe order of tens of thousands of grid cells. The storage on the CRAY 1 com-puter, for which the model is being developed, is on the order of 700,000words of memory. The available memory therefore places severe limitations onthe number of variables which may be stored for a chemistry module. It isestimated that only 40 chemical variables can be allocated for both liquid andgas phase chemical modules for an in-core (full-memory resident) version ofthe model. The homogeneous gas phase chemical mechanism must store concen-trations of no more than about 25 chemical species, leaving fifteen variablesfor the liquid phase module. These stored species are those which are stableenough to undergo long-range atmospheric transport. Additional species suchas HO radicals have to be included in the mechanism as local photochemicalequilibrium species, and their concentrations have to be recalculated from theothers at each time step. However, the gas phase chemical mechanism must haveenough chemical detail to give accurate oxidation rates of sulfur and nitrogencontaining species. It is also important for the mechanism to give accurateproduction rates of those species such as hydrogen peroxide, organic perox-ides, and ozone which are necessary for the oxidation of sulfur dioxide in

62

SolutionInitial

Conditions

PCEInitial

Conditions

Output I Output I

Completion

Options

Archived

Output

Database

Managej

Fig. 3.2 Basic structure of the ICSM computer program

63

Printed, Plotted

i

liquid phase. The mechanism must provide this high level of chemical realismwhile using a minimum of chemical detail.

William Stockwell has developed a first version of a state-of-the-art gasphase chemical mechanism for use in an acid deposition model and has comparedits behavior to that of two more complex chemical mechanisms. The complexmechanisms are the explicit mechanism of Leone and Seinfeld (1984a) and thecarbon bond mechanism, version CBM-X, of Whitten, Killus, and Johnson (1984).The explicit mechanism was chosen for these tests because it includes explicitdescriptions of dominant atmospheric organic species. It was built upon bothauthors' experiences and the experience of Atkinson et al. (1982) in air qual-ity modeling. The carbon bond mechanism was chosen for these tests because itrepresents a very different approach to the modeling of atmospheric chemistry.The three mechanisms have been tested against smog chamber data by their re-spective authors. The RADM mechanism has been tested against 21 smog chamberexperiments obtained from the Statewide Air Pollution Research Center (SAPRC)of the University of California at Riverside (see, for example, Pitts et al.(1976)). Further testing of the RADM mechanism will be undertaken when thedata of Jeffries et al. (1985a,b) becomes available.

3.3.1 Description of the Complex Chemical Models

The explicit mechanism is a relatively complete mechanism with twelvestable representative organic species. The twelve species are propane, n-butane, 2,3-dimethylbutane, ethene, propene, t-2-butene, toluene, m-xylene,formaldehyde, acetaldeyde, propanaldehyde, and acetone (Leone and Seinfeld,1984a). These species were chosen because they are the dominant species ob-served in the atmosphere around the Los Angeles area. The organic species arenot lumped, and explicit chemical reactions are used for each organic species.Reactions and rate constants are derived from three sources: Baulch et al.(1982), DeMore et al. (1982), and Atkinson and Lloyd (1984). This mechanismhas been successfully tested against smog chamber data obtained from theSAPRC smog chamber (Leone and Seinfeld, 1984b). Eleven experiments weresimulated: SUR-119J and EC-231 to EC-246 (Pitts et al., 1976).

The explicit mechanism has been used as a standard of comparison forphotochemical oxidant chemical mechanisms which have been implemented in airquality models for emissions control calculations (Leone and Seinfeld, 1984a).These mechanisms include Atkinson et al. (1982), Killus and Whitten (1982),Demerjian (1982), McRae and Seinfeld (1983), Penner and Walton (1982), andDodge (1977). The analysis technique was that of counter species described byLeone and Seinfeld (1984a). The results of Leone and Seinfeld were used inthe development of the RADM mechanism as described below.

The explicit mechanism has been adapted to the modeling of atmosphericacid generation. Reactions of methane and ethane with HO radical and thereactions of S02 with HO radical and with Criegee intermediates were added.With these modifications, the mechanism includes 174 total species with atotal of 251 reactions as implemented for the program CHEMK.

The carbon bond mechanism (Whitten et al., 1980; Killus and Whitten,1983; and Whitten et al., 1984) has a very different approach for the modeling

64

of atmospheric organic chemistry. Organic species are grouped according totype and number of carbon bonds in the molecule irrespective of the species inwhich the carbon bond is found. The structural classes were determinedthrough the use of structural reactivity analysis. The first version of thecarbon bond mechanism of Whitten et al. (1980) was designed to be very simple;only 32 reactions were included. More advanced versions of this mechanism usemuch more complex schemes of lumping, and detailed tables must be consulted todetermine the proper lumping of the organic species for complex atmosphericmixtures.

The carbon bond mechanism has several advantages over other lumped mech-anisms. For example, it conserves carbon atoms, it has a narrow range of re-activities for a group of lumped species, and the reactivity of the organicmixture changes in a natural way. This mechanism follows the reactions of thefunctional groups of carbon atoms, and carbon is conserved at each step. Whenmolecules are broken down into their component functional groups, the range ofindividual rate constants, which must be averaged in order to calculate therate constant for the lumped species, is narrowed. The reactivity of atmo-spheric organic mixtures changes with time as the concentrations of the morereactive species are depleted. The reactivity of the organic mixture changesaccording to the concentrations of the different bond types in the carbon bondmechanism. Complex parameterizations of the reactivity such as those used byAtkinson et al. (1982) are not required.

Many versions of the carbon bond mechanism have been extensively used inseveral urban and regional photochemical oxidant models such as the SystemsApplications, Inc. Photochemical Airshed Model. The current version (CBM-X)of the mechanism has been tested extensively against smog chamber data fromthe University of California at Riverside and from the smog chamber of theUniversity of North Carolina (Jeffries et al., 1985a,b). This mechanism asmodified by the inclusion of sulfur dioxide chemistry includes 90 species witha total of 177 reactions.

3.3.2 The RADM Chemical Mechanism (Version 1)

The inorganic chemistry of this mechanism is essentially the same as thatof the complex mechanisms with a few exceptions. Most notably, the productionof nitric acid from N205 and H20 is not allowed. With these changes, we ob-tained better fits of the nitrogen oxide data in smog chambers.

The organic chemistry for the RADM chemical mechanism is represented byseventeen stable species and eight radical intermediates. This number ofclasses and the complexity of the organic chemistry mechanism employed in theRADM chemical mechanism compares well with those of air quality models. Thenumber of lumped groups chosen to represent the organic classes of alkanes,alkenes, aromatics, aldehydes, and ketones was determined by the reactivity ofthe class and the range of rate constants for the species within each class.The classes with high reactivity and with larger ranges of individual rateconstants require representation by larger numbers of lumped groups. Thecounter species analysis of Leone and Seinfeld (1984c) has been used as a mea-sure of the atmospheric reactivity of organic classes and of individual spe-cies. Leone and Seinfeld have determined the number of NO to NO2 conversions

65

affected by individual chemical species as modeled by several chemical mecha-nisms. The conversion of NO to NO2 is both a measure of the amount of ozonegenerated through the photooxidation of the organic species and an indicationof radical production affected by the organic species. H02 radicals generatedby these species are the chain carriers which convert NO to NO2 while produc-ing HO radicals. The rate constants for the explicit mechanism of Leone andSeinfeld (1984b) and reviewed by DeMore et al. (1983) and Kerr and Calvert(1984) were the basis for the rate constants used in the first version of theRADM mechanism.

Leone and Seinfeld (1984a) have shown that one-fourth of the NO to NO2conversions in the air quality mechanisms which they have analyzed result fromthe oxidation of alkanes, making this class very important, although most at-mospheric chemistry mechanisms use a very limited number of species to repre-sent alkanes. The explicit mechanism of Leone and Seinfeld (1984b) has themost detail for alkanes of the mechanisms studied for the present work. Threealkanes are included: propane, n-butane, and 2,3-dimethylbutane. Past mech-anism intercomparison studies suggest that two alkane groups, along with me-thane, should be adequate for the range of atmospheric conditions which arelikely to be important for acid deposition modeling. For the present mechan-ism, alkanes are represented by methane, a species of lower reactivity ETH,and a more reactive species HC. Methane is treated as a fixed species in theRADM mechanism. ETH is treated as though it were ethane, and the more reac-tive alkanes are lumped into HC which has the same reactivity with HO as bu-tane. Ethane, rather than propane, was chosen as the model for the less reac-tive hydrocarbon because of its greater abundance in nonurban areas. Speciessuch as propane and 2,3-dimethylbutane are lumped into the HC group through asum weighted by carbon number and HO reactivity.

Leone and Seinfeld (1984a) showed that there were more conversions of NOto NO2 due to alkenes than for alkanes for the cases which they modeled. Thechanges in the reactivity of the organic mixture due to changes in relativeconcentrations of different alkenes is much greater than that for alkanes.Many of the air quality models use at least two classes for alkenes. Leoneand Seinfeld include three alkenes, ethylene, propylene, and trans-2-butane intheir mechanism. Atkinson et al. (1982) include ethylene, propylene, and aclass for higher alkenes. The carbon bond mechanism treats reactive alkenessomewhat differently (Whitten et al., 1984). The more reactive alkenes areinitialized as their reaction products of aldehydes and ketones. Otheralkenes are lumped into the classes of PAR and OLE, and ethylene is treated asa single class ETH. McRae and Seinfeld (1983) include two classes of alkenes:ethylene and a class for all higher alkenes. Demerjian (1982) uses only onealkene class in which ethylene is lumped with more reactive alkenes. TheDodge (1977) mechanism includes only propylene. Penner and Walton (1982) lumpall alkenes, isoprene, and mono-terpenes into a single class. Leone and Sein-feld have shown that most of the NO to N02 conversions due to alkenes arecaused by at least three organic compounds. Therefore, the RADM mechanismincludes three different groups for alkenes: ethylene, propylene, and otherolefins.

Air quality models differ in the number and complexity of the carbonylchemistry. Atkinson et al. (1982) and the explicit mechanism include several

66

carbonyl species. Atkinson et al. include formaldehyde, acetaldehyde, propi-onaldehyde, acetone, methyl ethyl ketone, and several product carbonyl speciessuch as benzaldehyde. Leone and Seinfeld (1984b) include formaldehyde, ace-taldehyde, propionaldehyde, acetone, and other product aldehydes. The carbonbond mechanism, version CBM-X, includes FORM (formaldehyde), ALD (aldehyde),KET (ketone), and ACET (acetone), along with several other carbonyl products.The Dodge mechanism includes formaldehyde, acetaldehyde, propionaldehyde,butaraldehyde, and several product carbonyl species. Many of the other airquality chemical mechanisms include only one or two aldehyde classes. Demer-jian includes one aldehyde class, McRae and Seinfeld include one class forformaldehyde and one class for higher aldehydes, and Penner and Walton includeonly one class for aldehydes and ketones. The evaluations of Leone and Sein-feld show that acetaldehyde and formaldehyde are the most important carbonylspecies for their model conditions. The contribution of aldehydes to the con-version of NO to NO2 and thus to the total reactivity of the daytime chemistryof the polluted troposphere was relatively small. For these reasons, the al-dehyde chemistry for the RADM mechanism is represented by two carbonyl spe-cies: HCHO (formaldehyde) and ALD (acetaldehyde and all higher aldehydes).The class ALD is modeled as acetaldehyde.

The analysis of Leone and Seinfeld (1984a) has shown that the conversionof NO to N02 affected by aromatic species is very important. Almost a thirdof the NO to NO2 conversions is due to the photooxidation of aromatics. Thephotooxidation of aromatics is very complex and involves many different pro-duct species. Many of these species have complex reaction mechanisms. Atkin-son et al. (1982), Leone and Seinfeld (1984b), and Whitten et al. (1984) usevery detailed aromatic chemistry. Atkinson et al. include chemistry for ben-zene, toluene, and xylene-like species, and Leone and Seinfeld and Whitten etal. include toluene and xylene. This chemistry is very complex, involvingalmost 75 reactions for the mechanism of Leone and Seinfeld. Demerjian (1982)and McRae and Seinfeld (1983) simply produce an organic peroxy radical and analdehyde as products for the reaction of aromatics with HO. Penner and Walton(1982) produce an aromatic radical as the product of the reaction of HO witharomatics which then reacts with NO to produce H02 radicals and methylglyoxal.The mechanism of Dodge (1977) gives very reasonable results for aromatics whencompared with the explicit mechanism without explicit consideration of aroma-tic chemistry according to the evaluations of Leone and Seinfeld. Therefore,the aromatic chemistry for the RADM mechanism could be greatly simplified.The RADM mechanism includes two aromatic species: XYL (a fast reacting aroma-tic species) and TOL (a slower reacting aromatic species). The rate constantsfor the reaction of HO with toluene and xylene differ by a factor greater thanthree. Two different species were included to account for these reactivitydifferences. The reaction of hydroxy radical with the aromatic species re-sults in the production of a fixed number of organic peroxy radicals. Thecorrect number of peroxy radicals to be produced is a function of the HO chainlength under given atmospheric conditions. The number was determined by fit-ting the mechanism to smog chamber experiments containing only toluene or xy-lene as the aromatic hydrocarbon.

An advantage for this choice of stable organic groups for the RADM chem-ical mechanism shown in Table 3.1 is that there is no greater complexity inthe organic chemistry than will be supported by available input data. The

67

TABLE 3.1

RADM STABLE HYDROCARBON SPECIES

REACTANTS MODEL REPRESENTATION

Methane CH4Ethane ETHOther Alkanes HCEthylene OL2Propylene OL3Other Olefins OL4Slow Aromatics TOLFast Aromatics XYLFormaldehyde HCHOOther Aldehydes ALD

PRODUCTS

Formic Acid ORA1Other Organic Acids ORA2Organic Nitrates ONITOrganic Peroxides OPPeroxyacetyl Nitrates PANPeroxynitric Acid HN04Peroxyacetic Acid PAA

68

data which is expected to be available from the United States EnvironmentalProtection Agency (EPA) for early testing of the model includes informationfor only a few classes. The chemical mechanism for the EPA/NCAR Regional AcidDeposition Model (RADM) is given in Table 3.2.

The RADM mechanism has been tested against the smog chamber cases listedin Table 3.3. The photolysis rate constants in each case were scaled to theexperimentally determined value for N02 according to the ratios given by Leoneand Seinfeld (1984a). Chamber dependent terms were also chosen according toLeone and Seinfeld. The most important test variables for the comparison ofthe smog chamber data with simulation results were 03 concentrations, hydro-carbon levels (as a measure of the H02 and HO levels), and N02 and NO concen-trations.

All chemical simulations for this study were run on a version of CHEMK(Whitten et al., 1984). CHEMK was modified in several ways for these simula-tions to correct problems with temperature variation and calculation of photo-lytic rate constants and to improve the archived data file. The outputs ofspecies concentrations, derivatives, and rates of each individual reactionrate as a function of time were used as input to a data analysis program CHEM-CAL. CHEMCAL greatly simplifies the calculation of lifetimes, reaction pathanalysis, and the intercomparison of model results with experimental data.Data files produced by modified CHEMK can be read by CHEMCAL. CHEMCAL allowsfor arithmetic operations upon the chemical variables. An integration func-tion is also provided, and counter species analysis of chemical mechanisms canbe performed through the use of CHEMCAL. CHEMCAL makes use of the NCAR graph-ics package to plot chemical variables and experimental results.

3.3.3 Comparison of the RADM Mechanism with Smog Chamber Data

The fit of the RADM mechanism to SAPRC smog chamber run EC-237 is shownin Figs. 3.3 through 3.12. Fig. 3.3 shows a comparison of the simulation tothe experimental data for NO and N02. The agreement between the simulationand the experimental results is good. The variable to which the NO and N02levels appeared to be most sensitive was the rate constant for the reaction ofN205 with H20. The agreement between the simulation and the experimental re-sults was greatly improved with the rate constant for this reaction set tozero. The agreements were good for all of the smog chamber runs consideredhere except for cases EC-242, EC-331, EC-344, and EC-345 where the total con-centrations of aromatics were extremely high. The rate of conversion of NO toN02 for these cases with high aromatics concentration levels was too rapid.Fig. 3.4 compares the simulated ozone levels with the experimental values, andthe agreement is reasonable. For all of the smog chamber experiments, thereappeared to be a slight bias toward higher ozone predicted by the simulation.These differences in the predicted and observed ozone levels may be due todifficulties in estimating chamber-dependent wall sources and in uncertaintiesin the reported photolytic rate constants for N02. Fig. 3.5 shows the resultsfor hydrocarbon oxidation. The agreement for hydrocarbon oxidation is reason-able. However, the hydrocarbon oxidation rate in the simulation is somewhatslower than in the smog chamber because of the greater reactivity of 2,3-di-methylbutane which is not explicitly modeled. Results for ethylene and forbutene for smog chamber run EC-237 are shown in Figs. 3.6 and 3.7, respective-

69

TABLE 3.2

RADM HOMOGENEOUS GAS PHASE MECHANISM

K298* E/Rt

+ .7 03

+++++

COCOHOHOHO

4.290E+043.250E+052.660E+011.010E+022.960E+001.230E+041.460E+033.180E+004.450E+032.460E-012.510E+037.090E+037.280E-104.730E-022.960E+04

+ 02 5.910E-011.770E+033.120E+00O.OOOE+001.630E+041.920E+021.600E+033.550E+021.180E+013.990E+023.840E+031.180E+044.290E+041.030E+059.470E+033.550E+04

+ H20 1.480E+042.360E+04

+ CO 9.310E-01

1.370E+039.400E+025.800E+02

-2.400E+02-6.900E+029.900E+03

-7.730E+02-2.978E+031.870E+02

-5.300E+022.450E+03

1.230E+036.000E+011.084E+04

1.710E+031.260E+035.600E+02

-8.390E+00-1.330E+01-5.420E+02-8.100E+02

-2.600E+02

+ NO123456789101112131415161718192021222324252627282930313233343536373839404142434445

N0203HONON03H202HCHOHCHOALDOPOPPAAO1DO1D030303H02H02HN04H02H02H202NONO03N03N03N03N205N205HOHOHOCOCH4ETHHCOL2OL3OL4TOLXYLHCHOALDHCHO

+

+

+

+

+

+

+

+

+

+

+

+++++++

+++++++++

++++++++++++++++

hvhvhvhvhvhvhvhvhvhvhvMH20NOHOH02NON02

H02H02HOHONON02NON02N02

H20N02HN03S02HOHOHOHOHOHOHOHOHOHOHON03

=>

,,>

,,>

~>

~>

->

+ H20 -->

+ 02 -->

">">

->

. >

">

->

->

->

->

->

->

->

~>

+ 2-->

.3

2.2.

03O1DHONOHOH2H02M02HCHOALDM0203HON02H02HON02HN04H02H202H202H02HONON02N03N02NON205N02HN03HN03N03SULFH02M02HCPHCPOL2POL3POL4PHCPHCPH02AC03H02

+++++++++

+++++

++++

++++

+

+

+

+

++++

+

++

NO.7 N02

HOCOH02H02H02H02C02

HO0202

2. 02HO

N0202H20H20

N0202N02N02

N03HN03

H20H02C02H20H20H20

COH20HN03

70

TABLE 3.2

(CONTINUED)

AC03 +PANAC03 +HCHO +ALD +ONIT

2. HCHO +2. ALD +2. H02 +

ONITH02 +ONITM02 +OPOPOPOPOPPAA +HCHO +

2. M02 +HCHO +HCHO +ALD +ALD +

.4 CRE1 +1.2 CRE2 +

H02 +1.3 M02

HCHO +ORAl +SULF +ALD +ORA2 +SULF +

HN03

N02H02H02

2.2.

H02HCHON02

2. ALD

N02

02M02

2. C02CR1CR2CR1CR2

.42 CO

.36 CH4

.57 HO

N02H20HCHON02H20ALD

3.700E+008.870E+032.520E-02

+ N02 1.120E+04+ N02 1.030E+04

8.280E+02+ N02 1.120E+04

5.390E+035.390E+034.290E+02

+ N02 1.030E+048.280E+021.420E+042.220E+032.220E+032.220E+032.220E+032.220E+032.220E+03

+ H02 2.220E+032.220E+032.660E-038.130E-038.130E-032.960E-01

- .12 H02 5.000E+07+ .72 CO 5.000E+07i .15 HCHO 5.000E+07

5.000E+071.030E+045.910E-011.030E+041.030E+045.910E-011.030E+04

1.333E+04-1.800E+02-1.800E+02-1.800E+02-1.800E+02-1.800E+02-1.800E+02-1.800E+02-1.800E+02-1.800E+02

2.560E+032.100E+032.100E+031.060E+03

-1.800E+02

-1.800E+02

*UNITS: SECOND-ORDER CONSTANTS, PPM-PRESSURE, 1 ATM; TEMPERATURE, 25°C.

MIN- 1; THIRD-ORDER, PPM-2 MIN- 1

tACTIVATION ENERGY; DIVIDED BY GAS CONSTANT; DIMENSIONLESS.

71

+ N03+ N02

4647484950515253545556575859606162636465666768697071727374757677787980

ALDAC03PANM02HCPHCPOL2POL3POL3POL3POL4POL4PAC03H02H02H02H02H02H02AC03AC03OL2OL3OL3OL4CR1CR2CR2CR2CRE1CRE1CRE1CRE2CRE2CRE2

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+++

NONONONONONONONONONOM02HCPOL2POL3POL4PAC03M02AC0303030303

NOH20S02NOH20S02

,,>,,>,,>

,,>�I)

,,>

,,>

,,>

,,>

,,>

,,>

,,>

,,>

,I>

,,>

,,>

,,>

q

4

TABLE 3.3

SMOG CHAMBER EXPERIMENTS FOR TESTING OF RADM MECHANISM

IDENTIFICATION DESCRIPTIONNUMBER*

EC142 ETHENEEC143 ETHENEEC178 n-BUTANEEC216 PROPENEEC231 MULTI-COMPONENTEC232 MULTI-COMPONENTEC233 MULTI-COMPONENTEC237 MULTI-COMPONENTEC238 MULTI-COMPONENTEC241 MULTI-COMPONENTEC242 MULTI-COMPONENTEC243 MULTI-COMPONENTEC245 MULTI-COMPONENTEC246 MULTI-COMPONENTEC254 ACETALDEHYDEEC305 n-BUTANEEC306 n-BUTANEEC331 TOLUENE + n-BUTANEEC340 TOLUENEEC344 m-XYLENEEC345 m-XYLENE

*REFERENCE NUMBER, STATEWIDE AIR POLLUTION RESEARCH CENTER (SAPRC), UNIVERSITYOF CALIFORNIA, RIVERSIDE; CASES CHOSEN REPRESENT SUPERSET OF ERT TEST CASES.

72

zI-p2r

cr

zu

0 0 1 00 150 200 250 300 350 400

TIME, MIN

Fig. 3.3 Predicted and observed concentration profiles of NO2 and NO

for SAPRC smog chamber experiment EC-237.

73

A e%

a

0

CL-

-i

zLLJ

L;z

0 50 1O 050 0 ]50 O OU U.;u tuu

TIME, MIN

Fig. 3.4 Predicted and observed concentration profiles of ozone forSAPRC smog chamber experiment EC-237.

74

-

I

I

h

0 50 100 150 200 250 300 350

TIME. MIN

Fig. 3.5 Predicted and observed concentration profiles of hydrocarbonfor SAPRC smog chamber experiment EC-237.

75

2.2

2.1

2.0

1.9

1.8

0-

X- 1.7z

¢ 1.6

z,z 1..5

1.4

1.3

1.2

1.1

1.0400

II I - I I I r e m

.\ ------- -- SIMULRTION

-\ I" - + EHPERIMENT

\,'N.

\

\'~~~~~~~~~~~~~~~~~~~~~1

0 50 100 150 200 250 300 350 400TIME. MIN

Fig. 3.6 Predicted and observed concentration profiles of ethene forSAPRC smog chamber experiment EC-237.

76

L.U

.9

.8

z.

: .6

ua

i 5

.4

.3

,5a C.

I tiI

i

I

�� i1iIIiiII

I

I UU

.045

.040

.030

.025

.020

,015

.010

.005

0

-1

I

I, 1 I

I I i I

I

I,

l '\.E li

Fig. 3.7 Predicted and observed concentration profiles of butene forSAPRC smog chamber experiment EC-237.

77

1.,I , ,, I

300 3500 50 1 0 150 200

TIME. MIN250

SIMULRTION

+ EHPERIMENT

-I

-4

I

zilJ

LLJ

1-I

-1

I

1-i

-i

1

I

-<

400

I I I I I a I I I I I I I I I I I Ii I I T- I T I I I I I I I I I I I I i -. -"

. 1 - - I I Ii I L L I I I I I I I 1

nciI I I f I I

Ii.I

,1+~~~~~~

K~~~~~~~~~~~~~~~.

> N.

0 50 100I - III I I I t I I I I I I

200

TIME. MIN250 300 50=1C it

Fig. 3.8 Predicted and observed concentration profiles of toluene forSAPRC smog chamber experiment EC-237.

78

4-\f I I I

N+

SIMULATION

+ EHPERIMENT

.086

.084

.082

.080

.078

.076

.074

.072

.070

.088

. 06

.062

.060

.058

.056

.054

.052

.050 I I I II I I I I I I I

150 400

i I I I I I -FI I F I I T I 7 - I I I I . I I I I I -1

. . . .. .. I. . . I. . . * . . . . . . . L I

I I I II I

CIL

k=xg

cVLL

i

I

iiI

i

I

2\

,08

.07

,07

: .05

I

iL

iLi

.03

.02

.n0 5 50 t 150 200 50 300 350 400

TIME. MIN

Fig. 3.9 Predicted and observed concentration profiles of xylene forSAPRC smog chamber experiment EC-237.

79

I- -----I

ii

iI

ii

I

0 50 100 150 0Z0 250 300 350TIME, MIN

Fig. 3.10 Predicted and observed concentration profiles of HCHO forSAPRC smog chamber experiment EC-237.

80

s55

.50

.40

;Lel.

Z

h-

LU(.

.35

.30

.2S

.20

.15

.10

0400

0 50 100 150 200 250 300 350TIME. MIN

Fig. 3.11 Predicted and observed concentration profiles of aldehyde forSAPRC smog chamber experiment EC-237.

81

.32

28

26

24

22

xCI

Car

cc

z

u

.20

.18

.16

.14

.12

.10

.06

.02

0400

.22

.20

.18

.16

0x

.12k-

1 .10

.08

.06

.04

.02

O0 50 100 150 200 250 300 350 400

TIME, MIN

Fig. 3.12 Predicted and observed concentration profiles of PAN forSAPRC smog chamber experiment EC-237.

82

ly. The agreement between the simulation and the smog chamber results is ex-cellent. In general, the results for alkenes show good agreement with thesmog chamber data. Figs. 3.8 and 3.9 show the results for toluene and xylene,respectively, and the agreement is excellent. The agreement for the aromaticspecies is good for all experiments modeled. Formaldehyde and aldehyde areshown in Figs. 3.10 and 3.11 for run EC-237, respectively. There is overpro-duction of formaldehyde and aldehyde in the mechanism. This is expected,since the chamber is lined with Teflon, and formaldehyde and acetaldehyde areeasily absorbed by the coating. Fig. 3.12 shows the PAN produced. For theentire series of runs, the RADM mechanism has a tendency to overpredict thePAN levels relative to the smog chamber results. This overprediction is dueto the higher aldehyde concentration levels predicted by the RADM mechanism.

3.3.4 Comparison of the Chemical Mechanisms

The range of conditions shown in Table 3.4 was chosen for the intercom-parison of the three different mechanisms, the explicit mechanism, the carbonbond mechanism CBM-X, and the RADM chemical mechanism, version 1. These con-ditions were used to initialize the model for a single 24-h simulation whichwas started at sunrise (5:00 a.m., corresponding to the 300 min mark in thefigures) and ended at sunrise the next day (5:00 a.m., corresponding to 1,740min in the figures). The initial conditions for the diurnal simulations werechosen to be representative of pollution conditions ranging from very pollutedurban environments to the relatively clean conditions of remote regions of thenortheastern United States. Case A represents an urban case with a relativelylow hydrocarbon-to-nitrogen oxide ratio typical of early morning urban airmasses. The NO-N02-03 mixture is imbalanced in a manner which is typical offreshly mixed ozone-rich older air from aloft and NO-rich air from early morn-ing anthropogenic sources (Calvert and Stockwell, 1983a). Cases A through Hare analogous to those used by Calvert and Stockwell (1983a,b). Cases B, C,D, and E represent cases where the initial hydrocarbon-to-nitrogen oxide ratiowas varied. Calvert and Stockwell varied the hydrocarbon-to-nitrogen oxideratio around urban Case A. For the current studies, the ratio was variedaround cleaner Case F. No sources of NO were used in these tests. Thischoice of conditions was made to emphasize differences in the treatment ofradical-radical reactions by the mechanisms. Additional tests are planned,including cases with NOx and hydrocarbon sources and cases without back-ground ozone. The initial conditions for the explicit mechanism are given inTable 3.4. The initial conditions were picked for the explicit mechanism andthen lumped into the conditions for the carbon bond mechanism according torules provided by Whitten et al. (1984). The initial conditions for the sim-plified RADM mechanism were obtained by lumping the organic species accordingto HO reactivity. All hydrocarbons except methane and ethane were lumped intothe class HC, methane was placed into class CH4, and ethane was placed intothe class ETH. Ethene was placed into class OL2, propylene into class OL3,and butene into OL4. For aromatics, toluene was placed into the class TOL,and xylene was placed into XYL. Formaldehyde was placed in class HCHO, ace-taldehyde was placed into ALD, and acetone was ignored as nonreactive.

The rate constants for all mechanisms were updated according to the eval-uations of Kerr and Calvert (1984) for the diurnal variation tests. Most ofthe rate constants and activation energies differed by less than 10 percent

83

TABLE 3.4

INITIAL CONDITIONS FOR MECHANISM INTERCOMPARISONS

SPECIES CONCENTRATION PPB

CASE A CASE B CASE C CASE D CASE E CASE F CASE G CASE H

N02 25 0.25 0.025 2.5 2.5 2.5 0.25 0.025NO 75 0.75 0.075 7.5 7.5 7.5 0.75 0.07503 30 30 30 30 30 30 30 30CO 100 100 100 100 100 100 100 100S02 60 6 6 0.6 0.06 6 0.6 0.06METHANE 1,400 1,400 1,400 1,400 1,400 1,400 1,400 1,400ETHANE 100 10 10 1 0.1 10 1 0.1PROPANE 75 7.5 7.5 0.75 0.075 7.5 0.75 0.075BUTANE 100 10 10 1 0.1 10 1 0.1DIMETHYLBUTANE 10 1 1 0.1 0.01 1 0.1 0.01TOLUENE 20 2 2 0.2 0.02 2 0.2 0.02XYLENE 10 1 1 0.1 0.01 1 0.1 0.01ETHENE 70 7 7 0.7 0.07 7 0.7 0.07PROPYLENE 30 3 3 0.3 0.03 3 0.3 0.03BUTENE 10 1 1 0.1 0.01 1 0.1 0.01FORMALDEHYDE 20 2 2 0.2 0.02 2 0.2 0.02ACETALDEHYDE 10 1 1 0.1 0.01 1 0.1 0.01ACETONE 5 0.5 0.5 0.05 0.005 0.5 0.05 0.005

H20 SET TO 50% HUMIDITY FOR ALL CASES: 15,600 PPM

84

from those of Leone and Seinfeld (1984a). The rate constant for the H02-H02reaction was calculated from the formula given in Kerr and Calvert (1984)which gives a value of 4.45 x 103 ppm-1 min 1. The photolytic rate data usedfor the diurnal simulations for all three mechanisms are from Demerjian,Schere, and Peterson (1980), except for formaldehyde, acetaldehyde, and ace-tone. The photolytic rate constants for formaldehyde and acetaldehyde wereadjusted according to the evaluations of Calvert (1980) and for acetone ac-cording to Gardner, Wijayaratne, and Calvert (1984). The photolytic rate con-stants were chosen for a clear summer day (July 1) at 40° N latitude, near sealevel, and a temperature of 25°C. A surface albedo of 20 percent was assumed.The key test variables for this intercomparison were chosen for their impor-tance for the production of acids in both homogeneous gas phase and in liquidphase. These variables for the intercomparison of the mechanisms were H202production rates, S02 conversion rates, nitric acid production rates, and con-centrations of ozone, nitric oxide, nitrogen dioxide, organic peroxides, andHO and H02 radicals.

3.3.5 Discussion of Results

The NO behavior for the three mechanisms for Case F is compared in Fig.3.13. The RADM and the explicit mechanism give very similar concentrations asa function of time for NO, while the carbon bond mechanism gives somewhat highNO concentrations. The integrated NO concentrations over the 24-h simulationsfor Cases A through H are given in Table 3.5. The RADM and the explicit mech-anism give very similar integrated NO concentrations, while the carbon bondmechanism gives higher concentrations except for the cleanest air case.

The concentration of N02 as a function of time for Case F is shown inFig. 3.14. The RADM N02 concentrations are very similar to those of the ex-plicit mechanism until about noon (720 min) where the concentrations diverge.The integrated N02 concentrations for the three mechanisms are shown in Table3.6. The integrated N02 concentrations for the explicit and RADM mechanismsare lower than those for the carbon bond mechanism. This is due in part tonitrate formation in the explicit and RADM mechanisms. For Case A, near tenpercent of the initial nitrogen is converted to organic nitrate in the RADMand explicit mechanisms.

Integrated ozone levels are compared in Table 3.7, which shows that thethree mechanisms are in acceptable agreement. However, for the hydrocarbon-rich cases (Cases A, B, and F), the carbon bond mechanism and the explicitmechanism are usually in better agreement than the RADM mechanism. This isnot unexpected, since both of the complex mechanisms were developed for photo-chemical oxidant modeling.

The total production of H202 for one diurnal cycle is shown in Table 3.8.This table shows that there are large differences between the mechanisms forthe production of H202. The RADM mechanism predicts more H202 production forall of the cases except Case C and Case E. Case E has a very high NOx-to-hydrocarbon ratio and Case C has a very low NOx-to-hydrocarbon ratio. Thecarbon bond mechanism can produce 274 percent more H202 than the explicitmechanism, as in Case C which represents low initial NOx and high hydrocar-bons typical of some smoke plumes. On the other hand, the ratio can be as low

85

400 60I0 00 1000

TIME, MIN1 -200 1400 1600 1 ;00

Fig. 3.13 Comparison of NO concentrations predicted by RADMmechanism with explicit mechanism and carbon bondmechanism, version CBM-X.

86

0az

a_

3i

zQL

An-Aa UU01

.0032

*0030

.0028

*0026

.0024

.0022

*0020

.0018

.0016

.001i

.0012

.0010

.0008

.0006

.0004

.0002

n200

TABLE 3.5

COMPARISON OF INTEGRATED NO PRODUCED BY MECHANISMS OVER 24 HOUR PERIOD

INTEGRATED CONCENTRATIONPPM-MIN

RADM6.80E+002.53E-021.72E-031.35E+001.69E+004.88E-014.84E-027.10E-03

EXPLICIT8.30E+002.28E-021.37E-031.36E+001.63E+005.33E-015.00E-028.00E-03

CBM-X1.25E+014.50E-022.64E-031.64E+001.88E+008.60E-015.75E-026.66E-03

RATIO OF RADM & CBM-XTO EXPLICIT

RADM82%

111%126X99%

104X92X97X89%

MECHAN I SMCBM-X

151%197%193%121%115%161%115%83%

CASE ACASE BCASE CCASE DCASE ECASE FCASE GCASE H

I UIU

.009

.008

*007

-OC5

..00o

a '00

.003

.002

.001

20 0 600 30 1000 1 1 1600 l300 1;0 10 0 00TIMEp MIN

Fig. 3.14 Comparison of N02 concentrations predicted by RADMmechanism with explicit mechanism and carbon bondmechanism, version CBM-X.

88

n 4 el

i

I

I

Ii

TABLE 3.6

COMPARISON OF INTEGRATED NO2 PRODUCED BY MECHANISMS OVER 24 HOUR PERIOD

INTEGRATED CONCENTRATION RATIO OF RADM & CPM-X

RADM2.17E+012.29E-011.80E-022.86E+003.69E+002.20E+002.36E-013.31E-02

PPM-MINEXPLICIT1.93E+011.36E-011.22E-022.80E+003.49E+001.87E+002.00E-012.79E-02

CBM-X2.51E+012.40E-011.82E-023.54E+004.23E+002.53E+002.02E-012.23E-02

TO EXPLICITRADM112Z168%148X102%106%118%118X119%

MECHANISMCBM-X

130%176%149X126%121%135%101%80%

ao

CASE ACASE BCASE CCASE DCASE ECASE FCASE GCASE H

TABLE 3.7

COMPARISON OF INTEGRATED 03 PRODUCED BY MECHANISMS OVER 24 HOUR PERIOD

INTEGRATED CONCENTRATIONPPM-MIN

RADM4.35E+026.72E+013.84E+016.28E+014.47E+011.23E+025.40E+014.05E+01

EXPLICIT3.37E+025.02E+013.72E+015.93E+014.52E+011.03E+025.01E+013.95E+01

CBM-X3.49E+025.54E+013.58E+014.35E+013.53E+011.03E+024.86E+013.84E+01

RATIO OF RADM & CBM-XTO EXPLICIT

RADM129%134%103%106X

99%1 19%108%103X

MECHAN I SMCBM-X

104%110%96%73%78%

1OOX97X97%

C)

CASE ACASE BCASE CCASE DCASE ECASE FCASE GCASE H

TABLE 3.8

COMPARISON OF H202 PRODUCED BY MECHANISMS OVER 24 HOUR PERIOD

CONCENTRAT IONPPM

RATIO OF RADM & CBM-XTO EXPLICIT MECHANISM

RADM2.99E-024.68E-038.40E-047.08E-043.86E-057.63E-033.49E-032.46E-03

EXPLICIT1.73E-022.46E-031.12E-035.15E-047.61E-054.45E-031.90E-031.28E-03

CBM-X1.14E-024.65E-033.58E-035.86E-065.80E-072.38E-032.43E-032.78E-03

I.

CASE ACASE BCASE CCASE DCASE ECASE FCASE GCASE H

RADM173%190%75%137%

51%171%184X192%

CBM-X66%

189X320%

1x1I

53%128%217%

as one percent (Case D, which is a moderately polluted case with a relativelyhigh NOx to hydrocarbon ratio). These differences in the production of H202are due to differences in the simulated concentrations of H02 radical. Sincethe rate of production of H202 is proportional to the square of the hydroper-oxy radical concentration, small differences in the H02 concentration lead tomuch greater differences in the rate of H202 production. An example of thesedifferences in H02 concentration is illustrated in Fig. 3.15 for Case F. Thepeak values occur at the same time for the RADM mechanism and the explicitmechanism, while the peak values for the carbon bond mechanism occur 150 minlater. The integrated H02 concentrations for all of the cases are shown inTable 3.9. The differences are smaller than for H202. Unfortunately, thereare no reliable measurements of either H202 or H02 concentrations in smogchambers. Therefore, the accuracy of the mechanisms for the generation ofH202 cannot be evaluated at this time. Clearly more experimental researchwill be required to produce the necessary data for modelers.

The integrated HO concentrations are in good agreement for most of thecases modeled here, as shown in Table 3.10. This good agreement is reflectedin Table 3.11 which shows the total sulfate produced by the three mechanisms.For sulfate production, the explicit mechanism results are usually in betteragreement with the RADM mechanism than with the carbon bond mechanism.

The total nitric acid produced by the three mechanisms is in good agree-ment, as shown in Table 3.12. The carbon bond mechanism and the RADM mechan-ism both give greater rates of nitric acid production than the explicit mech-anism for most of the cases modeled here.

In summary, our preliminary study shows that the RADM mechanism givesreasonably good results when compared with smog chamber data and with the com-plex models. There is considerable variation in the predicted amounts of hy-drogen peroxide between all three mechanisms. This would be important whenone considers aqueous chemistry. More data from laboratory studies, smogchambers, and field observations will be required before chemical mechanismsfor regional acid deposition modeling can be thoroughly tested.

3.4 Aqueous Chemistry and Cloud Processes

Clouds and their associated precipitation have four effects on the long-range transport and transformation of a given ensemble of pollutants.

(1) Vertical redistribution: From a dynamical standpoint, clouds con-sist of a combination of updrafts and downdrafts which act as anefficient vertical mixer of contaminants. In addition, rainwaterwith its appreciable fall velocity can carry many pollutants down-ward, even though a significant fraction of rain may evaporate be-fore reaching the ground.

(2) Radiative effects: Due to a cloud's ability to absorb and scatterincoming solar radiation, gas phase photochemical reactions will bemodified in the vicinity of a cloud.

(3) Aqueous reactions: With their associated liquid and ice water

92

400 600 S00 1000

TIME. MINi_00 1400 1600 1300

Fig. 3.15 Comparison of H02 concentrations predicted by RAOMmechanism with explicit mechanism and carbon bondmechanism, version CBM-X.

93

· UUUuu

.00007

* 00006

z .00005

U0000.,..7

.. oooo

.00001

O L2C

II

IiI,

-~~~~~~~ I,

II 1

II I

'i ;'I I

- ~~~~~I1 1

U ,,, II, ,/V---- ----

/ I --I * ~ i

..t' I * \r ~ ~ J.~

10

care

TABLE 3.9

COMPARISON OF INTEGRATED H02 PRODUCED BY MECHANISMS OVER 24 HOUR PERIOD

INTEGRATED CONCENTRATIONPPM-M IN

RATIO OF RADAM & CBM-XTO EXPLICIT MECHANISM

RADM5.70E-022.37E-029.60E-036.70E-032. 11E-032.96E-022.30E-021 .66E-02

EXPLICIT3.30E-021.63E-021.09E-025.80E-032.91E-031.90E-021.44E-021.17E-02

CBM-X3.32E-022.37E-022.14E-029.01E-042.66E-041.54E-021.78E-021.97E-02

CASE ACASE BCASE CCASE DCASE ECASE FCASE GCASE H

RADM173%145X88%116%73X

156%160%142X

CBM-X101%145X196%

16%9%

81%124X168%

TABLE 3.10

COMPARISON OF INTEGRATED HO PRODUCED BY MECHANISMS OVER 24 HOUR PERIOD

INTEGRATED CONCENTRATION

RADM1.23E-045.96E-052.76E-053.10E-041.95E-042.37E-042.64E-041.86E-04

PPM-M INEXPLICIT9.70E-054.71E-052.40E-052.96E-042.12E-041.87E-042.37E-041.81E-04

CBM-X9.70E-056.21E-052.63E-051.67E-041.32E-041.87E-043.03E-042.60E-04

RATIO OF RADM & CBM-XTO EXPLICIT MECHANISM

RADM CBM-X127% 100%127X 132%115X 110%105X 56X92% 62X

127X 100X111% 128%103X 144X

Jn

CASE ACASE BCASE CCASE DCASE ECASE FCASE GCASE H

TABLE 3. 11

COMPARISON OF H2SO4 PRODUCED BY MECHANISMS OVER 24 HOUR PERIOD

CONCENTRATIONPPM

RATIO OF RADM & CBM-XTO EXPLICIT MECHANISM

RADM1.12E-025.51E-042.65E-042.34E-041.61E-051.90E-032.07E-041.54E-05

EXPLICIT9. 15E-034.40E-042.32E-042.27E-041.72E-051.55E-031.90E-041.50E-05

CBM-X1.48E-026.26E-043.17E-041.41E-041.14E-051.58E-032.31E-042. 04E-05

CASE ACASE BCASE CCASE DCASE ECASE FCASE GCASE H

RADM122%125%114%103%94%123%109X103X

CBM-X162%142%137X62%66%

102%122%136%

TABLE 3.12

COMPARISON OF HN03 PRODUCED BY MECHANISMS OVER 24 HOUR PERIOD

CONCENTRATIONPPM

RADM6.22E-022.78E-048.66E-069.32E-038.48E-036.63E-037.39E-047.43E-05

EXPLICIT5.13E-022. 10E-048.48E-069.31E-038.81E-035. 12E-036.71E-047.00E-05

CBM-X7.27E-023.51E-041.01 E-059.44E-039.21E-037.46E-037.97E-048. 47E-05

RATIO OF RADM & CBM-XTO EXPLICIT MECHANISM

RADM CBM-X121X 142%132X 167%102% 119%100% 101%96% 105X

129X 146%110% 119%106% 121%

CASE ACASE BCASE CCASE DCASE ECASE FCASE GCASE H

loads, clouds act as an aqueous reaction chamber in the atmosphere.Trace gases and particulates can be depleted from the air within acloud as they are incorporated into cloud and rain drops. Once in-side the drop, a myriad of chemical interactions can occur.

(4) Scavenging effects: Under the proper environmental conditions,clouds can generate large amounts of precipitants, which will carryany absorbed pollutant to the ground, and also scavenge gases anddust particles as they fall.

Walcek developed a one-dimensional cloud model which is used to predict meso-scale and microphysical aspects of the clouds above a given point in the mod-eling domain (effects (1) and (2)). This model is integrated with an aqueouschemistry submodule which predicts liquid phase concentrations and depositionrates of various pollutants along with SO2 oxidation rates within the cloud.The cloud and aqueous chemistry models represent preliminary routines withinthe cloud processes subroutine of the RADM transport/transformation model.The cloud processes subroutine is used whenever clouds or rain are predictedabove a given grid cell during model execution. Methods of modeling non-pre-cipitating cloud systems are currently under development. In the followingsubsections, the precipitating cloud model and aqueous chemistry subroutinesare described in more detail.

3.4.1 Precipitation Cloud Model

The model used to predict the vertical variation of cloud parameters is asemi-empirical model based on the observations of Paluch (1979) which suggestthat cumulus clouds are efficient vertical, rather than lateral, mixers of en-vironmental air. This type of cloud model represents a steady-state, one-di-mensional approximation which overcomes several problems associated with otherlaterally-entraining cloud models.

Modeling of cumulus clouds as vertically entraining cells has been sug-gested by measurements of air motions in the vicinity of growing cumuli. Tel-ford and Wagner (1974) measured inflow to the cloud top and base regions, andmild outflow surrounding the clouds at middle levels. More recent thermodyna-mic analyses by Paluch (1979) show that air within cumulus clouds consists ofmixtures of cloud top and cloud base air. Certain aspects of these observa-tions can be explained dynamically by penetrative downdraft theory. Thistheory predicts that dry environmental air entrained into the top of a growingcumulus cloud will evaporate cloud water, thus cooling discrete parcels of airnear the top of the cloud. These negatively bouyant parcels will then descendwithin the cloud, mixing with updraft air, until they reach a level of neutralbouyancy.

It is well recognized that horizontally averaged measurements of liquidwater content within clouds are significantly lower than one might expect ifparcels of air were to rise adiabatically from cloud base and retain theircondensed water load. This suggests that significant entrainment of dry envi-ronmental air into the cloud is occurring, which will dilute and evaporate theliquid water load. It is assumed that the source of air entrained into thecloud is the reservoir of air above cloud top. In an attempt to quantify the

98

degree of entrainment into a given horizontal level of the cloud, the obser-vations compiled by Warner (1970) for growing, non-precipitating clouds havebeen fit to the following empirical expression

Qcld = 0.6 exp { lcl } + 0.2 , (1)(Q1)ad 60.0

where Qcld is the horizontally averaged liquid water mixing ratio at a spe-cified pressure level in the cloud, (Ql)ad is the liquid water mixing ratiowhich would be observed were a parcel to rise adiabatically from cloud base, Pis the pressure (mb), and Plcl is the pressure (mb) at cloud base (liftingcondensation level). While there are significant variations in this ratioover both vertical and horizontal scales within a cloud, Eq. (1) represents areasonable average of numerous observations.

Since this model is a top-down entraining model, it is necessary to knowthe height of the cloud top and base before in-cloud parameters can be compu-ted. Cloud top and base are calculated from representative vertical profilesof temperature and dew point in the grid cell containing the cloud.

Cloud base is determined as the lifting condensation level computed fromthe highest saturated equivalent potential temperature below 700 mb. Oncethis cloud source level is determined, perturbations of 1 °K and 1 g/Kg areadded to the source level temperature and water mixing ratio, respectively.The lifting condensation level for this perturbed parcel is then computed.The temperature, height, and pressure of the cloud base can be determined asthe level where the ambient temperature of a parcel rising from the sourcelevel equals the dew point temperature.

Cloud top is computed by following a saturated adiabatic lapse rate fromcloud base until it reaches the temperature of the environment, in roughagreement with observed cloud top heights.

Once cloud top and base are determined, a vertical profile of adiabaticliquid water mixing ratio can be constructed as the difference between thesaturated mixing ratio at each level and the source level mixing ratio

(Q1)ad= Qsrc - Qsat(Tad 'P ) , (2)

where Qsrc is the liquid water mixing ratio of the cloud source level. Theactual liquid water mixing ratio within the cloud will be a fraction of thisvalue, determined by Eq. (1). This reduction in liquid water content is as-sumed to result from a mixing of cloud top air into the adiabatic cloud. Byconservation of total water mass and energy, an implicit relationship betweenthe various cloud properties of interest arises, which can be solved by itera-tive techniques. Table 3.13 shows an example of the output of the procedureoutlined above for a given temperature sounding. Using the environmentalsounding shown in the table, a 6 km deep cloud is predicted to occur. Thelatter columns of Table 3.13 show the vertical profiles of temperature, pres-sure, liquid water content, and entrainment fraction within the cloud. Theamount of ice at each level in the cloud is assumed to be proportional to the

99

Table 3.13

Cloud and Environmental Sounding

Environmental Air

Water VaporHeight Pressure Temperature Mixing Ratio(kin) (mb) (K) (g/kg)

.06

.641.281.952.663.414.235.126.107.238.51

10.0612.0214.6818.63

1000936871807743679614550486421357293229164100

303.2303.2303.2296.3286.3282.2277.2273.2270.2266.8266.2269.7273.0273.0273.0

12.013.017.014.511.05.01.01.01.01.01.01.01.01.01.0

Cloudy Air

Liquid WaterTemp. Mixing Ratio(K) (g/kg)

291.1287.8284.0279.6274.5268.2260.4

0.10.81.11.41.82.22.7

Water VaporMixing Ratio

(g/kg)

17.915.813.411.1

8.76.34.1

Liquid Water Fraction ofContent Entrained Air

(g/m3)

0.100.680.840.981.111.221.27

0.000.080.200.320.440.560.66

,-.C)C)

i

depression of temperature below freezing. At 0°C, no ice exists. At -18°C,all cloud water is assumed to be in the ice phase. At temperatures betweenthese limits, the water is partitioned between the ice and liquid phase.

The horizontal extent of cloud coverage can be computed by equating theamount of deposited rain in the 80 x 80 km area to the moisture excess (totalwater in cloud - total water in air) generated by the cloud during its life-time.

Pt

a [(Q1 + Qv)cloud- e dp R , (3)

lcl g

where a is the fraction of the grid square covered by clouds, Ql is the li-quid water mixing ratio, Qv is the saturated cloud vapor mixing ratio, andQe is the environmental vapor mixing ratio. Thus, the depth and verticalextent of a cloud can be specified from meteorological input provided by themesoscale meteorological model.

3.4.2 Aqueous Chemistry Model

An aqueous chemical model has been developed to predict the concentra-tions and deposition rates for dissolved aerosol and trace gas pollutants andS02 oxidation rates within cloud water. The model represents a simplifiedversion of the aqueous model of Jacob and Hoffmann (1983), and is similar toan earlier equilibrium model of Ohta et al. (1981). All reversible aqueousphase reactions are assumed to occur rapidly, and thus an equilibrium is es-tablished between the various dissolved ionic and non-ionic pollutant species.Those irreversible reactions involving the oxidation of sulfur (IV) in cloud-water are calculated using a forward time-differencing technique, with chemi-cal equilibrium being reestablished at the end of each oxidation time step.

The initial chemical composition of a cloud is assumed to be determinedby the composition of the soluble aerosol on which the cloud water nucleatesand the subsequent dissolution of trace gases from the air surrounding thedrops. Each of these areas will be outlined in more detail in the followingsubsections.

a. Nucleation Effects

A significant portion of the contaminant load within cloudwater is incor-porated into the drops at the time of their formation. Recent measurements offogwater made immediately after condensation showed relatively high concentra-tions 9f a number of ionic species including NH4+, S04 =, H+, NO3-, C-, Na+,F-, Ca + , and Mg +. These dissolved ions most likely represent the solubleportions of the nucleating aerosol on which the cloudwater condensed.

Upon dissolution, soluble portions of a condensation nucleus will ionizeinto equivalent concentrations of cation and anion components, yet only a fewof these ions will influence the pH of the resulting solution. The composi-tion of the nucleating aerosol is assumed to be characterized by the following

101

factors:

Sulfate: Assumed to be apportioned between sulfuric acid (H2S04) andammonium sulfate (NH4)2SO4. While the sulfuric acid portion will stronglyaffect pH levels in the cloudwater, the ammonium ion will also mildly affectpH to the extent that it combines with OH- to produce NH3 gas.

Carbonate: Soil dust assumed to be in the form of either magnesium orcalcium carbonate. Carbonate laden soil dust will neutralize a solution bydriving C02 gas out of solution.

Chloride: A generalized sea salt assumed to be composed of either sodiumor potassium chloride. In a fashion similar to carbonate, C1- combines withH+ to produce HC1 gas in very small amounts.

Each of these components will be specified by their atmospheric loadingin air (micrograms per cubic meter) at sea level and their corresponding sca-venging efficiency (percent of aerosol load incorporated into cloud water).Eventually, the atmospheric loading of carbonate or chloride aerosol will beparameterized in terms of land use type and other meteorological factors.Table 3.14 shows a list of soluble ions which are assumed to exist within thenucleating aerosol load. While other ions may dissolve upon nucleation, theirnet effect on the aqueous equilibria will be limited to their effect on theactivity of the solution, a relatively minor effect, and are thus not includedin this model.

b. Aqueous Equilibrium of Trace Gases

Numerous trace gases will form ionic or acidic species in solution.Equilibrium relations used to define the various ionic concentrations in cloudwater as related to ambient partial pressures of each gas are listed in Table3.15. Values for the equilibrium constants are taken from Jacob and Hoffmann(1983) and Chameides (1984). All equilibrium constants are adjusted for thetemperature of the cloud. Liquid phase concentrations are modified by theactivity of the solution according to Davies Equation.

In cloud or rain water, the following electroneutrality relation is es-tablished, equating the concentration of positive and negative ions

[H+] + [NH4+ ] + 2([Ca2 + ] + [Mg 2+]) + ([Na +] + [K+])

[C1-] + [OH-] + [HC03-] + 2([S04] + (4)

[C03-] + [S03 ]) + [NO3-] + [HS04-]

Each of these concentrations can be algebraically expressed as a function ofeither the initial aerosol loading, the equilibrium constraints imposed inTable 3.15, or the hydrogen ion concentration.

An additional constraint on this charge balance requires that any pollu-tant entering the cloud water be depleted from the gas phase, thus conservingmass. Final equilibrium will thus be established between a perturbed gas

102

Table 3.14

Aqueous Ions Resulting from Nucleating Aerosol

NaCl + Na+ + C1-

KC1 + K+ + C1-

Ca(C03) + Ca2+ + C032-

Mg(C03) + Mg2+ + C03 2 -

(NH4)2S04 + 2NH4+ + S04:

H2S04 + 2H+ + S04=

A3Fe + Fe3+ + 3A- *

B2Mn + Mn2 + 2B- *

*Anions associated with these salts are assumedthe overall charge balance without altering thelibrium. Thus, their exact form is neglected.

to only contribute toresulting ionic equi-

103

Table 3.15

Gas Phase Sources of Aqueous Species

Equilibrium

S02(g) + H20(1) <==> S02-H20(aq)

S02-H20(aq) <==> H+ + HS03-

HS03 <==> H+ + S03

IHNO3(g) <==> H+ + NO3-

NH3(9) + H20(1) <==> NH40H(aq)

NH40H(aq) <==> NH4+ + OH-

H20(1) <==> H ++ OH-

HC1(g) <==> HCl'HzO(aq)

HCl1H20(aq) <==> H+ + C1-

C02(g) + H20(1) <==> H2C0 3 (aq)

HzCO3(aq) <==> H+ + HC0 3 -

HC03- <==> H+ + C03 =

HCOOH(g) <==> HCOOH(aq)

HCOOH(aq) <==> H+ + HCOO-

Kh,

K1l

K2s

Khn

KhNt

K1NH

Kw =

KhHc

K1HHc

Khc

K1c

K2c

Kh0

K1o

Equilibrium Expression

= [S02-H20]/Ps0 2

= [H+][HS03-]Y+ Y-/[S02OH20]

= [H+][S03=3Y+Y2-/[HS03 -]Y-

= [H+][N3-]Y+Y-/PHN03

= [NH40H]/PNH3

= [OH-][NH4+]Y+Y-/[NH40H]

[H+][OH-]Y +Y-

= [HC1-H20]/PHC1

= [H+][C1-]Y+ y-/[H C1-H20]

= [H2C03]/Pc02

= [H+][HCO3-]Y +Y-/[H2C03]

= [H+][C0O3=]y+Y2-/[HC03-]Y-

= [HCOOH]/Prg

= [H+][HCOO-]y +Y-/[HCOOH(aq)]

Equilibrium Constant@ + 288.15K (M/ATM) or (M)

1.76 x 100

1.66 x 10-2

7.59 x 10-8

7.05 x 106

9.27 x 101

1.69 x 10- 5

4.52 x 10- 5

1.54 x 103

6.31 x103

4.11 x 10-2

5.86 x10-7

3.61 x 10-

7.12 x 103

1.71 x 10-4

4:

-

Table 3.15

Gas Phase Sources of Aqueous Species(continued)

Fnuil ibrium Eauilibrium ExoressionEquilibrium Constant

@ + 288.15K (M/ATM) or (M)

03(g) <==> 03(aq)

H202(g) ==> H202(aq)

HO(g) <==> HO(aq)

H02(g) <==> HO2(aq)

H02(aq) <==> H + 02-

CH3(CO)OOH <==> CH3(CO)OOH(aq)

CHjOOH(g) <==> CH300H(aq)

HS04- <==> SO4= + H+

Kh03 = [03(aq)]/P 0 3

KhH202 = [H202(aq)]/PH20 2

KhHo = [HO(aq)]/PHo

KhHo2 = [HO2(aq)]/PH02

KlHo2 = [H+][02-]Y+Y-/[HO (aq)]

Khpaa = [peracetic acid]/Ppaa

Khmhp = [CH300H]/Pmhp

KS6 = [SO4=][H+]Y2-Y+/[HSO4-]Y-

0210- 2

105

103

103

10- 5

102

102

10- 2

1.54

2.07

9.00

1.53

2.05

9.59

4.24

1.40

x

x

x

x

x

x

x

x

L- :IL I I LF II %A11 - I . . - . -.. --TT- --- .-.- - -- 1-- 1 -

phase concentration which can be calculated by assuming that the initial gasphase concentration of pollutant equals the final gas phase concentration plusany additional pollutant dissolved in the cloud water. For S02, the followingrelationship applies

P° pfS-02 = S02 + L [[S02*H20] + [HS03-] + [S03]], (5)

RT RT 10

where P°so 2 is the initial concentration of S02 in the cloudy air beforedissolution and Pfso2 is the perturbed concentration of S02, L is the li-quid water content of the cloud, R the ideal gas constant, and T the temper-ature of the cloud. Similar expressions for the other soluble gases used inthe model can be derived. When charge balance and mass conservation condi-tions are imposed, an implicit relationship for the final [H+] of the cloud-water results, which can be solved iteratively.

This aqueous chemical model is applied to the total volume of the cloudby assuming that the mixing ratio of each pollutant mixes in the same fashionthat liquid water and temperature mix at any level. The partial pressure ofpollutant at any level in the cloud will result from a mixture of cloud topand boundary layer pollutant mixing ratio, adjusted according to the ambientpressure at each level in the cloud.

Pp = [FptoP + (1 - F)base] p air (6)

where P 0 represents the partial pressure (atm) of a gaseous pollutant at agiven cloud level, Ptop is the volume mixing ratio of pollutant above cloudtop, and abase is the volume mixing ratio of pollutant in the boundary lay-er. Pair is the ambient pressure at each level within the cloud, and F re-presents the fraction of cloud top air mixed into the specified cloud level.This fraction of entrainment was determined in the previous section throughthermodynamic and mass conservation constraints.

In a similar manner, aerosol loading at each level in the cloud can bespecified as

base topp [ P (1 -F)+ F ] Par (7)

Plcl Pt

where ip is the loading of particulate aerosol (pg/m 3) at a given cloudlevel, ppbase is the aerosol loading at the cloud base pressure level,Plcl. and Pptop is the aerosol loading at the cloud top pressure level,Pt.

c. Aqueous Chemical Reactions

Aqueous chemical reactions of importance for the formation of acid raincenter around the oxidation of sulfur (IV) to sulfur (VI). The following oxi-

106

dation pathways have been included in the preliminary model. All oxidants areassumed to originate in the gas phase and are transferred into the cloud waterwhen the reactions listed below occur.

Sulfate production by hydrogen peroxide (Martin, 1983)

d[S(VI)] =8x104 exp{-3650 ( - 1)}[H202][S02'H20](8)

dt 0.1 + [H+]

by ozone (Maahs, 1983)

2560 exp(-966 )d[S(VI)] = [4.39x011 exp( 4131) + ][03][S(IV)]; (9)

dt T [H+]

by methyl-hydrogen peroxide (MHP) (Lind et al., 1984)

d[s(VI)] = 1.75x10 7 exp{-3801 ( - )}[H+][HS03-][MHP]; (10)dt

by peracetic acid (PAA) (Lind et al., 1984)

d [S(VI)] = 3.64x107exp{-3994 ( 1 - )}[HS03-][PAA]([H+]+1.65x10- 5 ); (11)dt

by HO

dES(VI)] = (9.5x109[HSO3-] + 5.5x10 9[S03:])[HO(aq)]; (12)dt

and by H02

d(S(VI)] = 106[HO2(aq)][[HS03-]+[S03]). (13)dt

Iron and manganese catalized sulfur oxidation were taken from Jacob and Hoff-man (1983). The rate equations are shown in Table 3.16.

Concentrations of sulfur species and [H+] used in the above oxidation ex-pressions were calculated using the equilibrium cloud model. [S(IV)] refersto the total concentration of dissolved S02 species in the cloud water (i.e.,[S02'H20] + [HS03-] + [S03=]). Liquid phase oxidant concentrations werecalculated using an adjusted Henry's law equilibrium between the gas phaseconcentration of each oxidant.

Pollutant concentrations, liquid water content, temperature, and pressureare averaged over the entire cloud volume. The aqueous chemical model thencomputes the concentration of dissolved pollutants in the cloudwater andmarches forward in time oxidizing S02 until the cloud is predicted to dissi-pate. Rain falling during the cloud lifetime is assumed to have a compositionequal to the cloudwater composition predicted by the aqueous chemical sub-

107

Table 3.16

Sulfate Production Rates Catalyzed by Fe3+ and Mn2+

d[S(VI)]-at

pH > 4

5000Mn2+][ HSO3-]

4.7[Mn2+1 2

[H+]+ lx1O 7 [Fe3+][S(IV)] 2

pH < 4

O.82[Fe3+[S( IV)]3 [5000[Mn2+][HS03-] + [H+iI]

4.7[Mn2+]2 (0.82[Fe+][SV)]1 + 1.7x103[Mn3+] 1 5

[H1 ] [H1 ] 6.3xl0-6 + [Fe3+1]

U,

0T--4

V

L--

C00O

Ln

0r-4

r--

1-4

L j

i -

i I

I

routine. Pollutants are continuously removed from the cloud by rainfall.

3.4.3 Integration and Implementation

The overall flow of the cloud processes routine is outlined in Fig. 3.16.Input from the meteorological model is used to construct a vertical profile ofvarious pollutants and meteorological factors required by the aqueous chemis-try subroutine. These variables are then averaged into a zero-dimensional(box model) aqueous chemical model which incorporates the effects of bothrainout and aqueous oxidation of sulfur. Any changes in pollutant concentra-tion within this aqueous box model due to either aqueous reactions or scaven-ging are then uniformly distributed throughout the vertical profiles of pollu-tant concentrations generated previously. At the end of the cloud lifetime (1hour), the in-cloud pollutant profiles are then appropriately averaged intothe clear air pollutant distribution at each vertical level. In this manner,polluted cloudy air is detrained to the cloud-free environment when the clouddissipates.

Fig. 3.17 shows an example of the type of interactions which are current-ly predicted to occur in a typical raining cumulus environment. Numbers onthis figure represent a percentage of the total sulfur load in the boundarylayer over which a 6 km deep cloud was predicted to occur.

3.5 Dry Deposition

Numerous estimates suggest that up to one half of total acid depositionoccurs in the absence of rainfall over the northeastern United States. Inaddition, boundary layer concentration of acid precursors can be modified dueto dry deposition. Thus, a dry deposition submodule has been developed whichcan quantify the fluxes of numerous pollutants to the ground under non-rainingconditions. In this model, it is assumed that the flux of any pollutant tothe ground is linearly proportional to the pollutant concentration, C, in thelowest layer of the model,

Flux = Vd * C

The constant of proportionality, Vd, has units of length/time and is thuscalled a deposition velocity. This deposition velocity is highly variable intime and space for a given pollutant, depending very strongly on numerous me-teorological and other environmental factors. In the following section, abrief derivation of this deposition velocity in terms of a resistance modelingframework is provided. Following this, details of individual pollutant spe-cies' deposition calculations are summarized.

3.5.1 Resistance Model of Deposition

Vertical transport of a trace gas species through the boundary layer to aground surface is most accurately described by a Fickian diffusion processwhere the flux of a pollutant downward, F, is proportional to the local gra-dient of the concentration

109

Inputs (80 km resolution):

Cloud proce

Outputs:

- Rainfall rate (mm/hr)- Vertical profiles of:

- Temperature (K)- Water vapor mixing ratio (g/g)- Pollutant mixing ratio (g/g)

:sses routine:- Compute cloud base, cloud top,

and cloud area coverage.- Compute cloud vertical

structure for:- Pollutants- Temperature- Liquid water

- Run aqueous chemistry model- Compute rainout amounts for

each pollutant- Compute Mixed, scavenged and

grid averaged pollutant profiles

- DC/DT for each pollutantDC = DCmix +

DCreaction +DCremove

- Wet deposition amountfor each pollutant

Fig. 3.16 Summary of Cloud Processes Routine

110

sec ondary Sabove BL

Finalpr{sp-y 80Babrove I.

FinalIeconde' Il SO in 9I

t in ",

16.5

sp)oi««lt A 1he| itd)

deposited

Example of the effect of an individual convective cloudon a distribution of pollutants. All processes shownare currently modeled in the cloud effects subroutine.Numbers represent percent of initial sulfur belowcloud base.

111

Fig. 3.17

F = -K aC (14)az

where K is the eddy or molecular diffusion coefficient for the trace gas inair, and C is the concentration of trace gas. For the purpose of calculatingdeposition rates, this surface layer is divided into two layers. In one lay-er, turbulent diffusion processes dominate the flux of pollutant downward,while very close to the surface a thin laminar layer exists where moleculardiffusion limits the flow of trace gas. This model of mass flow is shown inFig. 3.18.

Following an approach similar to Sheih et al. (1979), the dry depositionflux can be estimated. Eq. (14) can be integrated between the top and bottomof the turbulent layer. By making the following assumptions,

(a) the flux of pollutant is constant through the layer, and

(b) the eddy diffusion coefficient for the trace gas is the same as thatof heat diffusion through the turbulent layer,

the following equation of scalar transport through a turbulent boundary layerthen becomes

ku* (C - C6 ) ku* (C - C6 )F , (15)

In (Z) _ Th in () -h + in (Z)Z6 Zo Z6

where Yh is a stability correction function which is only a function of L,the Monin-Obukhov length scale, Z6 is the thickness of the laminar layer,Cs is the concentration at the top of the laminar layer, k is the von Karmanconstant (-0.4), and Zo is the roughness length. This equation has beenderived from well-known expressions for describing the flux of momentum onheat through a turbulent boundary layer.

The flux of pollutant through the laminar layer can be approximatedby thin film theory as

DF = (C, - C) , (16)

Z

where Dg is the molecular diffusion coefficient for the gas in air and Ciis the pollutant concentration at the interface.

The flux of pollutant across the interface itself is also limited by asurface resistance Rs,

C.F = - . (17)

112

z C

Z ' Turbulent Flux Layer /

Z6 / Ce

Laminar Flux Layer 6 , Cj

Surface

Fig. 3.18 Schematic of surface layers through whichpollutants traverse as they are dry deposited.

113

Algebraically eliminating Ca and Ci in Eqs. (15) through (17), andsolving for the flux, the following equation results

CF = . ... , (18)

ln(Z/Zo) - Th ln(Zo/Z a ) Z6[ .]. +[ + ]+R

ku* ku* Dg

(R a ) (Rb)

which can be expressed as

F = Vd * C, (19)

where Vd = deposition velocity = 1(R + Rb + R)

Ra = aerodynamic resistance, Rb = sublayer resistance, and Rs = surfaceresistance.

a. Calculation of Aerodynamic Resistance

The aerodynamic resistance is calculated using the stability and windspeed in the lowest atmospheric layer, together with the surface roughness ofthe land type at a particular grid point. The friction velocity, u*, andthe Monin-Obukhov length, L, are computed from this information by firstcomputing the bulk Richardson number,

gz A vRiB = (20)

vg

where g is the acceleration of gravity, z is the height of the lowest atmo-spheric layer, A v = ova - evg, 9va = potential virtual temperature ofthe lowest atmospheric layer, ovg = potential virtual temperature of theground, and u = wind speed.

Following Louis (1979), the friction velocity is computed for unstableconditions (RiB < O) as

9.4 R 1/2u ku [1- iB ] , (21)

in z (1 + 7.4 B)ln Zzo

and for stable conditions (RiB > O) as

u* k u [ 1 , (22)

In z (1 + 94 iB

114

where B = 9.4 / IRiBI z (k/ln(z/zo))2.ZO

The Monin-Obukhov length, L, is then calculated from u,

30 u*

L = rg- (23)kg H

H is the heat flux in the lowest atmospheric layer, also parameterizedby Louis (1979) for unstable conditions (RiB < O) as

u AO 2 9.4 RiBH = v [_( k ) [1 - i (24)

0.74 In z (1 + 5.3 B)zo

and for stable conditions (RIB > O) as

uAOev 2 2H v __ k ) [ i] (25)H = -I +

0.74 In z (1 + R )ZO 2 iB

Knowing the Monin-Obukhov length, the stability correction function, Th, isapproximated as

2h = exp (0.598 + 0.39(ln Z) - 0.09(ln Z) ) (26)

L L

for unstable conditions (RiB < 0), and

5zY = _ 5Z (27)h LL

for stable conditions (RiB > o).

Surface roughness over water is assumed to be proportional to thefriction velocity. Following the approximation utilized by the meteorologymodel, the water surface roughness can be represented as

20.032 u*

zo = + 0.0001 , (28)g

where g is the gravitational acceleration, and zo is expressed in meters.

115

b. Calculation of Sublayer Resistance

The sublayer resistance is indirectly estimated as a residual in heat/momentum transfer measurements, and is assumed to be similar for pollutanttransport. A popular parameterization is

R - 2 ( _)_2/3Rb = D ' (29)ku, Dg

where K is the thermal diffusivity of air.

c. Estimating Surface Resistance

Values of Rs are currently estimated from field measurements. Numerousstudies show that Rs is a function of photosynthetic activity, insolation,and the type of surface to which individual gases are depositing. Currently,we will be using tabulated values of this number for S02 and sulfate over anumber of land use categories and scaling the surface resistance for othergases to that of S02.

3.5.2 Land Use

Both surface roughness, zo, and surface resistance, Rs, depend on thetype of land over which a deposition calculation is to be performed. For thismodeling exercise, land has been subdivided into nine land use types, and thedistribution of each of these land types within an 80 x 80 km grid has beentabulated. The domain over which land use information is currently availableis smaller than the total transport modeling domain, although full coverage isexpected in the near future. Table 3.17 shows an example of the look-up tablefor surface resistance and roughness currently being used by the dry deposi-tion routine. Similar tables exist for each season. At each grid point, thedeposition velocity is calculated for each land use type, than appropriatelyaveraged into a mean deposition velocity according to the distribution of landin the grid square. An example of the land use distribution for agricultureand water over the northeastern United States is shown in Fig. 3.19, and simi-lar figures could be plotted for each of the nine land use categories.

3.5.3 Deposition Velocities for Other Trace Gases

Measurements of deposition velocities for trace gases other than S02 arerelatively rare, and thus surface resistances cannot be tabulated easily. Inthis submodule, the surface resistances for other trace gases are scaled tothe surface resistance of S02, depending on the gas' reactivity and solubilityrelative to S02. This method for calculating dry deposition of other gasesappears to be the most accurate alternative in the absence of a field studydatabase for other gases. The assumptions made in scaling surface resistancesfor other gases are outlined below.

The surface resistances for NO and N02 were assumed to be equal to eachother and were also assumed to be accurately represented by the S02 surfaceresistance. Since NO and N02 are not as soluble as S02 over water surfaces,

116

Table 3.17

S02 Surface Resistances and Surface Roughness - Mid Summer

Surface Resistance (s/m)Insolation (w/m )

200-400 0-200

SurfaceRoughness (cm)

0 01

URBAN

AGRICULTURE

RANGE

DECIDUOUSFOREST

CONIFEROUSFOREST

FOREST/SWAMPMIX

WATER

SWAMP

MIX AG/RANGE

1000

70

100

60

150

70

0

50100

2Dew formation.Water surface roughness

1000

120

140

130

240

140

0

60

140

1000

200

200

1000

500

500

300 1000

400 1000

300

0

75

200

1000

0

100

5.00

calculated from friction velocity.

117

Land Type

>400

0

0

0

0

0

0

0

0

0

100

25

5

100

100

100

*2

15

10

Fig. 3.19 Distribution of two of the nine landuse categories which are currentlyavailable for use in the model.

118

6.m

a surface resistance for NOx species of 500 s/m was used for NOx deposi-tion calculations over water.

Values of Rs for ozone were assumed to be 0.6 of the corresponding sur-face resistance for S02, except over wetted surfaces where a 2,000 s/m valuewas used.

For nitric acid vapor, HN03, the surface resistance was everywhere as-sumed to be zero, due to its miscibility.

Other gases under consideration for deposition calculations include per-oxide, aldehyde, formaldehyde, organic peroxides, formic acid, N205, and NO3 .These gases are being considered for deposition because of their importance insubsequent gas or aqueous phase chemical interactions which could indirectlyaffect either wet or dry acid deposition. Preliminary estimates of depositionvelocities for these gases will simply use scaled S02 deposition velocities.

3.5.4 Particulate Sulfate Deposition

For particulate sulfate, a simple empirical approximation derived byWesely and Shannon (1984) for the surface resistance was utilized. In thisapproach, the sublayer resistance, RB, has been combined with the surfaceresistance, Rs, and is assumed to equal the inverse of a set of observationsof deposition velocity for sulfate,

1V = 1

d R + R

(30)1R -

Vds

Vds is a scaled surface deposition velocity, which is a function of surfacelayer stability.

For stable conditions (L > 0),

Vds = u/500 . (31)

For unstable conditions (L < 0),

Vds - [1 + (300 . (32)500 -L

For mildly unstable conditions (PBL/L < -30), Vds is scaled with respectto the planetary boundary layer height, PBL,

119

2/3

Vds = 0.0009 u ) . (33)-L

Vds is never allowed to exceed the maximum observed sulfate depositionvelocity for a given land use type, typically 1 cm/s, although this upperlimit depends on land use type.

120

SECTION 4

SYSTEM INTEGRATION AND VALIDATION

4.1 Introduction

The main objectives of the system integration and validation group of theNCAR Acid Deposition Modeling Project are the development of the transport anddeposition model (incorporating all the available submodels), studying tech-niques for the analysis of source-receptor relationships for Eulerian models,developing concepts for overall model validation and sensitivity analysis, andthe preparation and analysis of suitable databases for model initiation andvalidation. Following an earlier study on suitable numerical integrationtechniques for the transport and transformation model, the method of Smolar-kiewicz (1983, 1984) was chosen for its ability to maintain positivity in so-lution variables and in conservation of tracers. Analysis of the CRAY com-puter memory availability led to the development of an in-core model as thefirst-generation model. This will allow quicker turnaround in computer out-puts and quicker implementation of the chemistry submodels under separate,parallel development. Several standard numerical tests were carried out toassure the accuracy and stability of the numerical scheme (see Section 4.2).At this time, both the first-generation gas phase and aqueous phase chemistrysubmodels have been integrated with the transport model. A cloud effects sub-model and a dry deposition submodel are also included. The structure of thisin-core model allows it to be expanded to a full-sized, disk-based model withreasonable effort. The first-generation model has 30 x 30 horizontal gridsand as many as ten vertical layers. Both dimensions can be expanded slightlywithout difficulty. Each horizontal grid covers a surface area of 80 x 80km , while the vertical extent of the model matches that of the mesoscalemeteorological model (i.e., up to a pressure level of 100 mb).

The OSCAR database appears to be the most complete and suitable firsttest for the RADM, both for meteorology and for chemistry and deposition. TheOSCAR IV meteorology has already been analyzed (see Section 4.3). The new re-sults on the precipitation chemistry of OSCAR IV now allow a direct simulationof the whole event. This should provide the first test for the RADM. Asource-receptor analysis for an Eulerian model has been developed. The ini-tial findings are rather promising. At the minimum, this technique can iden-tify the contributing sources for any given receptor site. Quantification ofthe relative contributions awaits further analysis and better understanding ofthe effects of nonlinear chemical interactions on carrier signal propagation(Section 4.4).

121

4.2 The Transport Deposition Model of the RADM

The NCAR/PSU mesoscale model, version 4 or MM4, uses the observed initialstate of the atmosphere and the observed side boundary conditions to forecastgrid-volume-averaged wind velocity, temperature, and water vapor mixing ratioson a grid of 61 points east-west and 46 points north-south. Fig. 2.10 showsthe domain. The standard horizontal resolution is 80 km, although a nestingoption allows 40 or 20 km resolution in a region of interest (for example,Ohio and the northeastern United States). For mesoscale meteorological re-search, the standard vertical resolution divides the atmosphere between thesurface and 100 mb into ten layers of equal mass. For the RADM, the standardMM4 forecast uses the high resolution planetary boundary layer (PBL) with atotal vertical grid of fifteen layers. The lowest layer is 10 mb or 80 mthick, giving a nominal height of 40 m. Originally, the lowest layer was tobe 2 mb or 16 m thick, but this has been too expensive computationally becauseof the parameterization of turbulence used by MM4 in the convective PBL. Thisparameterization and the thickness of the lowest layer are under review.

We assume that subgrid-scale effects will be much more important in theforecast of acid deposition than they are for meteorological forecasts. Cur-rent best MM4 forecasts are produced using a quite simple parameterization ofprecipitating cumulus (based on resolved moisture convergence) and stratiformprecipitation (based on the relative humidity). Nonprecipitating cumulus arenot parameterized because they do not affect synoptic storms strongly. Themuch more detailed subgrid-scale parameterizations in the RADM must be basedon the meteorological variables forecasted by MM4. These variables representspatial averages over a grid volume of 80 km x 80 km in the horizontal and80 m to several kilometers in the vertical.

Obviously, MM4 does not explicitly resolve even the largest cumulonimbusclouds. Even a stratus deck with a horizontal extent of more than 80 km maynot be resolved unless it is a grid layer (say, one kilometer thick). Thesubgrid-scale, turbulent updrafts and downdrafts, will always be much largerin magnitude than the relatively small resolved synoptic scale vertical motionprovided by MM4. The magnitude, frequency, and spatial extent of these sub-grid-scale updrafts in clouds will have to be parameterized before it becomespossible to calculate the effects of the subgrid-scale cloudiness.

The horizontal resolution of RADM will be the same as that of MM4. Nor-mally this means 80 km, but it could be 20 or 40 km in a nested region forhigh resolution studies or 160 or 320 km for low resolution work. For thein-core RADM, fifteen vertical layers would restrict the number of grid pointsin one or two horizontal directions severely. The present plan is to averageMM4 forecast levels together to reduce the vertical resolution to six to tenlayers. We will always try to have two or three layers in the PBL to resolvethe effect of stack heights. The out-of-core model will have the normal fif-teen levels of MM4, or any compatible number that the particular study callsfor.

The structure of the transport deposition model of the regional acid de-position modeling (RADM) system is designed to be as close as possible to thatof MM4. However, MM4 has only four to six predicted variables and is in core,

122

while ultimately the transport model may have up to 80 chemical species and beout of core. The transport model's data structure was planned for eventualout-of-core use. To facilitate research and early analysis, we are startingwith an in-core version with the same structure.

For a subdomain of the original MM4 forecast domain, the preprocessorprepares the meteorological input files for the transport model. The inputsto the preprocessor are the MM4 forecast files, the land use data (currentlythe NEROS data provided by Argonne National Laboratory (ANL)), the map scalefactors at each grid point, and the terrain heights. Using a module suppliedby James Sheih and Marvin Wesely of ANL and modified by Chris Walcek, the pre-processor currently calculates the dry deposition velocities for six species.(William Stockwell is compiling a list of the reactivity and solubility of allthe predicted species relative to S02 so that the deposition velocities ofother species for which there are few or no experimental observations of de-position velocities can be scaled relative to that of S02.) The data is anal-yzed and rearranged by the preprocessor and written at 1-h intervals.

The transport deposition model requires as inputs the preprocessor files,the emissions data, and the initial conditions for the transported chemicalspecies. The model linearly interpolates between hourly values of meteoro-logical variables, deposition velocities, and eddy diffusivities. The modelcalls modules that transport and diffuse the chemical pollutants, emit pollu-tants, predict chemical reactions, and predict the accumulation of acid onsurfaces by dry deposition and wet scavenging. At regular intervals, the con-centrations of the chemical species are stored in what are known as chemistryhistory files.

A postprocessor then plots and analyzes the results of the transport mod-el. Over the last several years, Joe Klemp of NCAR has developed an interac-tive analysis program which runs on a VAX computer and can analyze his cloudmodel history tapes. Bill Kuo and his coworkers adapted this program for usewith MM4 output, and since the grid structure of the transport model is simi-lar to that of MM4, it is relatively easy to analyze interactively our chemis-try history files on a VAX computer with this program. We have begun doingthis.

In the planning of the transport model, the emphasis was on flexibility.The different processes are handled with replaceable modules.

With all processes included except wet scavenging, and with 7,500 gridpoints, the in-core model has a ratio of fifty to one simulated time to CRAYCPU time. We do not believe that the inclusion of wet scavenging will signi-ficantly increase these computational requirements. Also, we believe that afactor of four increase in speed is possible so that it will be feasible touse several times as many grid points.

4.2.1 Grid Structure and Finite Difference Methods

The vertical coordinate is sigma=s=(p-pt)/(pt-ps), where p is thepressure, Pt the pressure of the top, and ps the pressure of the surface.The horizontal mapping is the (MM4) midlatitude standard Lambert-conformal

123

projection. The grid is staggered in both the vertical and horizontal. Timesplitting is employed based on the process. First we step forward the advec-tion, then the diffusion, and finally the chemical processes. Theoretically,symmetrization of the above process can achieve a truncation error of orderat , but due to difficulties in memory management we could not do this easilyin an out-of-core model. Therefore, our finite difference truncation errorremains of the order At at this time.

The advection finite difference scheme is iterated upstream (Smolarkie-wicz, 1983, 1984). This scheme was chosen because it maintains the conserva-tion and positiveness of the transported tracer concentrations quite well.The scheme is also relatively fast compared to other methods with similar con-servation properties, and it is local--that is, the advective time change at apoint is determined by the concentrations and velocities at a small number ofneighboring points--which, again, is necessary for the out-of-core model thatwe eventually will use.

In this scheme, one first takes an upstream step and then takes one orseveral correction steps which attempt to remove the large numerical diffusionpresent in the upstream method. We have chosen to use a single correctionstep because repeated correction steps begin to distort the solution and ne-cessitate a more complicated correction algorithm involving "cross terms."Each correction step necessitates keeping two more "slabs" of data in core,and we wish to minimize the storage requirements.

For lateral boundary conditions, we specify in-flow conditions and ex-trapolate at the outflow boundaries. Many tests reveal that there does notseem to be any problem with reflections at the boundaries.

For horizontal and vertical mixing, we are using gradient diffusion.Whenever possible, we will use the same eddy diffusivities employed in MM4.However, eddy diffusivities are only available when MM4 uses the "Blackadarhigh resolution PBL" option, and they are never used by MM4 in the unstablePBL. For the unstable PBL, we will use eddy diffusivities K = 0.1 w* Zi,where w* is the convective velocity scale and Zi is the inversion height.Currently, the finite difference method is centered in space.

No vertical turbulent flux or mean resolved vertical transport is allowedat the top. At the bottom, the vertical turbulent flux is found from a depo-sition velocity, and the mean resolved vertical transport is zero. The first-generation cloud scavenging process is akin to convective adjustment and re-moves pollutants to the surface instantaneously once per hour. No explicitboundary conditions are involved. At the lateral boundaries, the normal de-rivative of horizontal fluxes is zero. Currently, point or area sources aremixed instantaneously into a grid volume. In the future, we may follow indi-vidual plumes until they fill an entire grid volume.

We tested predicting diffusion and advection together in one step andusing iterated upstream for both. This produced good results when there wasgood resolution--say, more than twenty points--but there were errors as largeas 5 to 10 percent when there were less than ten points, so we have tentative-ly decided to handle the two processes separately.

124

For the standard MM4 simulations, there are ten levels in the vertical,equally spaced in sigma or pressure, giving a resolution near the ground onthe order of 900 m. The crudeness of this resolution presents no problem forthe diffusive stability criterion. However, the "high resolution PBL" simu-lations of MM4 have 80 m resolution near the surface. Thus, we may eventuallyhave to use an implicit finite difference scheme such as Crank-Nicholson toavoid the diffusive time step restriction near the surface.

In a regional scale model with of the order of 104 grid points and morethan twenty transported species, we prefer to only store one old time of eachspecies. Thus, although a "Gear code" is known to be an accurate and robustsolver for the chemical kinetics equations, we adopted a two-step iterativemethod of solution based on the work of Hesstvedt et al. -(1978) and Lamb(1984). Our solver only needs one old time level and is at least five timesfaster than a very good version of a Gear code and hundreds of times fasterthan a slower version of a Gear code.

The solution of the 21 prognostic equations for current versions of gasphase chemical mechanisms is based on the solution of

dc/dt = P - L c (1)

This equation can be solved analytially if the production P and the "loss"term L are constant.

The method of solution proceeds as follows:

(1) The eleven diagnostic equations are solved at the current time t.

(2) The 21 prognostic equations are stepped forward from t to t+dt usingthe exponential solutions to Eq. (1) with P and L evaluated at thecurrent time t.

(3) The diagnostic equations are solved at the new time t.

(4) The prognostic equations are again stepped forward from t to t+dtusing P averaged in time between t and t+dt and L evaluated at t+dt.

In addition, to reduce the stiffness of the equations, we combine somespecies to form three lumped species: total nitrogen, 03-NO, and N20s+ NO3.The lumped species are stepped forward with a simple Euler forward time step.Ideally, one should use the lumped variables to predict the largest or one ofthe largest species combined in the lumped variable. Thus, the total nitrogenequation is used to predict N02; 03-NO to predict 03; and N20s + N03 to pre-dict N205. However, at night or high in the atmosphere, when NO2 may become arelatively small component of total nitrogen, it may become necessary to makethe lumping a function of time of day and the relative levels of the species.More testing is needed here. In addition, we plan to add PAN in the near fu-ture, and it will be lumped in the total nitrogen.

125

Figs. 4.1 through 4.3 compare the results of a 24-h simulation (startingat 300 min) with a Gear code and with our method of solution for the gas phasechemistry submodel. The agreement is excellent during the day and good atnight. Sunrise occurs at 300 minutes local time and sunset at 1,140 minuteslocal time. The poorest agreement occurs with the least important pollutantsand when their concentrations are relatively low.

4.2.2 Tracer Tests with Artificial Flow Fields

In simple advection tests with no other processes, with a constant advec-tion velocity, and with an infinite number of grid points (but any grid reso-lution!), our finite difference advection scheme appears to conserve a tracerto within machine precision. However, there is numerical diffusion in thedirection of a coordinate axis if there is an advection velocity along thataxis. The tracer diffuses both upwind and downwind of the original structureof the tracer. This numerical diffusion increases as the blob of tracer isless and less well resolved.

The numerically predicted advection velocity may slightly lag the actualadvection velocity. For a well-resolved cone with radius r = 10 dx, the ave-rage numerical advection velocity for the first 100 steps is about 0.1 percentless than expected, but for a poorly-resolved instantaneous puff, the predict-ed velocity may be 1 to 2 percent smaller than expected.

The RADM code was tested with in-situ point sources, surface area sour-ces, surface deposition sinks, horizontal and vertical diffusion, horizontaland vertical advection, various in-flow boundary conditions, and with a plan-etary boundary layer that was constant or growing in time. These tests de-bugged the different modules of the code and proved that there were no greatproblems with reflections at the boundaries. We never found a grid pointwhere a concentration became negative. The development of plumes from pointor area sources was realistic. The numerical diffusion in the iterated up-stream scheme never seemed excessive.

4.2.3 A Three-Day Simulation Using MM4 Meteorology and All Components of RADMExcept Wet Scavenging

We performed a 3-D simulation with RADM using the MM4 forecast for April22-24, 1981 (part of the OSCAR experiment). The transport model was in-coreand used a subdomain of 25 points east-west, twenty north-south, and fifteenin the vertical, giving a total of about 7,500 grid points. There were 21transported species and eleven diagnostic species. The vertical resolutionwas about 80 m near surface and about 1 km near the tropopause.

This was our first long simulation with the RADM model, and not all ofthe parameters of the simulation were optimal for this case. Nevertheless,all of the capabilities of the model were exercised except the wet scaveng-ing. The RADM model used a reasonable amount of computer time and generatedrealistic values and smooth patterns for the chemical species. Some of thecharacteristics of the simulation are listed below.

126

a0

C

U1 a~~~~a

I a~

UC

0(A

200. 1000. 1 oo. 200. 1000. I oo.

Time (min.) Time (min.)

96AA IAAA -

0.012

(

u

C

0

I

0 000

iVWV. I 0. U. VV 100. 1 800.Time (min.) Time (mtn.)

Fig. 4.1 Comparison of 24-h simulations using Gear code(solid line) and the RADM solver (ts). Thesimulation starts at sunrise (300 min); sunsetis at 1,140 min.

127

0.060

s0.048

C

o

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E

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200. 1000 1800. 200. 1000. 1800.Time (min.) Time (min.)

0.70

EC0c

0

S 0.35

0

0

0 00

200. 000. 1o00. 200. 1000 100

Time (min.) Time (min)

Fig. 4.2 Same as Fig. 4.1.

128

CCL

a

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0.0450

i

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0.0225C

u

Ca

0.0000 I

0.10

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200. 1000. 1800....... T vvv. TmI (m.

7Time (iin.)

0. 1000

iEQ

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200. 1000 1B00 200 1000 1800

Time (mmn) Time (mm)

Fig. 4.3 Same as Fig. 4.1.

129

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(1) The eddy diffusivities were artifically specified and had po diurnalvariation; i.e., the vertical eddy diffusivity kz = 25 m /s in thePBL and 1 m /s aloft.

(2) The photochemical rate constants varied diurnally with sunrise at300 minutes local and sunset at 1,140 minutes local. Although thesetimes are not appropriate for April in Ohio, the model's ability tohandle diurnally varying rate constants is demonstrated.

(3) The rate constants depended on temperature but did not have theweaker dependence on pressure.

(4) There were 45 grid box sources which used preliminary emissionsinformation for SO2 and NOx. Emissions of ten other species werescaled as a fraction of NOx emissions based on Stockwell's Case D(Section 3.3). These data were old and somewhat lower than the 1983EPA data, and there were no background emissions of NOx by thesurface. All sources were at surface. We were not yet simulatingthe effects of tall stacks.

(5) The chemistry initial conditions were Stockwell's Case D, which ismoderately dirty with a relatively high NOx-hydrocarbon ratio.

Fig. 4.4 shows the surface emissions of S02 and NOx. The main sourcesof SO2 are the large power plants along the Ohio River, but the largestsources of NOx are the large cities.

At the end of 72 hours of simulation time, Fig. 4.5 shows the SO2 concen-tration at three heights in the atmosphere as well as the total dry deposi-tion. The deposition pattern resembles the pattern of the S02 concentrationat the lowest height (40 m). With increasing height, the pattern becomessmoother and there is less evidence of the sources. The maximum of 15 ppb at40 m decreases to 5 ppb in the upper PBL at 1,000 m. Above the PBL at 5,000m, the fluctuations in S02 concentration are relatively small. The slightmaximum of 0.4 ppb has moved downwind to New York. The isolines also mirrorthe wind pattern at that height.

Fig. 4.6 shows the final concentration of S04 in the lowest layer and thetotal dry deposition as well as the final H202 and 03 concentrations in thelowest layer. The low-level S04 concentration pattern is smoother than thatof SO2 and the maximum is downwind from that of SO2 because S04 is a product.03 and H202 have built up maxima over Pennsylvania.

4.2.4 Conclusions and Future Developments

As part of the Acid Deposition Modeling Project's development of a systemof models to predict acid deposition, we have developed and tested a prototypein-core transport model. The equations and coordinate system follow those ofthe NCAR/PSU mesoscale model as closely as possible. All parts of version 1of the in-core RADM are incorporated except wet scavenging. RADM performed a3-D simulation successfully. The whole modeling system can now proceed from

130

S02 Emissions( kg / ( hectare-yer ) )

NOX Emissions( kg / ( hectare-year ))

Fig. 4.4 Emissions of S02 and NOx from the ElectricPower Research Institute.

131

Total S02 Dry Depositionafter 3 days ( g / hectare )

Predicted S02 Concentrationat 40 m after 3 days (ppb x 10)

Predicted S02 Concentrationat 1000 m after 3 days (ppb x I00)

,¢r ... *.

Predicted S02 Concentrationat 5000 m after 3 days (ppb x 1000)

Fig. 4.5 Results of a three-day simulation of an April 22-24,1981 storm. The meteorology was forecast by MM4 andthe pollutant concentrations by the RADM. (See textfor details.)

132

Total S04 Dry Depositionafter 3 days ( g/hectare x 0 )

Predicted 03 Concentrationat 40 m after 3 days (ppb x 10 )

Predicted S04 Concentrationat 40 m after 3 days (ppb x 100)

Predicted H202 Concentrationat 40 m after 3 days (ppb x 100)

Fig. 4.6 Same as Fig. 4.5.

133

the observational data, through the MM4's forecasts, the preprocessor's re-arrangement and analysis, and the transport model's calculations, to the in-teractive postprocessing on the VAX.

Future developments include the following:

(1) Use this comprehensive model to study some basic scientific problems

(2) Add wet scavenging based on the work of Walcek and Jake Hales ofPacific Northwest Laboratory

(3) Improve boundary conditions of long-lived chemical species such asHN03

(4) Get eddy diffusivities from MM4, except unstable PBL in which K =1 w* zi

(5) Emit S02 aloft

(6) Make out-of-core version with more grid points

(7) Speed up chemistry solution, particularly of the diagnostic equa-tions

(8) Get better land use and emissions data from EPA

(9) Add nonprecipitating cumulus parameterization being developed byFred Vukovich and his coworkers at Research Triangle Institute

(10) Develop an engineering model to reproduce certain features of thiscomprehensive model's forecasts

4.3 Data Analysis

The NCAR Acid Deposition Modeling Project must make the best use ofavailable field data to evaluate and further develop the RADM. An in-depthunderstanding of the existing database not only provides information on how toestablish model evaluation criteria, but also furnishes guidelines for futurefield observation programs. We first summarize our current understanding ofRADM data needs. We then describe our ongoing effort in analyzing the OSCARdatabase. We expect these data to be used in the preliminary evaluation testsof RADM and in developing objective analysis procedures for regional chemistryprecipitation data.

4.3.1 RADM Data Needs

Observational data are required for (1) the selection of episodes for thestatistical database of RADM and the development of appropriate weighting fac-tors to determine seasonal and annual average deposition from this database ofepisodic events, (2) the initiation and execution of RADM for individual epi-sodes, and (3) the evaluation of RADM performance. Meteorological databaserequirements were discussed in Section 2 of this report. We shall limit the

134

discussion here to the chemical databases.

a. Episode Selection and Averaging

Since RADM is an episodic model, it will not be feasible to actually sim-ulate the emission, transport, and transformation of acidic materials on adaily basis for a complete year. The work plan for the RADM calls for a sta-tistical analysis of representative episodic events. From this set of repre-sentative individual events, we then construct the annual and seasonal depo-sition averages. A total of twelve synoptic cases has been selected to repre-sent the major synoptic events that are believed to best represent the annualvariations in the eastern United States (see Section 2.3.4). The precipita-tion data are being analyzed by Perry Samson of the University of Michigan whois exploring the correspondence of deposition and synoptic events. At thistime, it is not clear how much chemistry precipitation data may be available.In the event that only limited chemistry data becomes available, we will useRADM to estimate the corresponding chemistry databases for each of the synop-tic categories from which we will develop a set of weighting factors. As willbe discussed, we will be using the precipitation database analyzed by Samsonto test the statistical procedure on a regional scale.

b. RADM Initiation and Execution

Execution of the RADM for each episode requires data on meteorologicalconditions, emissions, land use, and initial and boundary conditions for chem-ical species concentrations. The meteorological variables (including boundaryconditions) for all of the different episodes are provided by the Penn State-NCAR mesoscale meteorological model (MM4). Representative diurnal variationsof emissions of major chemicals on a seasonal average basis are adequate. Itis assumed that, for the development of annual and seasonal average deposi-tion, the emissions data for a particular year do not have to be specific forepisodes, provided that emission anomalies were not occurring during the epi-sodes. Land use data with the appropriate spatial resolution over the entirespatial domain are required. Again, it is assumed that land use did not varysignificantly from year to year during that 1979-1983 period from which theepisodes were selected.

Vertical profile data throughout the model domain for the major chemicalsare required for determining initial and boundary values for each episode.This requirement possibly can be relaxed by initializing the model during awell-mixed midday period. In these cases, surface data, together with climat-ological data above the mixed layer, may be adequate. Where surface data aresparse (i.e., in many rural areas), estimates of concentrations will need tobe made based on available information. In addition, while data for key chem-icals (S02, NOx, 03, total VOC, and sulfate aerosol) are essential, initialvalues for other chemicals could be obtained from equilibrium concentrationsobtained through box model simulations with fixed values of the known chemicalconcentrations.

c. RADM Evaluation

Data are required for three types of model evaluation: (1) data for

135

evaluating the performance of RADM components, (2) data for testing RADM per-formance on episodes, and (3) data for checking RADM's ability to predict ave-rage (e.g., seasonal and annual) deposition. Evaluations of the meteorolo-gical modeling component and the gas phase chemical mechanism component areunderway using available meteorological and smog chamber data, respectively(see Sections 2 and 3). Evaluation of other components which are less under-stood, such as the liquid phase chemical mechanism submodel and the depositionmechanism submodel, will require the use of appropriate laboratory and fielddata whenever possible. This is yet to be formulated.

To evaluate RADM performance on individual episodes, detailed meteorolo-gical and chemical measurements taken during the course of an episode over theentire domain of the episode are necessary. Evaluation of RADM for precipita-tion cases already is possible using the OSCAR data set. Evaluation of mostlydry deposition cases may be possible by using the NEROS database. Comparisonsof observations and RADM will be carried out by examining the spatial and tem-poral characteristics of the deposition and/or chemical concentrations, not bycomparing values on a point-by-point basis. Alternative evaluation strate-gies, which do not require the use of comprehensive databases, are also beinginvestigated.

RADM performance with respect to long-term averages requires wet depo-sition and air quality monitoring data for major chemicals for at least oneyear, at as many representative sites throughout the modeling domain as pos-sible. The air quality data are particularly useful for checking model per-formance for average dry deposition since dry deposition monitoring data arevery sparse. As with the episode evaluations, comparisons of annual and sea-sonal average characteristics of the deposition fields, not point-by-point orgrid-by-grid comparisons, are planned. The model average deposition fieldswill be determined from the episodes and weight-averaged using synoptic anddeposition frequency information. More definite plans will be developed bymid-1985.

At this time, only the OSCAR and NEROS field studies, which were conduct-ed in the northeastern United States, are available for evaluation of the com-prehensive RADM system. It is recognized that, while the emissions inventor-ies for man-made sources are expected to be adequate, air quality and deposi-tion data are sparse for rural areas. Table 4.1 summarizes the present sta-tus of the databases and our expectations in the near future. We expect afirst use of the RADM will be the identification of major characteristics ofpotential field programs for 1986-1988.

4.3.2 Analysis of OSCAR Data

Paulette Middleton has examined the fourth episode of OSCAR to determinethe characteristics of a wet deposition episode revealed in the precipitationdata and to determine appropriate comparison precedures for RADM and field ob-servations. She is continuing the study with the other episodes of OSCAR.

The characteristics of OSCAR precipitation are investigated first on aqualitative basis by examining hourly average contours of the rainfall volumeand precipitation chemistry data, and then on a statistical basis by examining

136

TABLE 4.1. CHEMICAL DATA REQUIREMENTS.DATA AVAILABLE ( ); DATA NOT YET AVAILABLE (--); DATA NOT NEEDED ( ).

Gases

S02NO2NOTotal VOCAlkanes(not methane)MethaneAlkenes(not propyleneor ethylene)

PropyleneEthyleneAromaticsAldehydes(not formal dehyde)Formal dehydeOrganic Acids(not formic)Formic Acid

CONH3HC1HFH2SDMS03PANHN03H2 02OHHO2R02

EMISSIONS....... ~~~~~~~~~~~~~~~

I

INITIALAND BOUNDARYCONDITIONS

i

II

I

1i

I

I

I

I

I

I

II

EVALUATION

TI

I

iII)

(

)

I)I

I

137

r

I

I

II

TABLE 4.1. CHEMICAL DATA REQUIREMENTS(CONTINUED)

Aerosols

Sul fateTotal MassNitrateGraphitic CarbonOrganic CarbonAcidityAlkaline DustSea SaltIronManganese

Precipitationand Clouds

Total Water ContentNitric AcidSulfuric AcidOrganic AcidsCarbonH+

Na+

Ca++K+

Mg++Fe+++Mn++S04 =N03-NO3 -NH4 +C1-F-

EMISSIONS

III

II!

INITIALAND BOUNDARYCONDITIONS

i

I

II

I

IIII

II I~~~~~~~~~~~~~~

EVALUATION

I

II

I

IiI

II

II

I

138

the correlations among rainfall volume and the chemical components of therain.

The OSCAR program was designed to study the chemical and dynamical fea-tures of selected cyclonic, or frontal, storm systems as they traversed theeastern portion of the North American continent. During the April 1981 studyperiod, four storm systems were examined. The meteorological descriptionshave been presented elsewhere (OSCAR, 1983; Kuo et al., 1985).

The precipitation data collected at the OSCAR intermediate network sta-tions during the April 22-24 episode are used. The locations of the stations,the collection times for each station, and the precipitation informationavailable for the stations are summarized in Fig. 4.7.

The precipitation sampling was done on a volume average basis. The goalwas to collect 100 ml (corresponding to 0.6 mm) of rain for each sample. Dur-ing periods of very heavy or very light rainfall, volumes typically were muchlarger or smaller than 100 ml, respectively. As illustrated in Fig. 4.8, vol-ume average sampling results in considerable variation in the temporal pat-terns of precipitation from station to station.

In order to facilitate contour and statistical analysis across all of theOSCAR domain, the data were temporally averaged. The averages for each vari-able were obtained by summing all of the contributions to the averaging per-iod. Each contribution was multiplied by the rainfall volume associated withthat contribution. The total was divided by the entire rainfall volume asso-ciated with the averaging period. If the collection time for a sample extend-ed over two or more different time periods, it was assumed that the amountcollected during each period was proportional to the collection time whichoverlapped each period. In summary, the concentration of chemical component Cfor time period i is given as

I Aij Rij a. .

C. = J (2)E R.. a..1j 1J

where Aj is the contribution j for chemical A to Ci in period i. Rijis the rainfall volume associated with Ai-. The factor aij is the frac-tion of the total sample collection time which is counted in period i. Inmost cases, individual samples contributed to only one averaging period,making aij = 1. The rainfall volume Vi for an averaging period i issimply

V = E. R.j c.j . (3)

For this study, an averaging period of one hour was chosen. Hourlyaverages provide a good compromise between detailed temporal resolution andefficiency of presentation. The effect of averaging on the results of thecontour and statistical analysis was checked by comparing hourly average and30 minute average results. Contour plots and statistics using data averaged

139

Fig. 4.7 Summary of OSCAR 4 precipitation data collection.Stations (I) are identified by OSCAR Code number. Stationsto the left of the north-south line (----) recorded rainfallmainly in the first half of the storm before April 23, 12-13 GMT.Stations to the right recorded rainfall mainly in the second half.Stations enclosed with a o had rainfall for less than 8 hours.Stations enclosed with a A had large amounts of missing chemical data.

140

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141

STATION 10

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I II III I II II III 11 1 IIIIIIh.II Ig I 11 1111I II II I

IA

over the shorter time period were consistent with the hourly averaged dataplots and statistics.

A considerable amount of effort went into insuring high data quality byCarmen Benkovitz at Brookhaven National Laboratory (BNL) and Terry Dana atPacific Northwest Laboratory (PNL). One additional check was made in thesestudies. This involved comparing pH measured immediately in the field and thecorresponding samples measured later in the laboratory. While some small dif-ferences were expected due to chemical changes in the samples, differences inthe hourly averages which were greater than a pH unit were considered to be anindication that the field or laboratory measurements were in error. As notedin Fig. 4.9, only four samples were screened out because of suspicious pH com-pari sons.

Contour plots were used for examining hourly variations in precipitationchemistry throughout the event. CONRAN (NCAR, 1983c), an NCAR package de-signed for highly unequally spaced data, was used to generate all of theplots.

This study concentrated on examining the sulfate, nitrate, ammonium, pH,and rainfall volume since data for the other chemicals were not as complete.If a station registered bad or missing data for any one of these five vari-ables for a particular hour, that station was omitted for that hour.

To insure that the contours did not include regions that had no data orbad data, only local contours in the neighborhood of valid data were studied.The boundaries of these shielded areas were placed no more than 50 km from thenearest station with valid data. The 50 km cutoff was chosen because manyprevious studies of precipitation show that the correlation between rainfallat different stations decreases greatly beyond 50 km (e.g., Granat, 1974).These studies suggest that there is no physical basis for extending contoursbeyond 50 km of existing rainfall measurements.

Statistical analysis provides a means for summarizing the relationshipsamong chemicals and rainfall volume during the precipitation event. The Sta-tistical Analysis System (SAS, 1982) was used extensively in this analysis.We focused upon correlations among variables within and across stations andprincipal components analysis for the entire episode.

Correlations of pairs of variables (Snedecar and Cochran, 1980) show howclosely two quantities change together during the event. Principal componentsanalysis (Morrison, 1976) determines the extent to which several variables arerelated to each other. If the first principal component explains a large part(e.g., more than 70 percent) of the variability in a data set, then the firstprincipal component can be used as a convenient single index describing thehour-by-hour change in a group of variables. As with the contour analysis,the statistical investigation focused on sulfate, nitrate, ammonium, pH, andrainfall volume.

The changing character of precipitation throughout the event is illustra-ted in Figs. 4.10 and 4.11. The shielded regions in which the contour programwas operative are enclosed in boxes. At all time periods, the patterns of

142

m

in 5-J

UL.

3

3 5 7

LAB pH

Fig. 4.9 Field pH .vs. lab pH for OSCAR 4. The code A refers to1 value, B refers to 2 values,per point, etc. The circled lettersrepresent data that was omitted from analyses because of significantdifferences in field and lab pH. In increasing lab pH order, thesedata points correspond to Station 80, April 23, 3-4; and Station 32,April 23, 15-16, 16-17, and 17-18.

143

,. PR ItP.-"23, 1981 4- 5 GMT RRINFRLL V0L (MM);-.** ssX BAD BR HISSING DFITr

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Fig. 4.10 a-e Contour plots for April 23, 4-5 GMT.Rainfall volume (mm), hydrogen, sulfate, nitrate,and ammonium ions (micromoles/liter).Contour plots are operative only in enclosed areas.

144

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146

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148

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Fig. 4.11 a-e Contour plots for April 23, 19-20 GMT.Rainfall volume (mm), hydrogen, sulfate, nitrate,and ammonium ions (micromoles/liter).Contour plots are operative only in enclosed areas.

149

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152

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chemical concentrations appear to be more closely related to each other thanto the patterns of rainfall volume.

The lack of a strong relationship between rainfall volume and chemicalcomponents of rainfall is also reflected in the statistical analysis. Anal-ysis shows that sulfate, nitrate, ammonium, and acidity are strongly interre-lated. Rainfall volume is not strongly associated with the concentration ofany of the rainfall chemical constituents (Fig. 4.12). The rainfall volume isplotted against the first principal component of an analysis of sulfate, ni-trate, ammonium, and hydrogen ion derived from laboratory pH. In this analy-sis, the first principal component described over 70 percent of the variabil-ity among the four variables. Except for a few high rainfall cases, there isvery little direct correlation between rainfall volume and the precipitationchemical concentrations.

This first phase of the analysis of OSCAR precipitation chemistry dataprovides insight into the characteristics of a precipitation event. Addition-al studies of this OSCAR event, as well as the other two regionally monitoredOSCAR precipitation events, are planned in order to develop a set of appropri-ate criteria for a first evaluation of RADM using the OSCAR database.

4.4 A Study on the Source-Receptor Relationship in an Eulerian RADM

It is commonly believed that direct analysis of the source-receptor re-lationship in Eulerian modeling involves expensive parametric analysis forlinear systems and is probably not possible for nonlinear systems (Johnson,1983). Hsiao-ming Hsu and Julius Chang have studied a new technique for car-rying out intrinsic source-receptor analysis within existing frameworks of Eu-lerian models. The basic concept is the superposition of a small oscillatory(in time) perturbation on each constant source function in the model. It isconjectured that this small oscillatory signal will not affect the actual phy-sical interactions in the model but will be carried downwind. If so, then atany given receptor site the time-dependent solution of any variable of inter-est can be Fourier-analyzed to recover the power spectrum of all the imposedcarrier signals along with relative amplitudes. Physically, this small super-imposed signal should be within the natural fluctuations of the source func-tions. For the major sources of atmospheric acid material, these fluctuationsare at least of the order of a few percent. There, signals of less than onepercent source can be safely utilized without affecting the simulation result.

A set of test problems was studied using both single and systems of lin-ear and nonlinear advection-diffusion equations in two dimensions. It wasfound that clear signals could indeed be found at all the receptor sites.However, the relative amplitudes of individual signals depends on severalparameters. For linear problems, this can be clearly sorted out, but for non-linear problems, it is much more complicated. The major influencing factorshave been identified, but procedures for quantifying the relative contribu-tions of different sources to a particular receptor site still require morestudy.

This concept can be demonstrated with a simple one-dimensional advection-diffusion equation with emission sources

154

. - -- *" ~ ------- "^~. 110

w

LL

-I

·-

.. · ^ ^

~L

0 -.· i#A a & &" GM A " A A

· -Mi- _M · "t ,· "

-2.6 0.0 3.0 5.4

FIRST PRINCIPAL COMPONENT

Fig. 4.12 Rainfall volume .vs. the first principal component foran analysis of sulfate, nitrate, ammonium, and hygrogen ions forOSCAR 4. The code A refers to 1 value, B refers to 2 values perpoint, etc. Zero concentration corresponds to -2.6 in the principalcomponent field.

155

a 2ac,+U^ = + iL [E. + A1 sin (2rN. t/T)] S(x-xi), (4)at ax ax i

where u is a fixed advection velocity, v is the diffusion coefficient, Eiare the constant source functions localized at xi, and Ai are the ampli-tudes of the small sinusoidal signal with period 2'Ni . Such an equation canbe solved with an outgoing flux boundary condition. Numerical solutions ob-tained with the same solution technique as the transport and deposition modelof the RADM agreed with the analytical solution to expected numerical accuracy(Fig. 4.13). The same is true in two dimensions.

Fig. 4.14 shows the detailed locations of both the sources and receptorsin a set of two-dimensional problems with linear or nonlinear chemistry, withstrong or weak diffusion and with different relative source strengths. Thesources are located at points 1, 2, and 3. The receptors are at points A, B,C, D, and E, both on and off the axis of horizontal flow field. The physicaldomain represents a 3,200 x 3,200 km region. The flow is in the positive x-direction at a speed of 20 m/s. From the one-dimensional analysis, it wasfound that the total time period used for analysis must be the least commonmultiple of all the frequencies used. This assures the orthogonality of indi-vidual signals to obtain a clear power spectrum. For the nonlinear probleminvolving chemical interaction terms, the frequencies must be sufficientlyclose yet distinct so that beat frequencies can be clearly separated from theprimary signals. It is also interesting to note that, if several point sour-ces carry the same signal, a modulation factor of cosine function with phaselag of the form (x-xi)/u (1-D case) occurs which smears the individual con-tributions. Therefore, it is necessary to have distinct carrier frequenciesfor each source grid. This, along with the above orthogonality requirement,then limits the number of sources that can be considered at one time, in apractical sense, to at most a dozen. With diffusion, there is a frequency-dependent damping in the signal amplitude. As Fig. 4.15 shows (for a 2-Dproblem without chemistry), the damping factor increases with frequency.Therefore, it is preferable to use low-frequency carrier signals, although re-sults such as those shown in Fig. 4.15 can be used to estimate the correctionfactors for any given distance from a source and for each frequency.

For a two-component system with linear chemistry of the form

Dcl = - kic + k2c2 + vv2cl + S , and (5)Dt

Dc2 = klcl - k2c2 + vv2c2 , (6)Dt

where ki and k2 are linear reaction coefficients and S is the source term inthe form of the previous 1-D problem. Fig. 4.16 illustrates the essentialfeatures of this system. If ki and k2 are equal, the 1-D analytical solutionshows an amplitude damping factor that is a function of the relative distancebetween any given pair of source and receptor. This is clearly seen in Fig.4.16. As one moves away from points 1, 2, and 3, the differences among thecarrier signal amplitudes reduce, but the sum of ci and c2 remains constant

156

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Fig. 4.13 Fourier analyzed signal at receptor sites for an idealizedlinear horizontal flow problem without diffusion.

157

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Positions of the source points (1, 2, 3) and receptorsites (A, B, C, D, E) of a 2-D test problem for Euleriansource-receptor analysis study.

158

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Fig. 4.15 Frequency dependence of amplitude damping insinusoidal signal propagation due to diffusionprocesses.

159

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Fourier analyzed signal amplitudes at receptor sites due to three sourceswith horizontal advection and 2-D diffusion.

wao

wa.d

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we

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Fig. 4.16

[ - f.. W.111,

II a a a a a a a 4a

--- IC.11

throughout since the sum of the above equations has no chemical loss term.Diffusion carries a very small amount of the chemical species to the off-axisreceptor sites, B and C. Similar results are found for a three-equation sys-tem with nonlinear chemical interactions for which there is no analytical so-lution. Current results can only be used as a guide for the more realisticcases. Nevertheless, Fig. 4.17 shows that, at the minimum, it is possible toobtain clear signals at all the receptor sites. Quantitative interpretationis much more difficult, however, and will require more studies with more real-istic chemical systems.

161

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- i

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Fig. 4.17 Fourier analyzed signal amplitudes at receptor sites due to three sourceswith horizontal advection and 2-D diffusion for a three-component nonlinearchemistry system.

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SECTION 5

ENGINEERING MODEL

5.1 Introduction

The development of an Eulerian Regional Acid Deposition Model (RADM) isan important component of the National Acid Precipitation Assessment Program(NAPAP). RADM is to incorporate the current and evolving state of knowledgeof atmospheric processes on a regional scale as related to acid deposition.This comprehensive description of all the major physical and chemical proces-ses makes RADM the most appropriate analysis tool for deriving the basic un-derstandings. For policy and assessment studies, this would require RADM tobe streamlined for computational efficiency and with suitable simplificationsin the representation of physical and chemical details. The current NAPAPschedule for RADM development does not allow for this to occur prior to 1988/1989. It is the general consensus of the scientific community that a rigoroustreatment of the relevant processes is of the highest priority at this time.Clearly actions must be taken to address the near-term needs of the policy andassessment components of NAPAP, especially in the time frame of the 1987 as-sessment report. A concept of an "engineering" model was proposed (Chang,1984) to bridge the gap between the already approved schedule for comprehen-sive RADM development and the policy and assessment needs of NAPAP in late1986 and beyond.

A Workshop on Acid Deposition Policy and Assessment Variables and Model-ing Approaches was held at NCAR on October 1-5, 1984. The workshop was de-signed to identify the key physical variables that are of interest to policyand assessment processes in NAPAP and to estimate the most probable outputforms of these variables from the models. It was also to establish a frame-work for studying the construction of a set of "engineering" models suitablefor fast-turnaround policy and assessment use. While there is a clear needfor "engineering" models, it must be emphasized that they cannot be createdwithout the basic understanding embodied in the comprehensive RADM. As wasfirst proposed and as will be further elaborated upon in later sections ofthis report, the most flexible and efficient program for "engineering" modeldevelopment and evaluation derives from the fundamental scientific credibilityof the comprehensive RADM.

A small group of NAPAP staff and researchers representing members of theNAPAP modeling, assessment, and management communities and EPA policy staffwas invited to NCAR on October 1-5, 1984 to participate in a frank exchange ofideas directed toward the specific goals stated above. All of the partici-pants (Table 5.1) contributed to the contents of the workshop report. Theexecutive summary (Table 5.2) outlines the major findings of the workshop.

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Table 5.1

List of ParticipantsWorkshop on Acid Deposition Policy and Assessment Variables

and Modeling Approaches

National Center for Atmospheric ResearchBoulder, Colorado

October 1-5, 1984

Dick BallRichard BarchetCarl BerkowitzChandrakant BhumralkarRichard BrostJulius ChangJohn F. ClarkeEirh-Yu HsieHsiao-ming HsuIvar IsaksenGregory McRaePaulette MiddletonJana MilfordDave RenneEdward RubinWi1 iam StockwellDennis TirpakDennis TroutChris WalcekDerek WinstanleyDwain Winters

DOE/ADASDOE/ADASPNLSRINCARNCAREPA/RTPNOAA/ERL/Aeronomy LabNCARNCAR/University of OsloCarnegie-Mellon UniversityNCARCarnegie-Mellon UniversityPNLCarnegie-Mellon UniversityNCAREPA/ORD/Assessment staffEPA/ORD/ADRSNCARNAPAPEPA/OAR

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Table 5.2

Executive Summary

Workshop on Acid Deposition Policy and Assessment Variablesand Modeling Approaches

National Center for Atmospheric ResearchBoulder, Colorado

October 1-5, 1984

(1) The type and format of physical variables that are of use to thepolicy and assessment needs of NAPAP were defined. This includesthe time and spatial resolutions for the desired variables.

(2) A group of preliminary technical studies has been identified andshould be carried out so as to guide the development and implemen-tation of a definitive plan for the construction of an appropriate"engineering" model(s).

(3) Several concepts for "engineering" models were explored and a 3-DEulerian model with reduced resolution and carefully parameterizedphysical and chemical submodules was identified as the most feas-ible approach.

(4) It was concluded that, based on the above concept, an efficient,fast-turnaround, and scientifically state-of-the-art "engineering"model can be built by mid-1986 for policy and assessment applica-tions, despite the severe time constraints. This was judged ade-quate by the policy and assessment staff for 1987 while awaitingthe expanded set of models destined for the 1989 assessment exer-cise.

(5) Scientific credibility of the comprehensive RADM will be estab-lished through progressive evaluations of the individual submodelsand the full model in a continuous manner. When appropriate, allavailable data from laboratory studies to field observations willbe used.

(6) Unanimous consent was reached that the comprehensive Regional AcidDeposition Model (RADM) is essential for developing the necessaryparameterization and for evaluating the adequacy of the "engineer-ing" model(s).

(7) These "engineering" models must be developed and be made availableon computers more accessible to the users. Further considerationof future transfer of these models to other computers dictates theuse of a VAX-class minicomputer as the development tool.

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Section 5.2 summarizes the basic needs of the policy and assessment communi-ties. Section 5.3 discusses several concepts for "engineering" models thatmay meet these needs. Section 5.3 also discusses the essential role of thecomprehensive RADM and the hardware required for "engineering" model develop-ment. In Section 5.4, we describe the recommended preliminary technical stud-ies required to assure a final plan for an "engineering" model that is realiz-able within the time constraints of the 1987 assessment report.

5.2 Policy and Assessment Variables and Model Requirements

The source-receptor relationship is a main objective in studying regionalacid deposition problems. Policy decisions are always imminent. Therefore,improvements in the state of the art of source-receptor modeling will be ofgreatest utility if they are made available incrementally over the developmentperiod. Therefore, a sequence of well documented, improving, and timelyavailable simple models with a clear common reference such as the comprehen-sive RADM would be very useful to the policy and assessment communities. Froma policy standpoint, it was stated that it is desirable that a federal programnot allocate emissions reductions below a state or large sub-state division,i.e., eastern Ohio vs. western Ohio. It is unlikely that a federal programwould address a source-by-source reduction. Consideration of emission reduc-tions aggregated at such regional levels would tend to be optimized towardtotal deposition as semiannual to multiyear averages. It is reasonable toassume that, with respect to regional acid deposition, decisions on the needfor additional sulfur control are more eminent than those on hydrocarbons andnitrogen oxides. Therefore, development of improved ability to model thetransport and deposition of sulfur-containing compounds in the atmosphereshould take the highest priority. Only the absolutely essential chemical de-tails should be included if that will facilitate the completion of a practicalmodel in the near future. A fast-turnaround sulfur model would be adequate ifit could provide quantitative information on sulfur target loadings in a plusor minus 50 percent range from 20 kg/hectare.

Beyond this general statement on the highest priority needs for policyconsideration, we can be much more specific from several perspectives. Thephysical variables of most interest are sulfate deposition, total acidity,S02 and 03 concentrations, nitrate deposition, precipitation amount, andammonia. In most applications, quantitative information on these variableswith temporal resolution of seasonal to annual deposition would be more thanadequate. Spatial resolutions should be adequate for studying interstatesource-receptor relationships, multistate influence on sub-state regions (suchas the Adirondack lake region), and transboundary fluxes of pollutants betweenthe United States and Canada. Operationally, assessment models must be ableto handle both area-wide (state size) and point sources. They must also havethe ability to differentiate the roles of local and distant sources, to anal-yze historical databases, and, most important of all, to quickly evaluate theimpacts of many emission distribution scenarios (i.e., fast-turnaround compu-tation).

The potential uses of a set of well established "engineering" models aremany and varied. Therefore, they should be transportable to computers otherthan the one they were developed on. Furthermore, as their uses broaden, ad-

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ditional simplifications such as transfer matrix representation may result.This would require careful structuring of input and output data to and fromthe "engineering" models. An additional application of the "engineering"models would be to serve as the atmospheric model components of the integratedassessment models.

Finally, these "engineering" models must be scientifically defensible.Their strengths, weaknesses, sensitivities, and insensitivities must be tho-roughly and clearly stated.

The most feasible plan is to derive this level of credibility throughthat of the comprehensive RADM. RADM development calls for continuous andprogressive evaluations using all appropriate data, from laboratory experi-ments to field observations. Individual submodels as well as the full RADMwill be evaluated. In the early phase of "engineering" model development, itwill have incomplete validation due to the preliminary nature of availableRADM evaluations, but the inherent coupling and compatibility of these twotypes of models make it easy to transfer the expected continuously-improvingscientific credibility of RADM to the derived "engineering" model(s).

It is clear that not all of these requirements can be met within the timeframe of the 1987 assessment report. Some of these requirements are probablynot essential to the concept of the "engineering" model. Concepts such astransfer matrix and atmospheric components of the integrated assessment modelare best developed separately. In the following sections, we shall discuss aset of concepts for meeting in different ways many of the requirements asstated here. A most preferred concept will be identified, and preliminarystudies designed to enhance the timely planning and construction of this pre-ferred "engineering" model will be explained.

5.3 Concepts for "Engineering" Models

There were four types of models that were judged to be worthy of consi-deration by the participants of this workshop: low resolution 3-D and 2-DEulerian models and 3-D and 2-D Lagrangian models. Empirical models and otherhybrid models are either too limited in general applicability or scientificcredibility. All of these four types of models were judged to be capable ofproviding the horizontal spatial resolution required and the spatial coverage(31 states and southern Canada). Obviously, the 2-D models would not be ableto provide vertical resolutions necessary for clarifying the role of upper airtransport in regional acid deposition problems. At this time, there is verylittle information on the difficulties one would face in developing a 3-D La-grangian model. For a 3-D Eulerian model, the basic capability for verticalresolution exists. It is mostly a question of desired accuracy. For the pre-sent consideration, we are not focusing upon all the well known questions onmodeling physical processes. We are considering the relative merits of thesemodels as "engineering" models supported by information and parameterizationsdeveloped from the comprehensive RADM. The group firmly believes that withoutthe comprehensive RADM it would be very difficult to develop the necessaryparameterizations and to provide any level of scientific defensibility for themodeling results.

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The 2-D Lagrangian model has the demonstrated ability for studying sea-sonal deposition, and hence annual deposition. The Eulerian models have equaldifficulty in analyzing depositions on time scales longer than episodicevents. The 3-D Lagrangian model would have great difficulty in obtaining theneeded 3-D trajectories over any season or year. Upper air meteorologicaldata are too sparse to be of any use without the help of some mesoscale (Eu-lerian) meteorological model. For an "engineering" model, the proposed ap-proximations for chemical transformation and scavenging are to be developedfrom the comprehensive RADM. Therefore, all the models have about equal ca-pability when one considers one family of chemical species at a time, e.g.,SOx. As the complexity of the chemical system increases, the 3-D Eulerianmodel becomes the clear favorite. Insofar as the ease of implementation goes,2-D models are somewhat superior, with the 3-D Lagrangian model being the mostdifficult. The output of this latter model is most easy to interpret forpoint sources, while the 3-D Eulerian model is most meaningful on a regionalscale. Overall, a lower resolution 3-D Eulerian model can best match the per-formance of the comprehensive RADM and utilize the parameterizations developedfrom this latter model.

A major consideration of the group was the available techniques for de-veloping simplified representations of many nonlinear physical and chemicalprocesses. The most obvious approach would be using the comprehensive RADM tosimulate the relevant processes in as elaborate a manner as possible. Simpleapproximations to these results, perhaps with limited generalities, can thenbe constructed. At the minimum, these approximations can be evaluated withthe full model. The basic compatibility of a lower resolution 3-D Eulerian"engineering" model with the comprehensive RADM allows quick and timely updateas our understanding of acid deposition improves. For example, the comprehen-sive model may provide a highly nonlinear sulfate transformation rate depend-ing on geographic location and a few meteorological variables. This responsesurface can be approximated by simple relations with well known techniques.Thereafter, the "engineering" model can use these approximations and onlyneeds to be checked when the range of variables in current use exceeds theoriginal range of applicability. This can be done if we look upon the processas an exercise in mathematical approximation rather than physical approxima-tion. The "engineering" model can have 3-5 vertical levels and less than 1/4the horizontal resolution of the full model. Each version of the model needsto consider only one family of chemical species with parameterized transform-ation and deposition processes. This would so simplify the computational dif-ficulty as to make the "engineering" model executable on a VAX-class mini-computer. At this time, consideration of a smaller than VAX-class computerwould not be advisable due to the additional time needed, which would be inconflict with the 1987 assessment deadline. The lack of nonlinear feedbackamong the "engineering" models can be cross-checked and reevaluated by thefull model. Beyond 1987, we can then consider more direct couplings and moreefficient computational models. At that time, we can study the advisabilityof converting some of these "engineering" models to other computers.

There are many questions related to details of the "engineering" modelssuch as how many, how fast, how to derive seasonal and annual averages, howflexible in input and output, model evaluation protocol, and sensitivityanalysis techniques that require more deliberated considerations. Several

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preliminary studies have been suggested by the participants of this workshopto at least address some of these questions.

5.4 Recommendations for Technical Studies

The above discussions on the preferred "engineering" model type and thereasons for reaching this conclusion are all based on indirect evidence. Atthis time, there is no such model. Therefore, it is important to examine someof the recognized potential difficulties immediately. In FY 1985, as soon asthe funding becomes available, a three-month study phase with specific techni-cal studies for "engineering" model development should be carried out. Thisset of technical studies or equivalent studies would be required regardless ofthe preferred "engineering" model type. These studies are generic in nature.The need for examining simplification of the nonlinear chemistry and observa-tion databases is absolutely necessary in all cases. The evaluation of compu-tational cost is also common to all model types.

(1) Parameterization of Nonlinear Chemistry

A dominant component of the computational cost associated with des-cribing the processes responsible for acid deposition is the numer-ical solution of the equations for gas and aqueous chemistry. Inpractice, the complexity of the chemical system forces a number oftradeoffs. Chemical details have often been traded for computation-al efficiency, and the number of desired runs often determines theacceptable computational cost per run. Availability of data andneeded spatial resolutions also limit the choice of chemical de-tails. The comprehensive RADM was designed to examine all the de-tails in nonlinear chemical interactions. Hence, it could be usedto carry out the following studies:

(a) Identify and rank the processes responsible for the nonlinearinteractions with respect to the key variables required for as-sessment purposes. Critical objectives are to isolate rate orprocess limiting steps in both gas and aqueous phase chemistryand to bound the strength of the nonlinearities.

(b) Investigate the chemical consequences of long-term and largespatial averaging inherent in the low resolution 3-D Eulerianmodel. This and the above activity should also establish thelikely domains of variations in the initial conditions andemissions as model inputs.

(c) Demonstrate the feasibility of developing simple approximationsto specific transformation processes. Piecewise linear andpolynomial approximations are two simple candidates.

(2) Evaluating the Computational Cost of a Low Resolution 3-D EulerianModel

A simple 3-D Eulerian long-range transport model including two orthree chemical variables should be constructed. With this model

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working on a VAX-class minicomputer, we can establish the tradeoffsamong spatial resolutions (vertical and horizontal), accuracy, andcomputational cost. Only with such direct information can we speci-fy the detailed computational structure of the "engineering" models.This type of evaluation would not be necessary for the comprehensiveRADM since this is not one of the high priority design criteria.

(3) Evaluation of Availability and Utility of Existing Database

Three types of data are required by the models: initialization,execution, and evaluation. The needs for the "engineering" modelsare different from those of the comprehensive RADM with differentsensitivities and resolutions. Lower resolution and simpler chem-ical representation may require more clever specification of thedatabase and more careful interpretation of model results in eval-uation studies. The comprehensive RADM could also be used to filldata gaps for the use of "engineering" models. It would be impor-tant to know the extent that this can be done. This would havemajor impact on any proposal for future field programs.

(4) Preliminary Tests on the Feasibility of a Statistical Ensemble Pro-cedure and the Direct Simulation Approach to Long-Term Averaging

Two fundamental approaches can be used to generate seasonal and an-nual averaged depositions with "engineering" models. The first isusing statistical ensemble averages and the second is direct simula-tion with long-term synoptic data inputs covering appropriate tem-poral periods. The latter approach is made possible only if "engi-neering" models are sufficiently fast in execution. The statisticalapproach of selecting representative synoptic events and gatheringstatistical data on frequency of occurrences is conceptually moreeconomical than the direct approach, but its applicability on a re-gional scale must be demonstrated. It may be possible to use theexisting precipitation database to test the accuracy or even theapplicability of this approach. Results from this study would de-termine the efforts required to reduce computational time and whe-ther we should establish the synoptic database for assessment use.Related questions such as interannual variability and nonlinear ave-raging are beyond this short study period. They should be part ofthe long-term NAPAP research program.

At the completion of this study phase, a detailed plan should be devel-oped to construct the first "engineering" model. This plan should includedescriptions of techniques for obtaining simplified approximations of indi-vidual physical and chemical processes. Expected model resolution and compu-tational efficiency should also be available. Protocol for interactions withthe comprehensive RADM should also be established at this time.

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SECTION 6

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