copyright 2015, daniel j. vecellio
TRANSCRIPT
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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Texas Tech University, Daniel J. Vecellio, May, 2015
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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Texas Tech University, Daniel J. Vecellio, May, 2015
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.
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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Texas Tech University, Daniel J. Vecellio, May, 2015
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
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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.
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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
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(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):
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• 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
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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
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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.
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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.
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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
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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.
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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:
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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.
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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
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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
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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
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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
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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
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Texas Tech University, Daniel J. Vecellio, May, 2015
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
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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.
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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
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Texas Tech University, Daniel J. Vecellio, May, 2015
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
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Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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
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Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
Additional evapotranspiration figures may be seen in the Appendix A of this
document.
43
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
Figure 3.5: Same as Figure 3.4 but for July 19-23, 2010 event.
45
Texas Tech University, Daniel J. Vecellio, May, 2015
Figure 3.6: Same as Figure 3.4 but for August 2-6, 2008 event.
46
Texas Tech University, Daniel J. Vecellio, May, 2015
Figure 3.7: Same as Figure 3.4 but for July 28-August 1, 2011 event.
47
Texas Tech University, Daniel J. Vecellio, May, 2015
Figure 3.8: Same as Figure 3.4 but for May 2-6, 2008 event.
48
Texas Tech University, Daniel J. Vecellio, May, 2015
Figure 3.9: Same as Figure 3.4 but for August 15-19, 2008 event.
49
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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.
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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
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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.
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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
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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
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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
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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
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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
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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
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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.
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Metzger, K. B., K. Ito, and T. D. Matte, 2010: Summer heat and mortality in NewYork City: how hot is too hot? Environmental Health Perspectives , 80–86.
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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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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)
elif SSCtype[c] == 3:
m.plot(X[d,e:e+11],Y[d,e:e+11],’r’)
m.plot(X[d,e],Y[d,e],’k’,linewidth=0.01)
elif SSCtype[c] == 4:
m.plot(X[d,e:e+11],Y[d,e:e+11],’c’)
m.plot(X[d,e],Y[d,e],’k’,linewidth=0.01)
elif SSCtype[c] == 5:
m.plot(X[d,e:e+11],Y[d,e:e+11],’b’)
m.plot(X[d,e],Y[d,e],’k’,linewidth=0.01)
elif SSCtype[c] == 6:
m.plot(X[d,e:e+11],Y[d,e:e+11],’g’)
m.plot(X[d,e],Y[d,e],’k’,linewidth=0.01)
elif SSCtype[c] == 66:
m.plot(X[d,e:e+11],Y[d,e:e+11],
color=’#006400’)
m.plot(X[d,e],Y[d,e],’k’,linewidth=0.01)
elif SSCtype[c] == 67:
m.plot(X[d,e:e+11],Y[d,e:e+11],
color=’#006400’)
m.plot(X[d,e],Y[d,e],’k’,linewidth=0.01)
elif SSCtype[c] == 7:
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Texas Tech University, Daniel J. Vecellio, May, 2015
m.plot(X[d,e:e+11],Y[d,e:e+11],’k’)
m.plot(X[d,e],Y[d,e],’k’,linewidth=0.01)
else:
m.plot(X[d,e:e+11],Y[d,e:e+11],’k:’)
m.plot(X[d,e],Y[d,e],’k’,linewidth=0.01)
elif e == 96:
if SSCtype[c] == 1:
m.plot(X[d,e],Y[d,e],color=’#FFA500’)
elif SSCtype[c] == 2:
m.plot(X[d,e],Y[d,e],’y’)
elif SSCtype[c] == 3:
m.plot(X[d,e],Y[d,e],’r’)
elif SSCtype[c] == 4:
m.plot(X[d,e],Y[d,e],’c’)
elif SSCtype[c] == 5:
m.plot(X[d,e],Y[d,e],’b’)
elif SSCtype[c] == 6:
m.plot(X[d,e],Y[d,e],’g’)
elif SSCtype[c] == 66:
m.plot(X[d,e],Y[d,e],color=’#006400’)
elif SSCtype[c] == 67:
m.plot(X[d,e],Y[d,e],color=’#006400’)
elif SSCtype[c] == 7:
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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Texas Tech University, Daniel J. Vecellio, May, 2015
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
Texas Tech University, Daniel J. Vecellio, May, 2015
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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Texas Tech University, Daniel J. Vecellio, May, 2015
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
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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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Texas Tech University, Daniel J. Vecellio, May, 2015
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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Texas Tech University, Daniel J. Vecellio, May, 2015
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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