Copyright 2015, Daniel J. Vecellio

An Assessment of Short-term Synoptic Air Mass Modification throughLand-Atmosphere Interactions

by

Daniel J. Vecellio, B.S.

A Thesis

In

Atmospheric Science

Submitted to the Graduate Facultyof Texas Tech University in

Partial Fulfillment ofthe Requirements for

the Degree of

MASTERS OF SCIENCES

Approved

Dr. Jennifer VanosCommittee Chair

Dr. Eric Bruning

Dr. David Hondula

Mark SheridanDean of the Graduate School

May, 2015

Copyright 2015, Daniel J. Vecellio

Texas Tech University, Daniel J. Vecellio, May, 2015

ACKNOWLEDGEMENTS

I would like to acknowledge:

Dr. Jennifer Vanos, for bringing me into her research group early into her

career here at Texas Tech and a year into my graduate studies, an inopportune time

for the both of us. I’d like to thank her for all the knowledge she has imparted on

me and all the connections she has allowed me to make in the short year we have

worked together that will benefit me for a lifetime. And finally, I’d like to give her

my sincerest gratitude for providing the path that allowed me to reconnect with the

passion I had for atmospheric science and academia in general.

My two other committee members, Dr. Eric Bruning and Dr. David Hondula.

Dr. Bruning has not only been a resource for feedback on research ideas, but also a

constant help with questions and concerns ranging from coding to the research

process itself. It has all been truly appreciated. Dr. Hondula came onto my

committee without even knowing what he was getting into with me, but I hope that

he has not regretted the decision. Thank you, Dave, for all the help and I hope to

continue working with you in the future.

Trent Ford and Dr. Steven Quiring of Texas A&M University for all of their

help with soil moisture data and their comments which improved the methodology

employed in this study.

The Texas Tech Climate Science Center and, specifically, Ian Scott-Fleming for

help with data acquisition and manipulation.

The NOAA Air Resources Laboratory (ARL) for providing reanalysis data and

answering my numerous questions on running the HYSPLIT model.

The Earth Observing System at NASA for radiation data from the CERES

project.

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The International Research Institute at Columbia University for NOAA OI sea

surface temperature data.

Dr. Jon Nese, my undergraduate advisor at Penn State University and still my

academic mentor today. I’ve always taken something from each of our talks over the

past seven years in Walker Building. Thank you for all you did during my time in

the Happiest of all Valleys and even more so after you were rid of me.

Nick Smith, my officemate for two years and Aaron Hill, my roommate for one.

Thanks for dealing with my antics since we started in August of 2012 by either

going to grab a beer with me or simply telling me to shut up.

Tony Reinhart, for the reasons listed above as well as helping me out with

numerous computer issues throughout the past two years.

My two best friends from undergraduate studies, Greg Ferro and Simone

Gleicher, for our monthly Google Hangouts which were always a welcome break

from the world of constant work. I’ll meet you two at Cafe 210 for teas once this

thesis passes.

Kevin Horne, Ryan Beckler, Devon Edwards, Julia Kern, Jessica Tully and

Anna Orso who all befriended me soon after my return to State College in January

2012 and got me through what was certainly the most stressful and lost periods of

my life. #DMT and “Hey, Jude”, y’all.

All others I have written with throughout the years at Onward State and Black

Shoe Diaries, especially Davis Shaver, Chase Tralka, Eli Glazier, Evan Kalikow, Dan

McCool, Bill DiFilippo, Chris Grovich, Jeff Junstrom, Mike Pettigano, Jared

Slanina and Cari Greene.

Ginuwine, as without his musical masterpiece “Pony,” I may not have written a

single line of code correctly over the past two years.

My parents, who have never stopped believing in me since the day I was born.

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Whether it was making sure I had a TV Guide to read when I was two, getting me

to soccer or basketball practice during grade school or listening to my problems,

both academically and personally, throughout college, I don’t know what I would

have done without all the love you’ve provided.

The rest of the Texas Tech Atmospheric Science Group and the National Wind

Institute, everyone else that I have met while in Lubbock, the rest of my friends and

family in Bradford and State College and everyone else who has supported my

journey from home to Penn State to Florida State to not knowing what was going

to happen to my eventual landing spot here at Texas Tech.

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TABLE OF CONTENTS

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 A Brief History of Weather Prediction . . . . . . . . . . . . . . 1

1.2 Studies on Air Mass Modification . . . . . . . . . . . . . . . . 3

1.3 The Spatial Synoptic Classification System . . . . . . . . . . . 6

1.4 Overview and Applications of this study . . . . . . . . . . . . . 10

2. Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1 Geographical and Seasonal Focus . . . . . . . . . . . . . . . . 13

2.2 Spatial Synoptic Classification . . . . . . . . . . . . . . . . . . 14

2.3 Surface moisture . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.4 Reanalysis, Back Trajectories and Clustering . . . . . . . . . . 21

3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.1 Methodological Limitations . . . . . . . . . . . . . . . . . . . . 26

3.1.1 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.1.2 Evapotranspiration Over Water . . . . . . . . . . . . . . . 30

3.2 Weather Type and Modification Frequency . . . . . . . . . . . 31

3.3 Case-Study: Huntsville, Alabama Dry Tropical (DT) Modification 34

3.3.1 MT-to-DT Modification . . . . . . . . . . . . . . . . . . . 34

3.3.1.1 Southeastern U.S. High Pressure Center . . . . . . . 35

3.3.1.2 “No Man’s Land” High Pressure Presence After Frontal

Passage . . . . . . . . . . . . . . . . . . . . . . . . . 36

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3.3.2 DM-to-DT Modification . . . . . . . . . . . . . . . . . . . 38

3.4 Effect of Evapotranspiration on Modification . . . . . . . . . . 40

4. Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.1 Synopsis of Results . . . . . . . . . . . . . . . . . . . . . . . . 50

4.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.3 Implications and Applications . . . . . . . . . . . . . . . . . . 55

4.4 Final Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 61

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

A Helpful Code. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

B Miscellaneous Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

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ABSTRACT

As air masses move across North America, they inherit the characteristics of

both the ambient air they move through, as well as the properties of the surface

they advect over. Due to the motion of said air masses, they become modified, both

in temperature and moisture content. It is advantageous to trace how these air

masses are modified spatially and temporally from their sources, as specific air

masses have been found to be detrimental to human health with respect to the

season. The goal of this project is to develop the methodology to create an

automated model to forecast synoptic weather types that will incorporate the upper

and lower level meteorological variables.

The Spatial Synoptic Classification System (SSC) will be employed to classify

air masses into one of seven types during warm season (May-September) events.

Five cities have been selected as target locations (Wilmington, Delaware,

Raleigh-Durham, North Carolina, Huntsville, Alabama, Lexington, Kentucky and

Oklahoma City, Oklahoma). These were chosen as they have readily available SSC

data and are located eastward enough that air parcels will track over land for a

suitable duration before ending at the target location. Using the Hybrid

Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, back

trajectories from these target regions will be computed from Eta Data Assimilation

System (EDAS) reanalysis data. Using the measure of evapotranspiration to try

and determine how moisture makes its way from the land to the atmosphere along

the paths of those trajectories, it is hopeful that it will help research better

understand how the air masses changed along their paths from source to target.

Tried and failed methodology is discussed as a way to help future researchers who

may attempt to provide additional solutions to the study of air mass modification.

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In addition, case studies were completed to attempt to find a connection between

modification and synoptic patterns.

The quantitative evapotranspiration study did not yield statistically significant

differences between parcel trajectories that spent varying amounts of time over a

water body and trajectories that journeyed solely over land. The qualitative

case-study did provide positive results, however, questions surrounding the physics

of the HYSPLIT model outputs are or become present. Additionally, the relativity

of the SSC, normally lauded for its uniqueness and ease of applicability, becomes a

point of contention in the scope of this study. Air mass modification is not able to

be explained within the constraints of this project, but through investigation, it

becomes apparent that both qualitative and quantitative methods must be examined

and that focus on the synoptic scale is not sufficient for fully describing the process.

viii


LIST OF TABLES

3.1 Average evapotranspiration (kg m-2) values for full and partial trajec-

tories in MT-to-DT modification scenarios. . . . . . . . . . . . . . . . 41

3.2 Average evapotranspiration values (kg m-2) for full and partial trajec-

tories in DT-to-MT modification scenarios. . . . . . . . . . . . . . . . 42

3.3 Average evapotranspiration values (kg m-2) for full and partial trajec-

tories in DM-to-MT modification scenarios. . . . . . . . . . . . . . . . 42

ix


LIST OF FIGURES

1.1 Basic sketch of factors affecting air mass modification . . . . . . . . . 6

2.1 Map of the five target locations chosen for study. . . . . . . . . . . . 14

2.2 Main methodological flowchart . . . . . . . . . . . . . . . . . . . . . . 25

3.1 Map of Lexington, KY MT trajectories . . . . . . . . . . . . . . . . . 28

3.2 Map of Oklahoma City, OK MT trajectories . . . . . . . . . . . . . . 29

3.3 Sample evapotranspiration data from Huntsville DT trajectories. The

seventh column shows values taken directly from GLDAS-1 data. The

eighth column shows calculated values using the Priestley-Taylor equa-

tion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.4 Surface analyses from Day 0-4 of event taking place June 4-8, 2008. A:

Day 0. B: Day 1. C: Day 2. D. Day 3: E. Day 4: Subfigure F shows

the trajectory into Huntsville for the four-day event. (Source: NOAA

WPC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.5 Same as Figure 3.4 but for July 19-23, 2010 event. . . . . . . . . . . . 45

3.6 Same as Figure 3.4 but for August 2-6, 2008 event. . . . . . . . . . . 46

3.7 Same as Figure 3.4 but for July 28-August 1, 2011 event. . . . . . . . 47

3.8 Same as Figure 3.4 but for May 2-6, 2008 event. . . . . . . . . . . . . 48

3.9 Same as Figure 3.4 but for August 15-19, 2008 event. . . . . . . . . . 49

A.1 Average evapotranspiration (kg/m2) values for each modification sce-

nario of the Huntsville, AL DT dataset. Full and partial designations

are described in Section 3.4 . . . . . . . . . . . . . . . . . . . . . . . 71

A.2 Same as Figure 4.1 but for Huntsville, AL MT dataset . . . . . . . . 71

A.3 Same as Figure 4.1 but for Wilmington, DE DT dataset . . . . . . . 72

A.4 Same as Figure 4.1 but for Wilmington, DE MT dataset . . . . . . . 72

x


A.5 Same as Figure 4.1 but for Lexington, KY DT dataset . . . . . . . . 73

A.6 Same as Figure 4.1 but for Lexington, KY MT dataset . . . . . . . . 73

A.7 Same as Figure 4.1 but for Raleigh-Durham, NC DT dataset . . . . . 74

A.8 Same as Figure 4.1 but for Raleigh-Durham, NC MT dataset . . . . . 74

A.9 Same as Figure 4.1 but for Oklahoma City, OK DT dataset . . . . . . 75

A.10 Same as Figure 4.1 but for Oklahoma City, OK MT dataset . . . . . 75

A.11 Same as Figure 4.1 but for a five-city average of DT-resultant modified

weather types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

A.12 Same as Figure 4.1 but for a five-city average of MT-resultant modified

weather type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

xi


CHAPTER 1

INTRODUCTION

1.1 A Brief History of Weather Prediction

Weather prediction has advanced significantly since 340 B.C. when the Greek

philosopher Aristotle wrote Meteorologica in which he described the process of

evaporation and laid out the properties of weather phenomena such as tornadoes.

Lynch (2008) comprehensively outlined the advancements in weather prediction over

approximately the last century, summarized as follows: Near the beginning of the

20th century, Vilheim Bjerknes, a Norwegian scientist, developed the set of

equations that numerical weather prediction, or “NWP”, is based upon today.

However, he did not have a numerical or analytical way to solve them. Around the

same time, Lewis Richardson caught onto the work of Bjerknes and derived a

numerical process to develop a forecasted state of the atmosphere based on the same

state equations. He came to realize that a more robust set of initial observations

was needed for even a six-hour forecast, as his prediction of surface pressure changes

were off by two orders of magnitude. The time to complete the forecast also

presented a problem as it took longer to complete the forecast for one time step

than the duration of the time step itself. Help would come in the mid-1940s as John

von Neumann and Jule Charney collaborated to forecast the turbulent fluid flows of

the atmosphere using the Electronic Numerical Integrator and Computer (ENIAC).

A filtered set of Bjerknes’ original equations were inputted into ENIAC and a

forecast was outputted some time later, normally in just enough time to keep up

with the weather (i.e. a runtime of 24 hours for a 24-hour forecast). Numerous

advancements were made as computer power increased over time.

There is now the ability to forecast at the mesoscale level as well as coupling

1


the atmosphere and ocean in general circulation climate models (GCMs). Multiple

numerical weather prediction models are operational as well, including the Weather

Research and Forecasting (WRF) model (Skamarock et al., 2001), the Global

Forecast System (GFS) (Kanamitsu, 1989; Kalnay et al., 1990; Kanamitsu et al.,

1991), the U.S. Navy’s Operational Global Atmospheric Prediction System

(NOGAPS) Model (Hogan et al., 1991) along with many others based in the United

States and across the world.

Starting in the late 1950s, a new method of predicting atmospheric variables

came into practice. Klein et al. (1959) developed the “perfect prog” method that

combined numerical, dynamical weather prediction – which had become more

popular with the emergence of increased computing capability – with statistical

methods. The original perfect prog used concurrent statistical relationships between

observed values of predictors and the variable to be estimated which would be

implemented with the model forecast to create the perfect prog forecast. Glahn and

Lowry (1972) developed Model Output Statistics, or MOS, a statistical method of

forecasting that is still used by forecasters today. MOS forms its statistical

relationships using observed values as well as historical model data before being

implemented with the current model forecast to create its perfect prog forecast.

This allows MOS to account for model biases, but also causes its statistical

relationships to be model-dependent. The team used a screening regression to relate

the predictand and the independent variables to explain and reduce the variance

between them. At this point in time, there existed two separate but integrated ways

to satisfactorily predict a range of atmospheric variables for weather forecasting.

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1.2 Studies on Air Mass Modification

The study of air mass modification began with those who birthed the science of

meteorology. This included those from the Bergen school which included the

previously-mentioned Vilheim Bjerknes, his son Jacob and his student Tor Bergeron.

In Bergeron (1928)’s dissertation, and later his 1930 paper (Bergeron, 1930), he

detailed his air mass classification system and determined that air masses should be

sorted by their sources of origin due to the fact that, even after transport from their

starting point, the main characteristics of the air mass did not change greatly. He

stated that the values of weather variables, such as temperature and humidity, were

so strongly retained during their track over oceans and continents and that knowing

the source region was essential to the future of weather forecasting. However,

studies on air mass modification since the time of Bergeron have shown that

modification takes place at a faster rate than what he had hypothesized.

Much of the previous air mass modification studies in the literature largely

focused on the boundary layer, using surface-lower atmosphere interactions to

determine the amount of modification occurring quantitatively. The first such study

was performed by Burke (1945) where he studied cold air masses transversing a

warm body of water, focusing on the transition from continental polar air masses to

maritime polar air masses based on the work of Bergeron (1928, 1930). Although

Burke was studying full air mass modification, he merely calculated temperature

change with time as a function of the initial surface air temperature, initial lapse

rate, sea-surface temperature and distance traversed across a water body, and

compared it with observed values to quantify his relationship. While he saw a high

correlation between his predicted and the observed temperatures, much work

remained to improve his methodology due to the fact that: 1) the characteristics of

an air mass are not spoken for by temperature alone and 2) at the time, there was

3


no way to determine a forecasted trajectory to base an operational model or study

from.

SethuRaman (1976) and Freedman and Fitzjarrald (2001) also maintained a

focus on the boundary layer to understand air mass modification; however, they

used the boundary layer’s growth to determine how air masses changed throughout

time. SethuRaman (1976) mimicked the observational study of Burke (1945),

focusing on the modification of an air mass as it made its way from water to land,

albeit in reverse (warm air mass over colder water) and further verified his simple

empirical model with wind tunnel tests. He placed importance on the time of air

mass travel (which could easily be converted to fetch), the upwind and downwind

surface temperature magnitudes and their difference and, finally, the downwind

friction velocity and, hence, roughness. This latter focus calculated the height of

modification which he signified as the height of the air mass’ low-level inversion.

Freedman and Fitzjarrald (2001) looked away from the typical water-land

interactions and instead focused on how the air characteristics in the forests of the

northeastern United States changed as fronts moved through the area during the

growing season. Like SethuRaman (1976), however, they used the growth of the

mixed layer, the heights of the boundary layer and Lifting Condensation Level

(LCL), and the frequent formation of boundary layer cumulus clouds after frontal

passages as means for determining air mass modification. Using back trajectories to

determine the source region of the fronts, the two found that sufficient, fixed source

regions of energetic thermals working in conjunction with transpiring vegetation in

the area of frontal passage, will most likely ensure the formation of boundary layer

cumulus during these events. The result was adequately replicated in a model

evaluating the mixed layer region and in the micrometeorological fluxes affecting the

boundary layer’s growth.

4


Along with Freedman and Fitzjarrald and their big-picture look at the

movement of fronts into the United States Northeast, Molinari (1987) also expanded

air mass modification with a synoptic viewpoint. He studied the interannual

variations in the position of the Loop Current in the Gulf of Mexico and its

interactions with surface winds. Sensible and latent heat fluxes were computed for

four different scenarios (two positions of the Loop Current, two general surface wind

directions) in the eastern Gulf, demonstrating large increases in latent heat flux for

both Loop Current scenarios (northward expanse and shallow, southerly position)

when northerly winds prevailed. Such situations were normally associated with cold,

dry fronts moving southward. Molinari concluded that changes in the Loop Current

position has an effect on the amount of water vapor transported into the continental

United States. These findings demonstrate that the positioning of the increased

latent heat fluxes calculated impact the number of moist air masses that would be

observed in the Eastern United States.

***

Air mass modification is a complex land-atmosphere interaction problem. As

stated in the literature already cited, an air parcel’s temperature and moisture

profiles can be transformed through a number of different means as it advects

through different environments. Another factor that must be considered as the

world continues to develop is the impact of land use and land-cover change and its

effect on surface interactions with overlying air. Using temperature data from

reanalysis, Kalnay and Cai (2003) estimated a 0.27 degree Celsius mean surface

warming per century due to increased land-use changes, largely the result of

increased urbanization over the past 50 years as well as agricultural expansion and

change. Bonan (2002) investigated changes to the climate system with an ecological

5


focus, concluding that the process of deforestation, occurring over many tropical

rain forests as well as across Europe, Africa and Asia, is leading to warmer and drier

conditions. While this is not a problem within the continental United States, it

shows that land use and land cover change make an impact on the air in the vicinity

of these changes.

Figure 1.1 provides a rough sketch of processes affecting air mass modification.

Figure 1.1: Basic sketch of factors affecting air mass modification

1.3 The Spatial Synoptic Classification System

There have been numerous attempts to classify weather systems since the birth

of modern meteorology beginning with Bergeron (1930)’s manual air mass

classification, which is still widely used today. However, Bergeron’s system is very

primitive when compared to the advances in the study of air mass modification and

weather prediction today. Within the Bergeron system, air is classified by whether

6


it is dry (continental) or moist (maritime) and where it came from/its inherent

temperature characteristics (Arctic, polar or tropical). All analyses are completed

by hand, making it a very time-consuming and arduous process. Automated

methods began to appear once computing power met demands, starting with Lund

(1963) whose sea-level pressure mapping classification was the first to use statistical

means to describe trends in synoptic climatology. Later on, Kalkstein (1979) would

use a principal component analysis (PCA) on the 28 variables used to describe the

daily weather at an observing weather station: seven atmospheric variables (air

temperature, dew-point temperature, sea-level pressure, visibility, cloud cover and

both horizontal wind components) at four different times of record. These were used

to determine PCA scores (i.e. the proportion of total population variance due to the

k-th principal component) specific to the synoptic class present at the ground

station. This is referred to as the Temporal Synoptic Index (TSI) (Kalkstein et al.,

1987) and became the basis for the current classification system, developed in 2002,

that is employed in this study.

The Spatial Synoptic Classification system (SSC) (Kalkstein et al., 1996b;

Sheridan, 2002) is an air mass typing system that uses ground-based measurements

of atmospheric variables (listed above for TSI) to determine the general makeup of

an air mass at a particular observing station. It is a hybrid method, combining the

manual classification methods of Bergeron (1930) with the automated methods of

Lund (1963) and Kalkstein (1979). First, manual determination of weather types is

completed to develop seed days, or characteristic days of each weather type for one

location. Using equal-weighted z-scoring, those seed days are applied to each day in

the weather record to classify years worth of weather data (most stations have SSC

data dating back to 1948). The seven SSC weather types are: dry polar (DP), dry

moderate (DM), dry tropical (DT), moist polar (MP), moist moderate (MM), moist

7


tropical (MT) and transitional (TR). The first SSC system (SSC1) by Kalkstein

et al. (1996b) accounted only for days in the winter and summer seasons, where

weather type characteristics had little variation. Sheridan (2002) introduced a

“sliding seed day” approach to account for the larger spread in normals contained in

the spring and autumn seasons. Hence, the SSC2 (hereby denoted SSC) was a

year-round classification system. Further detail on the development of the SSC can

be found in Section 2.2 of this document.

The SSC has been successfully used in studies analyzing the effect of synoptic

conditions on atmospheric constituents (Rainham et al., 2005; Hondula et al., 2010),

human health, namely heat-stress related mortality (Kalkstein et al., 2008; Sheridan

and Kalkstein, 2010; Hayhoe et al., 2010; Metzger et al., 2010), influenza (Davis

et al., 2012) and other respiratory ailments (Hondula et al., 2013), atmospheric

teleconnections (Sheridan, 2003; Knight et al., 2008), effects of the urban heat

island (Sheridan et al., 2000; Brazel et al., 2007), climate change (Kalkstein et al.,

1990; Knight et al., 2008; Vanos and Cakmak, 2014) and overall air mass frequency

shifts (Kalkstein et al., 1998). There has been an increased focus in recent years on

the identification of particularly harmful and oppressive weather types to human

health. In the spring and summer, studies have concluded that these oppressive

weather types are comprised of DT and MT+ (Sheridan and Kalkstein, 2004).

Vanos et al. (2014) assessed the risk for cardiovascular and respiratory mortality due

to air pollution and SSC type in ten Canadian cities, finding that tropical weather

type days lead to a nearly 10%, statistically significant, increase in the relative risk

of mortality in the spring and summers seasons. When this weather effect is

combined with air pollution, specifically carbon monoxide and nitrous oxide, higher

risks of respiratory mortality are present in the springtime.

Warm season (June-July-August) oppressive air mass relationships with

8


mortality have also been addressed regarding climate change. Kalkstein and Greene

(1997) projected future air mass types with three general circulation models (GCMs)

and estimated the increase or decrease of mortality in a changing climate. With a

focus on three cities (New York, Los Angeles and Chicago), Kalkstein and Greene

(1997) projected large increases in mortality risk coincided with MT+ weather types

in the future, specifically the years of 2020 and 2050, in Chicago and New York

City. Similar results for DT weather types were found in Los Angeles. Similar, more

in-depth work was undertaken by Sheridan et al. (2012b,a) for California after

development of an advanced six-step approach for classifying synoptic regimes in

future climate models by Lee and Sheridan (2012). Sheridan et al. (2012b,a)’s study

used two separate climate models, forced by three separate International Panel on

Climate Change (IPCC) scenarios (A1F1 - “higher emissions”, A2 - “mid-high

emissions” and B1 - “lower emissions”), to study how SSC weather type frequency

may change in the future in California. Their findings are considered extreme when

compared to present-day conditions, as the oppressive weather types of DT and MT

are projected to occur more frequently at the expense of the polar weather types,

which almost disappear, as well as moderate weather types. There is also variability

in determining which oppressive type becomes most prevalent, with DT conditions

becoming more frequent inland while MT weather types take a more permanent

hold near the coast in the future Sheridan et al. (2012b).

Cold-season (December-January-Feburary) SSC studies have also been

completed. Kalkstein and DeFelice (2014) examined the relationship between

wintertime hospital admissions and weather type for four cities in the southwest

United States. They found a statistically significant increase in the number of

admissions after the presence of a DP weather type, which brought dry, cold and

dusty conditions into the area. This results lent credence to previous work that

9


postulated a connection between dry, cold conditions and the presence of influenza

(Kalkstein, 2013).

Because of the number of studies already completed using the SSC as a basis, it

is important to attempt to also explain modification in terms of the SSC for ease in

crossover and collaborative studies. Moreover, the use of the SSC, or any other

automated synoptic classification, is important as its categories are reproducible.

Once categories and individual characteristics of those categories are set, dataset

origins become fixed and meteorological parameters produce the same results

(Yarnal, 1993) In addition, synoptic climatology’s goal is to link the atmospheric

circulation to the surface environment according to Yarnal (1993)’s working

definition of the area of science, which coincides with this study’s goal of examining

how land-atmospheric interactions change controlled synoptic weather types,

1.4 Overview and Applications of this study

The goal of this project is to develop the methodology to create an automated

model that incorporates surface variables along air parcel trajectory paths to

forecast SSC weather types which may then be used for biometeorological study

application with a specific focus on which variables will factor most into air mass

modification. The study focuses on the oppressive air mass types of DT and

MT/MT+. A series of warm-season (May through September), 96-hour back

trajectories are compiled from five different target locations spread across the

middle and eastern United States. Those target locations include Wilmington,

Delaware, Lexington, Kentucky, Raleigh-Durham, North Carolina, Huntsville,

Alabama and Oklahoma City, Oklahoma. Trajectories are clustered to determine

source region patterns. From there, values of evapotranspiration (ET) are taken at

the location of each twelve-hour trajectory point as a proxy to determine the

10


amount of moisture intake (or dispersal) the air mass is undergoing. While air

masses also have their temperature characteristics modified, this was not

statistically investigated in this study due to the lack of highly temporal resolute sea

surface temperature data, as well as the known dependent relationship between

latitude and temperature. In addition to the evapotranspiration analysis, a case

study focusing on air mass modification into a DT weather type in Huntsville,

Alabama is performed. This demonstrates the impact of both the conditions along

the direct trajectory path and the encompassing environment impacting the

eventual SSC weather type via synoptic factors as it moves toward its target

location. This is completed by examining synoptic-scale conditions across North

America during the previous ninety-six hours for each event investigated.

As stated in the previous section, this undertaking is significant in the

advancement of many applications of applied synoptic climatology research. With

knowledge of the processes and factors that impact synoptic air mass modification,

a large number of past and current research can be expanded upon, including, for

example, Kalkstein and DeFelice (2014)’s investigation into the weather type

correlation with hospital admissions due to influenza. Heat-health warning systems

(HHWSs) are used operationally in cities across the nation (32 in the United States)

and around the world in nations such as Korea, China and across Europe (Kalkstein

et al., 1996a; Tan et al., 2004; Bower et al., 2007). Some HHWSs use the SSC as a

basis for the initialization of advisory, watch and warning announcements based on

empirical mortality relationships in DT and MT+ weather types. The

implementation of these systems have been shown to be associated with a reduction

in mortality with nominal cost (Ebi et al., 2004). Additionally, Vanos and Cakmak

(2014) found increasing warm, oppressive weather type frequency in some Canadian

cities which may lead to excess heat-related and pollution-related mortality. Further

11


discussion of applications can be found in Sections 4.1 and 4.2.

It is the author’s goal that the qualitative and quantitative predictive value of

weather types based on the analysis put forth in this research will aid in furthering

the understanding of previous and current biometeorological research and be

implemented and expanded upon in the future. Predicting weather type with the

current study’s methods and combining those predictions with new and previous

biometeorological results can help to improve the health and well-being of humans

across the globe. With notice many days in advance, rather than two as the current

SSC forecasting system is set for, of a particularly harmful atmospheric setup,

policy-makers will have the information readily available to inform their citizens of

protective measures to heed in the hopes of decreasing significant issues of

weather-related morbidity and mortality. While an argument can be made that this

can already deciphered through the analysis of traditional weather models, the

results of this project will provide a different lens and more information for

decision-makers to create solutions from. This applies to past and also future

research that may focus on emerging ailments including valley fever, mosquito

vectors, asthma or any other respiratory or cardiovascular illnesses that are

exasperated by atmospheric conditions. In the end, the ultimate goal of the this

study is to aid in the betterment of human health and livelihood.

12


CHAPTER 2

DATA AND METHODS

The methods developed in this study are meant to provide the foundation for a

number of future SSC weather-type research prediction and applicative studies. The

trial and error incurred throughout this research is explained in detail and

highlighted so that future scientists may use a set-upon methodology and insert

additional intricacies as needed. This is applicable whether the research deals with

weather type prediction or applications into biometeorology and human health,

agriculture, economy, etc.

2.1 Geographical and Seasonal Focus

This study focused on the warm season months of May through September

between the years 2008 and 2012. Data is constricted to the warm season as to not

have significant snowpacks affecting the soil moisture characteristics of the ground

along the path of the back trajectories computed, especially when they are present in

the northern United States and Canada during the winter months. The scope is also

confined to the warm season as the most useful SSC projects relating to the most

oppressive weather types affecting human health occur during the warm season.

This is a second reason to not focus on wintertime analysis. However, year-round

studies are warranted for expansion of this research. With this exploratory analysis,

a more limited climatology of five years is employed, based on that of Dayan (1986),

for the development of methods. Future studies making use of the current developed

methods can apply a more robust dataset merely to refine and confirm results.

Wilmington, Delaware (IGL), Lexington, Kentucky (LEX), Raleigh-Durham,

North Carolina (RDU), Huntsville, Alabama (HSV) and Oklahoma City, Oklahoma

13


(OKC) were chosen as target locations (Figure 2.1) for this study as they are east

enough to not be considerably affected by air masses coming directly off the Pacific

Ocean during a statistically likely west-to-east air flow pattern, a pattern that made

itself apparent in the trajectories compiled for this project.

Figure 2.1: Map of the five target locations chosen for study.

2.2 Spatial Synoptic Classification

The Spatial Synoptic Classification system (SSC), developed originally in the

1990s (Davis and Kalkstein, 1990a; Kalkstein et al., 1996b) and further improved

upon by Sheridan (2002), classifies weather types by surface observations of

meteorological variables at first-order National Climatic Data Center (NCDC) sites,

mainly airports. It has become the go-to asset when examining correlations between

synoptic climatology and a number of other biometeorological research foci

(Hondula et al., 2014).

Taken four times daily, the meteorological variables used in the SSC seeding

process include (Sheridan, 2002):

14


• air temperature (degrees Celsius)

• dew point depression (degrees Celsius)

• mean cloud cover (in tenths)

• mean sea level pressure (millibars)

• diurnal temperature range (degrees Celsius)

• diurnal dew point range (degrees Celsius)

Weather types are determined by using these variables to determine seed days.

Seed days are days in a meteorological recording site’s record that have

characteristics that exemplify a specific weather type. Typical characteristics of seed

days for each weather type are quantified by using a range of the meteorological

variables that comprise the SSC. Once ranges are specified, all days in the

meteorological recording station’s period of record for that time of year are gathered

and sorted by the criteria (range of meteorological variables) decided upon. Further

confirmation of the analysis is performed by comparing each day’s chosen weather

type with weather maps of that day to confirm its representativeness. If found to be

non-representative, the criteria is modified and the process is repeated.

Sheridan (2002)’s seed day selection differed from Kalkstein et al. (1996b) and

was done so in order to make the SSC a year-round classification system. Sheridan

(2002) introduced “sliding seed days” which involve the selection of seed days in

four two-week windows during each season of the year to correspond with the

hottest and coldest two weeks of the year as well as the midway points. The sliding

seed day method allows for identification with respect to change in the climate

system and the two-week windows represent a period of time where criteria will not

15


change during seasonal transition. This encapsulates the temporal-relative

component of the SSC.

Once seed days are selected, daily weather types for the period of record are

determined with the use of equal-weighted z-scoring. Error scores, or the amount of

discrepancy between seed day characteristics and the characteristics of the day

being analyzed, are determined. The weather type associated with the lowest error

score becomes that day’s designation.

In the SSC, there are six distinct weather types, with definitions specific to

North America, as follows (Sheridan, 2002):

• Dry Polar (DP): Cold, dry air with mainly cloudless skies. Air associated with

this weather type is normally advected from the north (Canada or Alaska) in

North America.

• Dry Moderate (DM): Mild, dry air that is either modified by mixture with

another air mass (i.e. Dry Polar with Dry/Moist Tropical) or, in a more

specific example, warmed and dried by sloping down off the Rocky Mountains

in a zonal flow setup.

• Dry Tropical (DT): Air mass that is associated with the hottest, driest

conditions. DT air masses typically originate from the desert southwest, but

may also come about due to violent, compressed downslope winds.

• Moist Polar (MP): Cool, cloudy and humid air mass that typically sees its

origin from the northern Atlantic or Pacific Oceans. Also may come about

when air overruns a front, commonly a warm front, or moves over a cool body

of water.

• Moist Moderate (MM): Humid air mass with normal temperatures. As with

16


DM air masses, MM types may form due to modification of a pre-existing air

mass.

• Moist Tropical (MT): Warmest and most humid air masses which may bring

about convective precipitation. Typically start off in the Gulf of Mexico or in

the tropical Atlantic or Pacific. If temperatures or humidity becomes

extremely high, this weather type may be broken up into the additional

classifications, Moist Tropical Plus (MT+) and Moist Tropical Double Plus

(MT++). MT+ conditions occur when morning and afternoon

apparent-temperature values are both above MT weather-type means for the

location and MT++ conditions are present when morning and afternoon

apparent-temperature values are both more than one standard deviation above

MT weather-type means for the location (Sheridan and Kalkstein, 2004).

A seventh weather type, Transitional (TR), represents a day where different air

masses are present on the same day, normally signifying a frontal passage. These

transitional seed days are based on three meteorological factors: diurnal dew point

range, diurnal sea level pressure range and diurnal wind shift (Sheridan, 2002).

Seed days and, by process, weather types are selected for each individual

weather station. Hence, the SSC is also a spatial-relative system. Once the process

is completed for one station, the procedure is then applied to the next closest

station, where its own sliding seed days are calculated, not required to be in the

same two-week window as the station before it. Seed days at the previous station

are compared with the characteristics of seed days at the new location and days of

different character are excluded. The SSC algorithm is then run again to determine

the character of each weather type’s seed day and meteorological variable ranges are

updated. The process is run again for the new criteria to select updated seed days.

17


More detailed step-by-step description of the SSC algorithm can be found in

Sheridan (2002). Hence, all weather types are based relative to each station and

each time of year, ensuring that quantified ranges of each meteorological variable

are not connected to merely one certain situation (Sheridan, 2002).

In this study, a focus is placed on investigating the dry tropical, moist tropical

(MT and MT+) and moist tropical plus (alone) weather types. They provide the

greatest risk to population health based on heat-stress and interactions with air

pollution and are predominantly prevalent during the warm season. Any instance of

the presence of these weather types at one of the study’s target locations shall

herein be called an “event”. An event is a single day of the resultant weather type

or the first day of a string of consecutive days of the same resultant weather type.

Currently, the SSC is only available at set weather stations. The process of

interpolating weather types into a gridded dataset is currently underway (Lee,

2014a,b), but is not yet completed. Hence, for this study, SSC type at each

trajectory point is characterized by the SSC-reporting station where it was found

nearest. This process is completed for trajectory points over land as well as those

over water, as it is assumed that coastal stations would be more or less

representative of conditions a bit further offshore.

For the purposes of the current study, air mass modification is defined as the

changing of a weather type from one SSC type to another.

SSC data was downloaded from Dr. Scott Sheridan’s website, hosted by Kent

State University. More information on the SSC and the datasets themselves can be

found at http://sheridan.geog.kent.edu/ssc.html.

18


2.3 Surface moisture

In order to quantify surface moisture and transport characteristics, the Global

Land Data Assimilation System (GLDAS-1), a NASA Goddard Space Flight Center

and NOAA National Center for Environmental Prediction (NCEP) collaboration, is

employed (Rodell et al., 2004). As part of the GLDAS-1, the Noah 2.7 Land Surface

Model from NCEP is also used. GLDAS data was obtained via the Mirador search

tool on the Goddard Earth Sciences Data and Information Services Center (GES

DISC) website (http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings). More

information on the goals and specifications of GLDAS can be found on NASA’s

website here: http://ldas.gsfc.nasa.gov/gldas/.

All variables are model-derived after integrating observation-based data as

forcing fields and assimilating them with Global Data Assimilation System (GDAS)

reanalysis data. The model calculates variables every fifteen minutes and output is

provided every three hours at 0.25◦ resolution. The outputted total

evapotranspiration is used as the surface moisture parameter for this study as

opposed to a true soil moisture measurement. This is because evapotranspiration

represents the connection between soil moisture and the air mass that lies above it,

which is the land-atmosphere interaction within the modification process that this

project is focused on (Lawrence et al., 2007).

For locations above dry land, evapotranspiration was obtained directly from the

GLDAS model output as described above. However, locations over water did not

have this information calculated by the dataset. Instead, an equation formulated by

Penman (1948) and later Priestley and Taylor (1972) to estimate the maximum

potential evaporation over saturated surfaces is used. Priestley and Taylor stated

and proved that the equation was valid over large bodies of water. Therefore, the

equation was used for points in the trajectory that were located over any body of

19


water where the GLDAS dataset did not have values. The Priestley-Taylor equation

is as follows:

ET = α(s

s+ γ)(Q∗ −G) (2.1)

where:

• ET is the evapotranspiration (mm OR kg m-2),

• α is the Priestley-Taylor parameter which, while having a seasonal variation,

can be set to 1.26 for summertime conditions and for any sized body of water

based on Priestley and Taylor (1972)’s model results,

• s is the slope of the saturation specific humidity-temperature curve (kPa C-1),

• γ is the specific heat (J kg-1 C-1) divided by the latent heat of vaporization (J

kg-1),

• Q* is the net radiation (W m-2),

• G is the surface heat flux (W m-2).

When the land is saturated, as it is over a body of water in the current

research, the surface heat flux is negligible, hence the equation becomes:

ET = α(s

s+ γ)(Q∗) (2.2)

which is used for determining evapotranspiration values over water. Values of

shortwave and longwave radiation were obtained from satellite data provided by the

Clouds and the Earth’s Radiant Energy System (CERES) project put together by

NASA’s Earth Observing System (EOS) (Wielicki et al., 1996). Specifically, data

for this research came from the project’s Terra-Aqua observatories.

20


Q*, the net radiation, was calculated from this data by the equation:

Q∗ = (SWdown + LWdown) − (SWup + LWup) (2.3)

where SW represents shortwave radiation and LW represents longwave

radiation in W m-2.

The saturation specific humidity-temperature curve, s is

temperature-dependent and is calculated as:

s =4098(0.6108e

17.27TT+237.3 )

(T + 237.3)2(2.4)

where T is temperature (degrees Celsius), hence a dataset of sea surface

temperatures (SSTs) were employed to calculate s. This SST data was obtained

from the NOAA Optimum Interpolation (OI) Version 2 Sea Surface Temperature

dataset (Reynolds and Smith, 1994; Reynolds et al., 2002). Monthly mean sea

surfaces temperatures are used in all calculations due to the coarse temporal

resolution of the dataset. The fact that SST varies little over this time and that the

over ET equation has little sensitivity to the SST value are added reasons for the

dataset’s usage.

2.4 Reanalysis, Back Trajectories and Clustering

To provide the wind and air pressure data for the period of interest, Eta Data

Assimilation System (EDAS) reanalysis data (Draxler and Rolph, 2006; Draxler and

Hess, 2010), provided by NCEP, is used. EDAS data provides 40-kilometer

horizontal, spatial resolution with variables calculated at twenty-eight different

vertical levels. These meteorological variables are outputted every three hours.

More information on EDAS reanalysis may be found at:

21


https://ready.arl.noaa.gov/edas40.php

Three-dimensional back trajectories are computed using Version 4 of the

NOAA Air Resources Laboratory’s (ARL) Hybrid Single Particle Lagrangian

Integrated Trajectory (HYSPLIT) Model with EDAS reanalysis data as its inputted

forcing. Trajectories are calculated for 96 hours from the 12z initialization at each

target location’s latitude and longitude, beginning at an elevation of 500 meters

above ground level, following the methodological decisions of Davis et al. (2010) and

Hondula et al. (2010). Personal justification for the use of 500 meters AGL as the

terminal elevation include it being high enough so surface roughness characteristics

on the mean flow become negligible, but at the same time, still being coupled with

the surface layer with the transport of heat and moisture in mind. Only complete

96-hour trajectories are included in the final analysis. More information on the

HYSPLIT model can be found at: http://ready.arl.noaa.gov/HYSPLIT.php

To determine synoptic regime commonalities for each weather type in each

target city of interest, a two-stage cluster analysis technique, first utilized by Davis

and Kalkstein (1990b) and later used by Hondula et al. (2010) and Davis et al.

(2010), is executed. While other clustering methods have been used in the past, the

two-pronged technique of Davis and Kalkstein (1990b) included both hierarchal and

non-hierarchal approaches as a way to correct for biases that only using one

approach may have produced. For example, different hierarchal techniques form

clusters based on different distances between objects. This will affect how many

clusters are settled upon at the end of processing (i.e., the difference between having

many evenly populated clusters or a large cluster with many outlier clusters around

it). The use of a non-hierarchal technique, in addition to the hierarchal linkage,

allows for objects to be arranged after previous placement into a group, providing

improved convergence and cohesion amongst objects.

22


Positional latitudes, longitudes and altitudes are given for each hour of each

back trajectory. Before the hierarchal clustering process occurs, both the latitudes

and longitudes are changed into distance values (kilometers away from the target

location) while each hour’s altitude is converted to the distance relative to the

target location’s initial 500-meter height above ground level. To ensure that each

latitude, longitude and altitude are weighted evenly before cluster analysis, every

variable in each hour is converted into z-score units. Z-scoring is a statistical process

of standardizing values around a normal distribution (mean of zero, standard

deviation of one). To accomplish this, the mean of each variable at each hour is

subtracted from the observation and then divided by the standard deviation. Hence,

before heading into clustering, there were 288 equally-weighted observations (3

dimensions × 96 hours).

The next step entails implementation of complete linkage, or hierarchal,

clustering. This clustering finds both natural groupings within the complete set of

trajectories as well as calculated seeds (or centroids) for the subsequent

non-hierarchal clustering process. The average linkage technique has proven to be

the most valid of the hierarchal techniques (Cunningham and Ogilvie, 1972;

Hawkins et al., 1982) as it produces large separation between clusters and small

variance between the objects inside each respective cluster. The number of linkage

clusters are determined by a MATLAB algorithm, which used an “inconsistency

coefficient” parameter to compare the number of adjacent branches in the total

cluster tree. A higher coefficient represents an association between the links of the

tree that join distinct clusters, while a low coefficient distinguishes more indistinct,

less coherent clusters and, hence, little association between links. Once the

hierarchal clusters are determined, the z-score means of each variable are calculated

and used as centroids, or starting seed values, for the second clustering step: the

23


non-hierarchal, k-means clustering (Arthur and Vassilvitskii, 2007). The seeds are

used as a convergence proxy and the subsequent k-means process continues until

trajectories end their unbounded shuffling and settle around some central point.

Stohl et al. (1998) noted that due to turbulent mixing and model

parameterizations, trajectories could actually deviate 20% away from the actual air

mass path. If the parcel experiences flow that cannot be properly resolved by the

HYSPLIT model, that deviation could increase up to 100% (Stohl et al., 2002). In

their work with combining HYSPLIT model results with in-situ measurements,

Fleming et al. (2012) and Freitag et al. (2014) found that using a high number of

back trajectories will increase the reliability of a dataset. This is because they will

fill up more of the air parcel trajectory volume that must be assumed due to the

errors discussed earlier by Stohl et al. (2002).

Uncertainty is commonly present in all types of scientific research and the

HYSPLIT is no different. The previous paragraph presents research quantifying said

uncertainty. However, many peer-reviewed papers have used single-event

trajectories from the HYSPLIT model, including Covert et al. (1996), Falkovich

et al. (2001) and Artuso et al. (2007), for example, for a number of different

applicative studies. Additionally, in speaking with a creator of the HYSPLIT model

through email (Draxler, personal communication), it was decided that conclusions

using single trajectories may still be valid if the dataset used is large enough. Given

these factors, the use of single trajectories in the case-study portion of this project’s

analysis, as well as the use of many single trajectories in the later

evapotranspiration study, are deemed suitable by the author.

**

Gathering of SSC weather types was completed using R 3.1.1 (The R

24


Foundation for Statistical Computing, 2014). Cluster analysis was completed using

MATLAB R2014a (8.3.0) (The Mathworks, Inc., 2014). All other analyses and data

collection were completed using Python provided by Enthought Canopy-1.1.0.1371.

A flowchart of main methods is found in Figure 2.2.

Figure 2.2: Main methodological flowchart

25


CHAPTER 3

RESULTS

3.1 Methodological Limitations

As part of the results of this project, methods that were tried but deemed not

useful or non-applicable are listed so that future researchers may not spend time

trying to make failed methodological concepts work or, at the very least, not use

those concepts in the same way as what has been shown to be not useful in prior

analyses. Two of the main methodological hinderances in the research included:

1. the commonly-used hierarchal/non-hierarchal, two-step clustering process

when attempting to group trajectories, and

2. the use of the calculated evapotranspiration (Eqn. 2) for trajectories that had

data points located over water.

In this section, methods will be discussed or re-discussed and reasoning will be

given for its failure to be a part of this research and potential future research.

3.1.1 Clustering

To determine the best inconsistency coefficient to apply in for hierarchal cluster

analysis, a loop is used in the MATLAB code to examine the total number of

clusters that each coefficient provided. That loop is calculated between the values of

0.5 and 1.2 at 0.01 increments. These results are found in Table 1 with repeated

values of the total number of clusters for a unique coefficient omitted. For the 15

scenarios that the study encompasses, an inconsistency coefficient of 1.2 yielded

only one cluster. However, lesser inconsistency coefficients provided a large range of

the total number of clusters for each scenario. For a majority of these scenarios, the

26


number of clusters yielded by the algorithm is comparable the number of individual

trajectories themselves.

For example, for the Lexington MT scenario (Figure 3.1), which contained 85

trajectories, the range of coefficients yielded total number of clusters of 76, 65, 63,

59 and finally 1, none of which are viable for further analysis. Although there is a

large spread in trajectory position for this scenario, those scenarios with clusters

that can be determined using a simple eyeball test also were not described well by

the algorithm’s output. The Oklahoma City MT scenario, depicted in Figure 3.2,

contains 70 individual trajectories. There are distinct northerly and southerly

trajectory groupings with minimal outliers in the dataset. However, as shown in

Table 1, the total cluster numbers provided by MATLAB are 82, 62, 51 and 1, once

again, not viable for further analysis.

Table 1: Number of clusters using different MATLAB Inconsistency Coefficients

(0.5-1.2). Repeated values omitted.

HSV IGL LEX RDU OKC

DT MT MTP DT MT MTP DT MT MTP DT MT MTP DT MT MTP37 83 29 11 70 14 14 76 24 31 53 23 45 82 3619 82 23 10 51 8 13 65 17 21 45 16 26 62 241 65 20 8 19 1 5 63 4 20 44 14 21 51 15

62 10 1 1 4 59 1 16 39 12 3 1 154 1 1 1 10 37 9 16 1 1 11

Pre-grouping trajectories by weather type may be a mitigating factor in the

effectiveness of clustering. Hondula et al. (2010) found that while each SSC type

had its own general flow pattern, there was still overlap between trajectory groups

of different SSC types, hence, knowing the SSC type at the trajectory’s terminal

location was not enough to know the source region of any individual parcel on a

27


Figure 3.1: Map of Lexington, KY MT trajectories

daily time-scale. Hondula et al. (2010)’s study had a focus on a full back-trajectory

climatology with SSC typing of the trajectories being a complimentary component.

They even stated that they expected the overlap that they revealed in their

analysis. Due to the overlap, they reclassified trajectories, a step that was not taken

in this study as the pre-grouping by SSC type was the primary focus of the study.

The argument that the datasets were not large enough for significant natural

groupings to be disseminated is one that can be made when explaining the

clustering algorithm’s seemingly poor performance. The largest of this study’s

datasets contains 105 trajectories (Huntsville MT and Raleigh-Durham MT) while

Davis et al. (2010) and Hondula et al. (2010) used ten years worth of trajectories to

28


Figure 3.2: Map of Oklahoma City, OK MT trajectories

compete their clusters using the same technique. Short-term climatological patterns

begin to become apparent over such a time period. There is less of a chance that

the current study’s relationships can represent any significant trend using only the

warm-season period over five years of record. However, the Oklahoma City MT

scenario discussed above presents two established trajectory patterns that appear

despite the limited amount of data (see Figure 3.2). Without knowing how many

trajectories are used, it is not unfathomable that someone may see a potential

climatology of air mass positions entering Oklahoma City. Due to this, strength is

lacking in the small sample size argument, although the technique has worked

satisfactorily in previous studies with more trajectories. Further analysis is needed

to explain the algorithm’s trouble with the current data.

29


3.1.2 Evapotranspiration Over Water

Values of maximum potential evapotranspiration (MPET) are found to be

anywhere between 10-1000+ kg m-2 for each 12-hour period (comparable to values

found in Borma et al. (2009) which investigated ET values over a floodplain in

Amazonia), which is intuitive as parcels of air over the ocean have a limitless water

supply to extract from. Such values relative to the land values from GLDAS-1 are

very high. GLDAS-1 values are, at most, on the order of 100 kg m-2. As a example of

this, sample data from each set of evapotranspiration data taken from text files can

be seen in Figure 3.3 for comparison. Negative values in Figure 3.3 can be ignored

as evaporative processes are not present due to the lack of solar radiation at night.

Figure 3.3. Sample evapotranspiration data from Huntsville DT trajectories. Theseventh column shows values taken directly from GLDAS-1 data. The eighth columnshows calculated values using the Priestley-Taylor equation.

Due to the fact that differences between land-based evapotranspiration and

30


MPET values can be as much as three orders of magnitude, statistical comparisons

between different trajectories are rendered meaningless as the direct, land-based

values become negligible and calculations are too heavily weighted on the MPET

values. Hence, calculated measurements of MPET were not used in this study

although they provided valuable insight into the controlling mechanisms of a large

body of water resulting in moist air masses on land.

***

The MATLAB clustering algorithm and process as well as the values of

evapotranspiration over water were only two of the methods that, although

hypothesized to provide critical information, were deemed unsuitable for analysis for

this study. These two hinderances and corresponding methods are listed here so

that future researchers addressing this topic are aware as they delve deeper into

these factors. If able to improve upon the thoughts presented here, they may create

a new methodology of their own that acknowledges the limitations if they choose to

attempt to include them in consequent studies.

3.2 Weather Type and Modification Frequency

The number of events for each of the fifteen location-weather type combinations

are listed in Table 2. Also shown are numbers for each modification scenario, listed

in column 1, based on the starting, 96-hour SSC weather type heading each column.

The main takeaway from the Table 2 is that air mass modification – examined using

the SSC – occurs more often than not, therefore, validating the need for

understanding the physical nature of these modifications within this research.

Modification of any air mass into a DT weather type is the most common change

when compared to MT and MT+ ending weather types. The highest percentage of

31


DT presence at both the start and end of a trajectory occurred in Lexington at

13.3% frequency. It is important to note that Lexington DT is by far the study’s

most limited dataset and that DT-to-DT presence at the other four target locations

was well under 10%. MT weather type presence at both the start and end of 96-hour

back trajectories was far more common than that of DT (19.8% at Wilmington

being the minimum and and 48.6% at Oklahoma City being the maximum), but

over the five target locations, modification still occurred more than half of the time.

MT/MT+ and DM are the most common starting SSC weather types across

the fifteen scenarios studied. The southern cities close to the coast (Huntsville and

Raleigh-Durham) each see 34% of their resulting DT-type associated air masses

start off in the moist tropical regime. This number nearly doubles for Oklahoma

City DT cases ( 64%), most likely caused by air from the Gulf of Mexico reaching

the city due to the climatological presence of anticyclones across the lower Midwest

and Ohio River Valley during the warm season (Harman, 1987). This allows for

southeasterly flow to develop from the Gulf into Oklahoma City. DM sources

represent a moderate percentage of events for all but the Oklahoma City cases. As

discussed in Section 2.1, DM weather types have no distinct source region, but are

rather normally modified themselves from a number of previous weather types. The

most common way for a DM weather type to become present – through west-to-east

flow descending off of the Rocky Mountains – would give credence to the fact that

DM is present ninety-six hours ahead of time for the events in this dataset that are

well off to the east in locations, such as Huntsville and Raleigh-Durham. This also

explains the relative lack of DM presence in the previous 96 hours of Oklahoma City

events given its relative vicinity to the lee of the Rockies.

32


Table

2:

Fre

quen

cyof

modifi

cati

onfo

rea

chSSC

wea

ther

typ

eat

each

loca

tion

.E

ndin

gSSC

wea

ther

typ

esan

d

loca

tion

are

list

edin

each

row

.Sta

rtin

gSSC

wea

ther

typ

ear

elist

edin

each

colu

mn.

(‘T

otal

’M

TP

valu

eis

asu

bse

tof

‘Tot

al’

MT

valu

e)(H

SV

-H

unts

ville

,IG

L-

Wilm

ingt

on,

LE

X-

Lex

ingt

on,

RD

U-

Ral

eigh

-Durh

am,

OK

C-

Okla

hom

a

Cit

y).

DM

DP

DT

MM

MP

MT

MT

PT

RN

AT

otal

HSV

DT

1432

.56%

24.

65%

24.

65%

613

.95%

00.

00%

1227

.291

%3

6.98

%1

2.33

%3

43M

T27

25.7

1%1

0.95

%5

4.76

%13

12.3

8%3

2.86

%32

30.4

8%9

8.57

%8

7.62

%7

105

MT

P2

5.41

%1

2.70

%2

5.41

%2

5.41

%0

0.00

%19

51.3

5%8

21.6

2%3

8.11

%0

37

IGL

DT

618

.75%

825

.00%

26.

25%

412

.50%

39.

38%

39.

38%

13.

13%

26.

25%

332

MT

2930

.21%

66.

25%

44.

17%

1717

.71%

44.

17%

1919

.79%

11.

04%

44.

17%

1296

MT

P5

16.6

7%0

0.00

%1

3.33

%2

6.67

%0

0.00

%14

46.6

7%4

13.3

3%2

6.67

%2

30

LE

XD

T4

26.6

7%1

6.67

%2

13.3

3%0

0.00

%3

20.0

0%3

20.0

0%0

0.00

%1

6.67

%1

15M

T15

17.6

5%11

12.9

4%2

2.35

%15

17.6

5%2

2.35

%27

31.7

6%3

3.53

%5

5.88

%5

85M

TP

421

.05%

15.

26%

210

.53%

210

.53%

00.

00%

736

.84%

15.

26%

15.

26%

119

RD

UD

T12

20.6

9%8

13.7

9%2

3.45

%5

8.62

%3

5.17

%15

25.8

6%5

8.62

%2

3.45

%6

58M

T11

10.4

8%11

10.4

8%5

4.76

%14

13.3

3%2

1.90

%32

30.4

8%10

9.52

%8

7.62

%12

105

MT

P5

10.4

2%3

6.25

%1

2.08

%6

12.5

0%0

0.00

%20

41.6

7%8

16.6

7%4

8.33

%1

48

OK

CD

T3

7.69

%1

2.56

%1

2.56

%3

7.69

%2

5.13

%17

43.5

9%8

20.5

1%2

5.13

%2

39M

T11

15.7

1%6

8.57

%3

4.29

%2

2.86

%2

2.86

%34

48.5

7%7

10.0

0%4

5.71

%1

70M

TP

13.

57%

00.

00%

517

.86%

27.

14%

00.

00%

1242

.86%

725

.00%

00.

00%

128

33


3.3 Case-Study: Huntsville, Alabama Dry Tropical (DT) Modification

In addition to the quantitative nature of this air mass modification study based

on evapotranspiration, a qualitative case-study is performed using one of the fifteen

city-weather type scenarios to determine if the synoptic environmental factors that

play an important role in the air mass’ modification. The goal of this case-study is

to examine the indirect factors outside of the direct path of the calculated trajectory

that impact the modification of the air mass encapsulated by the single trajectory.

As described in the frequency table at the beginning of this chapter, there were

forty-three instances of Huntsville, Alabama incurring a warm-season, DT weather

type event of one or more days during the period of study. Of these forty-three

instances, only two starting weather types made up more than 20% of modified air

masses based on weather typing: DM (32.56%, 14 instances) and MT/MT+

(34.88%, 15 instances) – refer to Table 2. It is deemed unlikely any patterns in

synoptic conditions would be valid given the limited dataset of the other starting

weather-type sub-scenarios. Thus, this case study focuses on the DM and MT

sub-scenarios alone. Archived daily synoptic weather maps made available by

NOAA’s Weather Prediction Center (WPC) are compared with HYSPLIT back

trajectories from each event and used to diagnose the synoptic setup during each

five-day event leading up to the presence of DT conditions in Huntsville.

3.3.1 MT-to-DT Modification

In Huntsville, there were fifteen instances of moist-tropical-to-dry-tropical

weather type modification, where moist tropical includes both MT and MT+

weather types. Certain synoptic patterns are apparent at points during the event’s

duration. Of these fifteen events, thirteen of them can be split into one of two main

patterns that emerged. The first of these patterns is characterized by a center of

34


high pressure either moving into or building up in the southeastern United States.

The second is distinguished by a frontal passage during the event, still leaving high

pressure in its wake, but with a weak pressure gradient associated with it. Only two

of the events were qualitatively classified as outliers and not addressed in this case

study.

3.3.1.1 Southeastern U.S. High Pressure Center

The first of these synoptic patterns is characterized by a high pressure center

taking hold in the general vicinity over the Southeastern United States by the end

of the four-day modification event, such as that displayed in Figure 3.4. This

synoptic setup takes place seven times in this dataset. The event depicted in Figure

3.4, which took place between the days of June 4-8, 2008, provides a textbook

example of the case. On Day 0 of the event (-96 hours), a low pressure system and

associated front is present over the midwestern United States, while a large high

pressure system is situated well off the east coast with only a portion of the closed

isobar present in this surface analysis. As the event progresses, the high pressure

system over the Atlantic retrogrades back towards the east coast of the United

States, eventually forming a 1020-millibar closed center that settles over the

Georgia/South Carolina border. This occurs on Day 4 of the event and brings

relatively calm conditions to the Huntsville area, providing conditions for

stagnation. This pattern is also seen during the MT-to-DT modification event of

July 19-23, 2010 (Figure 3.5). In this case, while a completely-closed-off high

pressure center is not present in the surface analysis presented, a large high pressure

system is present in the southeastern United States for the entire five-day period,

shifting its position daily, but always affecting the region.

A hypothesis can be formed from these sub-scenario results on how synoptic

35


conditions may dry out the air mass once it arrives in Huntsville. When a center of

high pressure is situated over the southeastern United States, the clockwise flow

around it brings air from the southern Texas and Mexico region into the southeast.

Winds from Huntsville rarely come from the southwest when DT conditions are

present (winds are commonly southerly or southeasterly on Day 4 at Huntsville),

but the wind direction at stations to Huntsville’s west normally have a

southwesterly component on Day 3 or 4. Therefore, advection of drier air from

common DT source regions (i.e. the desert Southwest and Mexico) into the

Huntsville region are postulated to be partially the reason for DT conditions at

Huntsville on Day 4 when air mass trajectories begin and travel through MT source

regions, as seen in Figures 3.4 and 3.5.

3.3.1.2 “No Man’s Land” High Pressure Presence After Frontal Passage

The second of these synoptic patterns found in the case-study for MT-to-DT

modification is classified as a “No Man’s Land” situation in Huntsville as high

pressure is present, but there is no substantial center or gradient of the measured

high pressure in the region based on the surface analysis. The high pressure is found

to frequently follow a cold frontal passage through the region which provides a clue

into how the region may dry out as the originally moist parcel of air makes its way

to the target location. This sub-scenario of the MT-to-DT modification occurred six

times out of fifteen within the five-year dataset at Huntsville.

Figure 3.6 shows an example of this situation spanning between August 2-6,

2008. At the beginning of the period, a low pressure system was located over the

Northeastern United States with its associated cold front stretching through the

Ohio River Valley and into the midwest. The front becomes stationary by Day 2

(August 4) and moves through the Southeastern United States on Day 3. High

36


pressure builds soon afterwards which is strong enough at mid- and upper levels to

push Tropical Storm Edouard (Brown et al., 2010), which was occurring and located

in the Gulf of Mexico at that time, towards Louisiana and eventually Texas. By

Day 4, centers of high pressure are found over western Canada as well as the

upper-midwestern United States. While a prototypical center was not found in the

southeastern United States, Huntsville’s pressure still read near 1020 millibars on

August 6, comparable to the center over the midwest at the time.

A similar event is depicted in Figure 3.7, which took place between the dates of

July 28-August 1, 2011. An initial stationary front located over the northern United

States began sliding southward as a cold front on Day 1 (July 29) of the event. This

continued until it was positioned along the southeastern seaboard at the end of the

period. A disorganized high pressure system was located behind the advancing front

which once again left Huntsville in this area of high pressure with no gradient

present.

The hypothesized subsequent drying-out of the Huntsville region in this

sub-scenario of the MT-to-DT modification is also part of the basis of synoptic

meteorology. Cold frontal passages typically bring drier air from the north along

with it. In the six events in this subset of the MT-to-DT modification data, a cold

front made its way through the Huntsville area at some point in or right before the

ninety-six hour event period. That synoptically-forced phenomenon, coupled with

stagnant conditions after the front’s passage through the region, not allowing for

new, moist air to advect into the area, is a simple hypothesis for partial reasoning

for the presence of DT conditions by Day 4 when MT air is modified to DT in

Huntsville.

***

37


While briefly touched upon and somewhat assumed above, it is important to

note that low wind speeds at Huntsville on Day 4 are consistently prevalent

throughout the case-study. In the thirteen cases described in the two sub-scenarios

of MT-to-DT modification discussed above, the highest wind speed present at

Huntsville on Day 4 was eight miles per hour, most of the cases having recorded

wind speeds between 3-5 miles per hour. These wind speeds are certainly light

enough for stagnation of air, effectively maintaining dry conditions once air of that

character enters the region (either by southwest flow or a frontal passage, as

exhibited).

3.3.2 DM-to-DT Modification

The DM-to-DT weather type modification story is very different from that of

MT-to-DT modification as a temperature change (rather than moisture) is the

variable of interest. In North America, temperature normally has a strong

latitudinal dependence. This train of thought is confirmed in the trajectories that

comprise the DM-to-DT sub-scenario. Of the fourteen events examined, nine have

air parcel trajectory paths that traverse into Huntsville from the north. Another

four of the events start at a latitude comparable to Huntsville’s, leaving only one

outlier that started at a position deep in the Gulf of Mexico. The outliers become

an MT after 12 hours and experiences an environment much like the one described

in Section 3.3.1.1.

Apart from modification relative to the MT-to-DT sub-scenario, where events

(advection and frontal activity) outside of the parcel’s path play a large part in the

modification, there is a synoptic story present in the DM-to-DT events. In 13 of 14

DM-to-DT modifications, air parcel trajectories follow the path around an

anticyclone present in the eastern half of the continent into the target location of

38


Huntsville. While the anticyclone is always present, air parcel trajectories have no

common source region. Within the dataset, air parcel trajectories begin at locations

such as British Columbia and Ontario, Canada, Washington, Minnesota, Iowa and

Wisconsin to the north, the Gulf of Mexico to the south and even Alabama itself.

The fourteenth case, what can be considered an outlier, involved 2008’s Hurricane

Kyle forcing an air parcel, well ahead of the center of the storm, into the target

around its vast low pressure system.

Examples of this synoptic pattern are shown in Figures 3.8 and 3.9. Figure 3.8

depicts an event which took place between May 2-6, 2008. On Day 0, a low-pressure

system is located over the midwestern United States while a high pressure system is

starting to stretch into the Rocky Mountain region of the United States. Over the

four-day period, the large North American anticyclone center moves into the

Midwest and eastward until covering the eastern third of the United States by Day

4. The air parcel follows along the northern and eastern side of the anticyclone on

its entire path to Huntsville from western Canada across the Rockies and Midwest.

The random assortment of source regions for DM-to-DT modification is

apparent when comparing the previous event to the August 15-19, 2008 event shown

in Figure 3.9. Widespread high pressure from the east coast to the Rocky

Mountains is present throughout the four-day period. A distinct anticyclone center

is not present until Day 4 when it takes hold over the Great Lakes, but weak

clockwise circulations are found throughout the event’s period. The air parcel

associated with this event makes a much shorter trip into Huntsville when compared

to the previous event. It starts in South Carolina and rides along the weak pressure

gradient at the lower periphery of the vast high-pressure system that is present

during the ninety-six hour period.

39


3.4 Effect of Evapotranspiration on Modification

To quantitatively describe the process of air mass modification, specifically in

the framework of the moisture content of an air parcel, evapotranspiration along

trajectory paths are examined. Once it was established that evapotranspiration over

bodies of water would unevenly weight results towards trajectories that traversed

water at some point during their journey to a target location (Section 3.1.2), a novel

method to compare evapotranspiration values was surmised. For each city and each

modification scenario, trajectories are split into two groups: a group denoted by

trajectories being over land and having evapotranspiration values throughout the

entire 96-hour period (a total of nine values along the path), and a group of

“partial” trajectories. For these partial trajectories, the evapotranspiration values

from hour 0 (the target location) and stepping back every twelve hours are used in

analysis. However, partial trajectories are denoted as being found over water at

some point in the five days. Evapotranspiration values (every twelve hours) up until

the point where the trajectory is over water are used for analysis. For example, for

the trajectory presented in Figure 3.6, evapotranspiration values from hour 0

through hour 48 are considered valid for analysis as the trajectory is over land for

these time steps before it moves over the Gulf of Mexico.

Based on the partial and full groupings, the simple hypothesis is formed: For

instances of air mass modification where the characteristics of a weather type’s

moisture parameter is switched (i.e. moist to dry or vice versa), there should be a

noticeable difference between the groups’ average evapotranspiration values. For

example, in a moist-to-dry modification, it is predicted that the partial trajectory

group has a much lower average evapotranspiration than the full trajectory group

due to the fact that the partial trajectory spent time over water previously where

the intake of moisture into the air parcel was much greater than an parcel that only

40


traveled over land.

Table 3.1 displays average evapotranspiration values for both full and partial

trajectories for each of the five target locations during MT-to-DT modification

scenarios. The final row of the table displays the five-city average of those average

evapotranspirations. In order to sufficiently dry out the air parcel to become a DT

weather type relative to each target location, one would expect the partial

trajectory average to be much lower than its full counterpart according to our

hypothesis. In this modification scenario, partial trajectory evapotranspiration

values are lower than their full trajectory counterparts in Huntsville (slightly),

Wilmington and Oklahoma City. Taking the average ET over the five cities, average

full trajectory evapotranspiration comes out to be 1.16 kg m-2 and partial trajectory

evapotranspiration is 1.07 kg m-2. While the partial group is lower by 0.09 kg m-2,

the difference between the two groups is two orders of magnitude lower than the

values themselves and not statistically significant. This result does not agree with

the hypothesis of a large difference between the two values.

Table 3.1. Average evapotranspiration (kg m-2) values for full and partial trajectoriesin MT-to-DT modification scenarios.City Full Avg. Amt. Partial Avg. Amt. P-value

Huntsville, AL 1.34 3 1.28 12 0.86Wilmington, DE 1.69 2 1.28 2 0.38Lexington, KY 0.75 2 1.05 1 –Oklahoma City, OK 0.89 3 0.60 22 0.39Raleigh-Durham, NC 1.13 7 1.15 13 0.91

Five-City Average 1.16 17 1.07 50 0.68

The opposite situation is examined with results displayed in Tables 3.2 and 3.3.

Both DT-to-MT and DM-to-MT modification scenarios are examined due to the

41


minimal amount of data contained in the former’s scenario. The results in both

analyses agree closer to the initial hypothesis stated, showing full trajectories to

have higher evapotranspiration values on average than their partial counterparts.

On a city-by-city basis, the only scenario that does not fit the trend is the Huntsville

DT-to-MT modification scenario. The five-city averages also agree with the given

hypothesis as the differences between the full and partial trajectory groups are 0.28

and 0.42 kg m-2 for DT-to-MT and DM-to-MT scenarios, respectively.

Table 3.2. Average evapotranspiration values (kg m-2) for full and partial trajectoriesin DT-to-MT modification scenarios.City Full Avg. Amt. Partial Avg. Amt. P-value

Huntsville, AL 1.24 3 1.38 2 0.77Wilmington, DE 1.43 2 0.79 2 0.61Lexington, KY 1.57 2 0.00 0 –Oklahoma City, OK 0.78 1 0.48 2 –Raleigh-Durham, NC 1.36 3 1.36 2 0.94

Five-City Average 1.28 12 1.00 8 0.15

Table 3.3. Average evapotranspiration values (kg m-2) for full and partial trajectoriesin DM-to-MT modification scenarios.City Full Avg. Amt. Partial Avg. Amt. P-value

Huntsville, AL 1.28 12 0.89 15 0.09Wilmington, DE 1.60 8 0.75 21 0.01**Lexington, KY 1.23 12 1.13 3 0.53Oklahoma City, OK 1.20 5 0.71 6 0.12Raleigh-Durham, NC 1.07 5 0.81 6 0.47

Five-City Average 1.28 42 0.86 51 0.01**

42


Additional evapotranspiration figures may be seen in the Appendix A of this

document.

43


Figure 3.4. Surface analyses from Day 0-4 of event taking place June 4-8, 2008. A:Day 0. B: Day 1. C: Day 2. D. Day 3: E. Day 4: Subfigure F shows the trajectoryinto Huntsville for the four-day event. (Source: NOAA WPC)

44


Figure 3.5: Same as Figure 3.4 but for July 19-23, 2010 event.

45


Figure 3.6: Same as Figure 3.4 but for August 2-6, 2008 event.

46


Figure 3.7: Same as Figure 3.4 but for July 28-August 1, 2011 event.

47


Figure 3.8: Same as Figure 3.4 but for May 2-6, 2008 event.

48


Figure 3.9: Same as Figure 3.4 but for August 15-19, 2008 event.

49


CHAPTER 4

DISCUSSION AND CONCLUSIONS

4.1 Synopsis of Results

This project is meant to be an establishment of the methods that can be used

in future air mass modification projects. The results show that the use of

evapotranspiration is not a strong determinant in air mass modification analyses as

hypothesized. A reason weak relationships are found is due to the relativity of the

Spatial Synoptic Classification system and its contrast with the absolute nature of

evapotranspiration. As stated in Sections 1.3 and 2.2, the SSC is a spatial- and

temporal-relative weather typing system that bears unique quantifying

characteristics for each reporting station at different times of the year.

Evapotranspiration, on the other hand, is dependent on three separate factors:

1. Temperature, which can be described as spatially- and temporally-relative,

but not on the scales of one weather station or the two-week stepping that is

used to develop the SSC. There is a latitudinal dependence and seasonality

encompassed in the variability of temperature, but over larger time-steps and

spatial areas than the SSC.

2. Radiation, which once again has a latitudinal dependence, but may also vary

daily based on cloud cover. It is a variable that also has a dependence on land

use and land cover which may change over time.

3. Soil moisture, which is dependent on precipitation which is a highly variable

process, not leading to any relativity.

The MT-to-DT hypothesis presented in Section 3.4 failed as there was not a

statistically significant difference between average ET values for the full and partial

50


trajectory groups. While DM-to-MT and DT-to-MT results given in Section 3.4

accurately describe and confirm the hypothesis stated in Section 3.4 on the surface,

less importance should be placed on results pertaining to dry-to-moist

modifications. Partial trajectories in these scenarios reach water and ingest a large

amount of moisture before returning to land where this average evapotranspiration

analysis begins. This is opposite of what was thought in the previous moist-to-dry

analysis where the evapotranspiration, or lack thereof, of a parcel moving from

water to land was quite important in the drying-out process of the air mass. With

this in mind, due to the lackluster results seen in moist-to-dry modification

scenarios as detailed in Section 3.4, it is stated that using evapotranspiration as a

quantitative measure of air mass modification is weak and inconclusive.

An alternate surface moisture parameter that could be considered is discussed

in the next section.

However, this project has shown that the relativity of the Spatial Synoptic

Classification system has both its advantages and disadvantages, the former having

been discussed primarily to this point with regards to its use in applicative studies.

The spatial and temporal relativity is a unique feature allows an individual station

to have certain criteria distinguish its classification. However, when comparing two

stations (i.e. the target location and the location ninety-six hours before arrival at

the target location), the distinguishable criteria can become lost in the translation.

For example, take an air mass in the month of June that begins in Bismarck, North

Dakota as a moist tropical weather type and traverses to Huntsville, Alabama in

ninety-six hours where it is classified as dry tropical. Based on classification, it is to

be expected that the parcel dried out as it made its way south. However, in June, a

Bismarck MT has a characteristic dewpoint of 62 degrees Fahrenheit while a

Huntsville DT has a characteristic dewpoint of 61 degrees Fahrenheit. In a relative

51


sense, the air mass is moist for its location in Bismarck while it is dry in Huntsville.

However, in the absolute sense, the change in the moisture characteristics of the

parcel is almost negligible which brings the use of the term “modification” into

question which, in turn, casts doubt on the SSC being the best option for weather

station comparison.

There is no question that to most accurately describe air mass modification,

both qualitative and quantitative methods must be undertaken. The atmosphere is

a chaotic mechanism and while scientists have been able to describe it adequately

with a set of equations, they are riddled with unrealistic assumptions, namely

isolating a parcel of air from the rest of the atmosphere and treating it separately, a

process exhibited by the output trajectories from the HYSPLIT model. From the

results of the case studies performed in this research, it is apparent that the

surrounding environment has a large impact on the final state of an air mass in

conjunction with the air along its direct path to the target location. Advection of

additional air masses along other paths during the time period of any situation that

is being examined should be expected given the fluid medium that is the Earth’s

atmosphere. For instance, in Figure 3.6, a cold front that pushed down from the

north is hypothesized to be the agent that dried out conditions in Huntsville.

However, this is not apparent from the given trajectory that moves north from the

Gulf of Mexico and shows no sign of being affected by the frontal passage. The

trajectory does not show the full advective transport into the Huntsville area, but

merely the transport along one individual streamline. Additionally, the HYSPLIT

model does not account for any thermodynamic properties, leaving radiation fluxes,

adiabatic processes and sources and sinks of moisture (all sources of modification)

to be determined through other means.

52


4.2 Future Work

There are many directions for future work in this research area. First and

foremost, finding a variable or set of variables to help quantitatively describe air

mass modification should be a focus. While evapotranspiration seemingly provides a

dead end in modification analysis, using a moisture parameter to describe air mass

modification should not be dismissed entirely. The Standardized Precipitation Index

(SPI) (McKee et al., 1993) calculates the probability of precipitation on many

different monthly time scales at different stations to provide a statistical

representation of precipitation deficits or surpluses. This can be used as a proxy for

soil moisture deficits or surpluses. In its calculation, the SPI is normalized which

allows for locations with differing climatological standards in precipitation to be

compared against each other. This provides a similar spatial- and temporal-relative

system akin to the SSC. The SPI has already been used to forecast drought and

heat waves with much success (Cancelliere et al., 2007; Mueller and Seneviratne,

2012) and may be used in the future as a way to predict modifications in SSC

weather type (Ford and Quiring, 2014b). The SPI was not originally used for this

study as it did not represent a proxy for the land-atmosphere interaction focus of

this project. Once the predictive variables are decided upon and confirmed through

studies, year-round prediction should be the main focus of future development.

The focus of this research consisted of warm-season studies as snowpacks

during the cold season, especially with trajectories reaching into Canada during the

winter, impacted ET analyses. Although outside of the project’s scope, this can be

resolved in a future project and is needed due to cold-related mortality. It is already

known that evapotranspiration is essentially negligible when there is snow on the

ground, but interactions between the ground and air become much more complex

once the snowpack begins to melt in spring. This is another reason against the use

53


of evapotranspiration as the predictand for air mass modification studies. Extension

of these methods to the cold season should not prove to be difficult once a method

for snow cover analysis and its interactions with the air above it is appropriately

handled.

In addition to the needs for future development of this research already stated,

progression in other facets of research in the field would be helpful to fully

implement the ideas put forth by this project.

1. Currently, the North America Soil Moisture Database (Quiring, 2014), housed

at Texas A&M University, provides historical soil moisture quantities for

stations across the United States, Canada and Mexico. Datasets of varying

periods of record at these sites are available for research based upon past

events. However, the system does not presently have real-time capabilities.

Improvements to the temporal acquisition of data as well as the addition of

new stations to enhance spatial coverage of the network would provide for a

more robust dataset to work with. This would help to provide a sufficient air

mass modification forecasting tool, not to mention how it may be used in

current forms of numerical weather prediction models in their surface

parameterizations.

2. In addition to the back trajectories output by the HYSPLIT model in this

research, the model also has capabilities to compute forward trajectories based

on a given input. Hence, the ever-sought-after challenge to create the best

forecasting model possible should continue to have an importance placed upon

it, especially for the search of better boundary layer turbulence

parameterizations to better investigate the movement of air masses with time,

so that five-day forecasts produced by the GFS, WRF or ECMWF models

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may be included in trajectory forecasting with desired confidence. HYSPLIT

capabilities and techniques to resolve boundary layer motions, something

touched upon by Stohl et al. (2002), should also be continued to be improved

upon.

3. In the end, a traditional SSC numerical weather prediction model, one with

the ability to produce SSC forecasts multiple days out for the United

States/North America, may be the greatest advancement that can be made in

applied synoptic meteorology. However, it would certainly be deemed a large

undertaking due to the amount of data that would need to be assimilated into

said model because of the relative nature of the SSC. If such progress was

made, there would be two methods of predicting SSC type: a dynamical

method and a statistical method. This is much like what is seen in traditional

weather prediction today as described in the introduction of this paper. There

are many other improvements to our numerical weather prediction models

currently being undertaken to allow them to produce better results in the

context of its current output, but the science and the data needed for the

improvements presented here are certainly available for synopticians and

modelers.

4.3 Implications and Applications

Once extension of this work in the development of an air mass modification

prediction process is completed, there will be many opportunities to apply the

results to better applied research already completed with the SSC. The SSC is used

in many studies within the field of biometeorology when determining relationships

between weather with mortality and morbidity while also being used to understand

the weather’s effects on human health.

55


The prediction of these oppressive air masses will become of greater importance

as society surges into a future with an ever-increasingly warming climate. According

to the International Panel on Climate Change’s (IPCC) Fifth Assessment released

in 2013 (Stocker et al., 2013), relative to the period between 1986 and 2005,

temperatures could increase by almost five degree Celsius on average across the

globe by the end of the 21st century. Knight et al. (2008) confirmed this trend in

the historical data with respect to the SSC, finding that MT air masses had

generally increased over a majority of the United States with no preference to

season. Vanos and Cakmak (2014) confirmed this to a greater extent. As a result, it

would be expected that moist tropical weather type frequencies, and to a lesser

extent, dry tropical weather type frequencies, will continue be on the rise as time

moves to the future. Kalkstein and Greene (1997) explored mortality relationships

in forty-four large United States cities before narrowing their discussion down to the

cities of Chicago, Illinois, New York, New York and Los Angeles California. Using

three different general circulation models, the pair found large increases in MT

frequency in Chicago and New York as well as significant increases in DT frequency

in Los Angeles as parts of their future 2020 and 2050 climates. As a general

conclusion, they state that during summer, hot and dry DT and very warm and

humid MT consistently appear as “high-risk”. The spatial presence across the

country differs greatly, hence varying which regions will be affected more by each air

mass. In the three cities selected, as well when totaling across the studied cities, the

team found that excess mortality during the average summer season could triple as

a result of climate change.

The team of Sheridan et al. (2012a,b) performed similar research, but focused

on the state of California, a state with different climate zones, each that will feel the

effects of climate change in unique ways. Using future climate projections, they

56


found that inland locales such as Fresno and Sacramento will experience more

frequent DT weather types while cities along the coast such as Miramar and El Toro

will see increases in MT weather types. Both situations are associated with an

uptick in oppressive air mass types and, coinciding with them, an uptick in

projected mortality due to heat stress. Vanos and Cakmak (2014) looked at the past

climate in 30 different Canadian weather stations, finding a summertime increase of

moist tropical air masses in the majority of stations across the country with an

upward trend that looked to continue increasing into the future. In addition to

heat-related stress, they also noted, along with research completed by Health

Canada (Seguin and Berry, 2008), that air pollution episodes will become more

severe and longer-lasting in a projected warmer climate, negatively impacting those

living in those regions if adaptive measures are not taken.

Heat-health warning systems have become more and more prevalent in urban

areas over the past two decades (Sheridan and Kalkstein, 2004; Michelozzi et al.,

2010) as major heat waves, such as those in the northeastern United States in 1993,

in Chicago in 1995 and across Europe in 2003, have proven to be disastrous in terms

of loss of human life. Kalkstein et al. (1996a) developed one such system for the city

of Philadelphia, Pennsylvania in 1995 based on the SSC’s predecessor, the Temporal

Synoptic Index (Kalkstein et al., 1987). Using MOS forecasts, the system was able

to predict the arrival of an oppressive air mass, which was considered to be dry

tropical or maritime tropical for the city of Philadelphia, 48 hours before it arrived.

The system used an algorithm to determine when a health watch, health alert or

health warning should be issued based on the prediction of TSI category type (for

watch and alert) and estimated mortality (for warning). Later research found that

between the years of 1995 and 1998, the Philadelphia Hot Weather-Health

Watch/Warning System (PWWS) saved an estimated 2.6 lives on average in those

57


age 65 or older, resulting in a $468 million net benefit for the city during that time

(Ebi et al., 2004). A similar system was set up in Phoenix, Arizona in 2002 (NOTE:

This SSC-based system has since been replaced). Kalkstein and Sheridan (2007)

surveyed residents of the area and gauged how they perceived the warnings put out

by the National Weather Service (NWS) office. Over 86% of respondents said that

they were aware that warnings or advisories were issued, yet only 49.7% said that

they changed their daily routine on days of issuance. If, based on the results of this

research, better air mass type forecasts were able to be issued with more advanced

notice, mortality and morbidity figures would be expected to decrease even further

as more time and preparation would be available to the public and policy-makers.

Cold-related illness relationships can also be predicted by the SSC. Kalkstein

(2013) confirmed a heightened mortality in winter for the entire United States when

compared to summer, especially in the southwestern United States. Some of his

later research highlighted the presence of influenza outbreaks in the wake of dry

polar air mass types moving into regions of the southwestern United States due to

the cold, dry and particularly dusty conditions (Kalkstein and DeFelice, 2014). The

relationship is not constricted to the Southwest. Davis et al. (2012) found

relationships between influenza and pneumonia mortality and dry and cold weather

conditions in New York City, however, relationships with the actual DP weather

type were not statistically significant. Yet, a signal between atmospheric conditions

and human health was once again found in the data.

Being able to predict SSC type will help out in each of these previous studies as

well as many others that show dependence on weather type. In short-term projects

such as heat-health warning systems and influenza mortality prevention, simple

predictive probability will allow for numerous lives to be saved in events that take

place over the course of a few days. However, much larger research questions may

58


also be explored when breaking down the factors behind air mass modification.

Numerous papers have investigated how SSC frequency will change under future

climate scenarios, many of them cited in this work. But there remains a literature

gap in what factors will cause these frequency shifts. In studying the processes that

modify air masses, one or multiple atmospheric variables may arise as being the

drivers of this modification. Then, those factors may be probed in future climate

scenario analyses to confirm previous results based solely on SSC-type

characteristics as originally laid out by Sheridan (2002).

This may also have potential to be applied to other areas of future research and

new method application:

• Climate change and Earth’s warming are already apparent. However, research

has shown that while statistically significant risks of heat-related mortality

have remained, adaptation to higher temperatures have decreased heat-related

mortality and mortality risk in recent years (Bobb et al., 2014). Ebi et al.

(2004) also found that long-term adaptation-favorited processes, such as

improved healthcare (as hospitalizations during extreme heat events

increased), were at least partially responsible for declines in mortality.

Questions remain as to how projected future temperature increase will affect

the adaptation to heat that has been found in current studies. Voorhees et al.

(2011) used the IPCC A1B emissions scenario to model future temperature

change (2048-2052) and heat-related mortalities (3,700-3,800 for all-cause,

3,500 for cardiovascular disease and 21,000-27,000 for non-accidental) with no

adaptation or mitigation strategies accounted for. Stone et al. (2014),

however, incorporated vegetation and albedo enhancement mitigation

techniques into their analysis, revealing an offset of heat-related mortality by

40-99%. Discussed in Section 4.1, previous work has already been done using

59


the Standardized Precipitation Index (SPI) to predict extreme heat events

(Mueller and Seneviratne, 2012). This work has also been done using a simple

soil moisture method (Ford and Quiring, 2014a). Work combining the SPI and

SSC to predict future extreme heat events, whether by maximum temperature

of percent of hot days, using the characteristic factors of modification into

oppressive air masses highlighted by the framework of this and subsequent

research, can be examined in the future (Ford and Quiring, 2014b).

• In addition to heat, drought is becoming an increasingly prevalent problem

facing the United States, specifically in the western and midwestern portions

of the country (Peterson et al., 2013; Kam et al., 2014), with years such as

2011 standing out in recent memory. The coupling of heat extremes with the

severe lack of precipitation will have affects on health in the short- and

long-term. Sources of drinking water may begin to become scarce if these

conditions exacerbate in the future. Also, food shortages can result from

future water shortages as crops will not be able to be watered and livestock

will not receive the nutrients needed to provide acceptable meat for sale. An

investigation into large-scale SSC and modification factors in previous severe

drought conditions may help to provide a clue on atmospheric factors to look

for on preceding seasonal or yearly timescales.

• Plant phenology is emerging as a significant topic in the biometeorology field,

including how climate change is affecting the timing of the beginning of

growing seasons as well as early-season cold snaps which may affect a crop for

the rest of the year. A climatological study of SSC type specific to these

events and, once again, examining the characteristics of modification leading

up to the climatological mean may provide farmers a tool to protect their

60


harvest and livelihood.

• In the field of microbiology, some microbes have been found to thrive in

certain temperature and moisture conditions. As they are so small, they are

able to be picked up and transported within an air mass to a new location.

This applicative study would include both a HYSPLIT component to find

where these microbes are traveling from as well an SSC study to see which

weather types harbor populations of whatever organism is begin studied

(San Francisco, 2014).

There are hundreds-to-thousands of applications that a completed modification

framework can lead to. In the end, it’s a project that will increase the ability for

humans to adapt to their living conditions, both in the present and in the future.

The implications of this research and the subsequent follow-up studies are

significant, having a hand in human health, policy-making, agriculture, culture,

customs, society and human livelihood as a whole.

4.4 Final Conclusions

This project’s main findings can be summarized by three main points: the

inability of evapotranspiration to become the predictive variable in dealing with air

mass modification, the distinct disadvantage of using the SSC to describe the

characteristics of an air mass on an extended journey and the physics that the

HYSPLIT model masks or does not take into account. Overall, the goal of this

project was not achieved, but important takeaways from its failures were deduced

and discussed in Section 4.1.

With the realization that the relativity of the SSC may actually be hinderance

in modification studies, a greater importance should be placed on the realization of

modification in numerical weather prediction models to more accurately predict

61


meteorological variables and, in turn, SSC weather types more than a couple of days

in advance. If air mass modification must be disseminated with an absolute point of

view in order to calculate and predict SSC weather types days in advance, the

smaller-scale parameterizations within numerical weather prediction models must

continue to improve. This project examined air mass modification from a

synoptic-scale point of view while most of the fluxes, whether it be radiative and

moisture, work on the scale of the boundary layer or smaller.

To sum up, air mass modification occurs in our atmosphere, however,

attempting to quantify it is a complex problem. Additionally, use of the spatially-

and temporally-relative SSC to compare one air mass as it moves between two

different locations provides its own challenges, even though the classification system

is used in many meteorological contexts. The integration of the use of the

HYSPLIT model along with numerical weather prediction models may provide for

better modification or, at the very least, SSC weather type prediction, which has

been deemed important for knowledge in biometeorological applications. With this

in mind, broadening the spectrum of the weather scales at which researchers and

forecasters examine in the attempt to detect air mass modification is warranted.

62


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APPENDIX A

A.1 Finding closest station to a given latitude/longitude

import math

import numpy as np

import glob

def distance(origin, destination):

lat1, lon1 = origin

lat2, lon2 = destination

radius = 6371 # km

dlat = math.radians(lat2-lat1)

dlon = math.radians(lon2-lon1)

a = math.sin(dlat/2) * math.sin(dlat/2) +

math.cos(math.radians(lat1)) \

* math.cos(math.radians(lat2)) *

math.sin(dlon/2) * math.sin(dlon/2)

c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))

d = radius * c

return d

city = [’LEX’,’HSV’,’IGL’,’RDU’,’OKC’]

mass = [’DT’,’MT’,’MTP’]

sfilpath = ’/Volumes/PassportEHD/’

sfilname = ’SSCStations2013LatLon.txt’

sfildir = sfilpath+sfilname

IDs = np.genfromtxt(sfildir,skip_header=1,usecols=(0),dtype=’S3’)

sdata = np.genfromtxt(sfildir,skip_header=1,usecols=(1,2))

slats = sdata[:,0]

slons = sdata[:,1]

stations = []

for k in range(len(slats)):

stations.append([slats[k],slons[k]])

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HELPFUL CODE


for i in city:

for j in mass:

h = open(’/Volumes/PassportEHD/

closeststation’+i+’_’+j+’.txt’, ’a’)

pfilpath = ’/Volumes/PassportEHD/’+i+’_Files/’+j+’/’

pfilname = ’listall’+i+’_’+j+’.txt’

pfildir = pfilpath+pfilname

pdata = np.genfromtxt(pfildir,usecols=(0,1,2,4,5,6))

year = pdata[:,0]

month = pdata[:,1]

day = pdata[:,2]

hour = pdata[:,3]

plats = pdata[:,4]

plons = pdata[:,5]

points = []

closest = []

for l in range(len(plats)):

points.append([plats[l],plons[l]])

count = 0

for m in points:

a = []

for n in stations:

dist = distance(m,n)

a = np.append(a,dist)

b = (a == np.min(a))

statID = np.array(IDs)

closest = statID[b]

h.write(’’+str(year[count])+’\t’+str(month[count])+’\t’+

str(day[count])+’\t’+str(hour[count])+’\t’+

str(closest[0])+’\n’)

count = count+1

del a

del b

A.2 Mapping trajectories based on SSC type

import numpy as np

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import matplotlib.pyplot as plt

from mpl_toolkits.basemap import Basemap

city = [’OKC’]

mass = [’MT’]

for a in city:

for b in mass:

aa = ’/Volumes/PassportEHD/’+a+’_Files/’

+b+’/latsbyhour’+a+’_’+b+’.txt’

bb = ’/Volumes/PassportEHD/’+a+’_Files/’

+b+’/lonssbyhour’+a+’_’+b+’.txt’

cc = ’/Volumes/PassportEHD/closeststation’

+a+’_’+b+’withSSCDateAdj.txt’

LAT = np.genfromtxt(aa)

LON = np.genfromtxt(bb)

SSCtype = np.genfromtxt(cc, usecols=(5))

length = LAT.shape[0]

print LAT[2,44]

print length

if a == ’HSV’:

statlat = 34.38

statlon = -86.46

elif a == ’IGL’:

statlat = 39.40

statlon = -75.36

elif a == ’LEX’:

statlat = 38.02

statlon = -84.36

elif a == ’RDU’:

statlat = 35.52

statlon = -78.49

elif a == ’OKC’:

statlat = 35.23

statlon = -97.36

# Make Mercator Projection map

m = Basemap(llcrnrlon=-130.,llcrnrlat=15.,urcrnrlon=-60.

,urcrnrlat=60.,

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projection=’merc’,resolution =’c’)

statX, statY = m(statlon,statlat)

X, Y = m(LON,LAT)

c = -1 # For certain trajectory, c = (9*d)-1

m.drawcoastlines()

m.drawcountries()

m.drawstates()

m.drawmapboundary()

m.fillcontinents(color=’white’,lake_color=’white’)

m.plot(statX,statY,’s’,linewidth=2)

for d in range(0,length,1):

for e in range(0,96,12):

c = c+1

if e < 96:

if SSCtype[c] == 1:

plt.plot(X[d,e:e+11],Y[d,e:e+11],

color=’#FFA500’)

plt.plot(X[d,e],Y[d,e],’k’,linewidth=0.01)

elif SSCtype[c] == 2:

m.plot(X[d,e:e+11],Y[d,e:e+11])

m.plot(X[d,e],Y[d,e],’k’,linewidth=0.01)


m.plot(X[d,e:e+11],Y[d,e:e+11],’r’)



m.plot(X[d,e:e+11],Y[d,e:e+11],’c’)



m.plot(X[d,e:e+11],Y[d,e:e+11],’b’)



m.plot(X[d,e:e+11],Y[d,e:e+11],’g’)



m.plot(X[d,e:e+11],Y[d,e:e+11],

color=’#006400’)



m.plot(X[d,e:e+11],Y[d,e:e+11],

color=’#006400’)



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m.plot(X[d,e:e+11],Y[d,e:e+11],’k’)


else:

m.plot(X[d,e:e+11],Y[d,e:e+11],’k:’)


elif e == 96:

if SSCtype[c] == 1:

m.plot(X[d,e],Y[d,e],color=’#FFA500’)


m.plot(X[d,e],Y[d,e],’y’)


m.plot(X[d,e],Y[d,e],’r’)


m.plot(X[d,e],Y[d,e],’c’)


m.plot(X[d,e],Y[d,e],’b’)


m.plot(X[d,e],Y[d,e],’g’)


m.plot(X[d,e],Y[d,e],color=’#006400’)


m.plot(X[d,e],Y[d,e],color=’#006400’)


m.plot(X[d,e],Y[d,e],’k’)

else:

continue

plt.title(’’+a+’ ’+b+’ Trajectories’)

plt.show()

Additional code, including a script to run the HYSPLIT model as well as otherdata manipulation framework, may be made available upon request.

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APPENDIX B

Figure B.1. Average evapotranspiration (kg/m2) values for each modification scenarioof the Huntsville, AL DT dataset. Full and partial designations are described inSection 3.4

Figure B.2: Same as Figure 4.1 but for Huntsville, AL MT dataset

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MISCELLANEOUS FIGURES


Figure B.3: Same as Figure 4.1 but for Wilmington, DE DT dataset

Figure B.4: Same as Figure 4.1 but for Wilmington, DE MT dataset

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Figure B.5: Same as Figure 4.1 but for Lexington, KY DT dataset

Figure B.6: Same as Figure 4.1 but for Lexington, KY MT dataset

78


Figure B.7: Same as Figure 4.1 but for Raleigh-Durham, NC DT dataset

Figure B.8: Same as Figure 4.1 but for Raleigh-Durham, NC MT dataset

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Figure B.9: Same as Figure 4.1 but for Oklahoma City, OK DT dataset

Figure B.10: Same as Figure 4.1 but for Oklahoma City, OK MT dataset

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Figure B.11. Same as Figure 4.1 but for a five-city average of DT-resultant modifiedweather types

Figure B.12. Same as Figure 4.1 but for a five-city average of MT-resultant modifiedweather type

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Copyright 2015, Daniel J. Vecellio