Chemical Data Assimilation in Support of Chemical Weather

ForecastsGreg Carmichael, Adrian Sandu, Dacian Daescu, Tianfeng Chai,

John Seinfeld, Tad Anderson, Peter Hess, Dacian Daescu

Data Assimilation

Chemical Data Assimilation in Support of Chemical Weather

ForecastsOutline Motivation

Current State of Forward Models

Data Assimilation Framework (4d- Var) – Issues

Preliminary Results

Future Directions

Models are an Integral Part of Atmospheric Chemistry Studies

• Flight planning• Provide 4-Dimensional context of the

observations• Facilitate the integration of the different

measurement platforms • Evaluate processes (e.g., role of biomass

burning, heterogeneous chemistry….)• Evaluate emission estimates (bottom-up

as well as top-down)• Emission control strategies testing• Air quality forecasting

TRACE-P/Ace-Asia/ITCT-2K1 EXECUTION

Emissions-Fossil fuel-Biomass burning-Biosphere, dust

Long-range transport fromEurope, N. America, Africa

ASIA PACIFIC

Satellite datain near-real time:MOPITTTOMSSEAWIFSAVHRRLIS

3D chemical model forecasts: - x - GEOS-CHyEM - CFORS - z

FLIGHTPLANNING

Boundary layerchemical/aerosolprocessing

ASIANOUTFLOW

Stratosphericintrusions

PACIFIC

Forward Models Are becoming More Comprehensive

MesoscaleMeteorological Model

(RAMS or MM5)

MOZART Global Chemical Transport Model

STEM Prediction Model with on-line

TUV & SCAPE

Anthropogenic & biomass burning Emissions

TOMS O3

Chemistry & TransportAnalysis

Meteorological Dependent Emissions

(biogenic, dust, sea salt)

STEM Tracer Model (classified tracers for

regional and emission types)

STEM Data-Assimilation

Model

Observations

Airmasses andtheir age & intensity

Analysis

Influence FunctionsEmission Biases/

Inversion

110 115 120 125 130 135 1400

1

2

3

4

5

6

7

CO Scale(ppbv)300+250 to 300200 to 250150 to 200100 to 15050 to 100

110 115 120 125 130 135 1400

1

2

3

4

5

6

7

K(ug/m3)1+0.8 to 10.6 to 0.80.4 to 0.60.2 to 0.40 to 0.2

Fight Planning: Frontal outflow of biomass burning plumes E of Hong Kong

Observed CO –Sacshe et al.

Observed aerosol potassium - Weber et al.

Biomass burning CO forecast

Longitude

100 ppb

P-3B

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Te

mp

era

ture

H2

O

Win

d S

pe

ed

O3

SO

4

J[O

1D

]

SO

2

PA

N

Eth

en

e

Pro

pa

ne

CO

J[N

O2

]

Eth

an

e

No

y

Eth

yn

e

RN

O3

Be

nze

ne

+ T

olu

en

e

OH

AO

E

HN

O3

NO

2

NO

Co

rrela

tio

n C

oeff

icie

nt

R(<1KM)

R(1-3KM)

R(>3 KM)

Predictability – as Measured by Correlation Coefficients

Met Parameters are Best

Performance decreases with altitude

< 1km

Model vs. Observations

Modeled O3 vs. Measured O3

• Cost functional measures the model-observation gap.

• Goal: produce an optimal state of the atmosphere using:

Model information consistent with physics/chemistry

Measurement information consistent with reality

+

Development of a General Computational Framework for the Optimal Integration

of Atmospheric Chemical Transport Models and Measurements Using Adjoints

(NSF ITR/AP&IM 0205198 – Started Fall 2002)

A collaboration between:

Greg Carmichael (Dept. of Chem. Eng., U. Iowa)Adrian Sandu (Dept. of Comp. Sci., Virginia Tech.)

John Seinfeld (Dept. Chem. Eng., Cal. Tech.)Tad Anderson (Dept. Atmos. Sci., U. Washington)

Peter Hess (Atmos. Chem., NCAR)Dacian Daescu (Dept. Math, Portland State)

http://atmos.cgrer.uiowa.edu/people/tchai/

Basic Idea of 4D-Var

0 0 b 1 0 b obs 1 obs

0

1 1( )

2 2

NT Tk k k kk

k

J c c c B c c c c R c c

•Define a cost functional

•Derive adjoint of tangent linear model

λ λ( λ ) ρ (ρ )λ φ

ρTi i

i iiu K F c

t

Where adjoint variables are the sensitivities of the cost functional with respect to state variables (concentrations), i.e.

ii c

J

•Update Initial conditions using the gradients

Useful by themselves !!

Assimilation ResultsAssimilate O3/NO2 with O3/NO2 observations in the window [0,6] GMT, March 01, 2001;Twin experiments framework;Full 3D simulation with SAPRC chemical mechanism.

O3

CO-assimilation

Observation Frequency vs Number of Species O

3

O3 - only

O3 & NO2

Recovery of O3 and NO2 is Different WHY?

NO2

O3

Most of the grid points values are recovered within in 1%; but some locations the error is > 20%.

1%

20%

Assimilation requires better algorithms (with known error behavior)

Additional details see Chai’s paper on Thursday

Overview of Research in Data Assimilation for Chemical Models. Solid lines represent current capabilities. Dotted lines represent new analysis capabilities that arise through the assimil. of chemical data.

Ensemble methods

Chemical Assimilation and

Big-Iron“BIGMAC”@VT

Ranked 3rd with measured performance = 10 Tflop/s.A Pentium class cluster with 16-24 processors has ~ 50 Gflop/sec.On such a cluster we run parallel STEM (TraceP): 1 hour simulation time / 5  minutes cpu timeOn the terrascale machine we can run in parallel an ensemble of 200 simulations for the same simulation / cpu time ratio.

Assimilation of Aerosol Dynamics

•Theoretical framework enables the solution of coupled coagulation and growth with minimal number of size bins;

•Piecewise polynomial discretizations;

•Adjoint/assimila-tion system built

Data FrequencyGradient Methods

Recovery of Initial Distribution

We plan to test some of these developments in an operational setting this summer as part of a large field experiment.

We are Developing General Software Tools to Facilitate the Close Integration of Measurements

and Models

The framework will provide tools for: 1) construction of the adjoint model; 2) handling large datasets; 3) checkpointing support; 4) optimization; 5) analysis

of results; 6) remote access to data and computational resources.

Adjoints being developed for MOZART, plans for WRF-Chem

http://atmos.cgrer.uiowa.edu/people/tchai/

Chemical Data Assimilation: The Future?

Feasible & necessary.Just the beginning—

more ??s than answers – but we have test beds!

Huge implications for measurement systems and models.

Need to grow the community.

PORT PHILLIP BAY

260 280 300 320 340 360

EASTING (km)

DND

BRI

FTSPSY

PTC

MTC ALP

PTHGLS

GVD

PLP BXH

5740

5760

5780

5800

5820

5840

NORTHIN

G(km)

LIGHT

MODERATE

HEAVY

AIR QUALITY FORECAST-MELBOURNE

AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE

NORTH EAST

HOUR

IND

EX

NORTH EAST

HOUR

IND

EX

Tomorrow will be fine and sunnyTomorrow will be fine and sunny--with moderate to heavy air pollutionwith moderate to heavy air pollution

PORT PHILLIP BAY

260 280 300 320 340 360

EASTING (km)

DND

BRI

FTSPSY

PTC

MTC ALP

PTHGLS

GVD

PLP BXH

5740

5760

5780

5800

5820

5840

NORTHIN

G(km)

LIGHT

MODERATE

HEAVY

AIR QUALITY FORECAST-MELBOURNE

AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE

PORT PHILLIP BAY

260 280 300 320 340 360

EASTING (km)

DND

BRI

FTSPSY

PORT PHILLIP BAY

260 280 300 320 340 360

EASTING (km)

DND

BRI

FTSPSY

PTC

MTC ALP

PTHGLS

GVD

PLP BXH

5740

5760

5780

5800

5820

5840

NORTHIN

G(km)

LIGHT

MODERATE

HEAVY

AIR QUALITY FORECAST-MELBOURNE

AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE

NORTH EAST

HOUR

IND

EX

NORTH EAST

HOUR

IND

EX NORTH EAST

HOUR

IND

EX

NORTH EAST

HOUR

IND

EX

Tomorrow will be fine and sunnyTomorrow will be fine and sunny--with moderate to heavy air pollutionwith moderate to heavy air pollution

TWO-SCENARIO TWO-SCENARIO FORECASTFORECAST

http://www.wmo.ch/web/arep/gaw/urban.html

Air Quality Forecasting Research Elements

Summary of USWRP Air Quality Forecasting WorkshopApril 29 - May 1, 2003

Houston, TX