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An Investigation of Dust Aerosols and Atmospheric Profiles Associated with North
Atlantic Hurricanes Using Multi-Sensor Measurements
A dissertation submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy at George Mason University
by
Mohammed Mostafa Kamal
Master of Science
Howard University, 1994
Director: John J. Qu, Professor
Department of Geography and GeoInformation Science
Fall Semester 2012
George Mason University
Fairfax, VA
Copyright 2012 Mohammed Mostafa Kamal
All Rights Reserved
ii
DEDICATION
This is dedicated to my only brother Ziaur Rahman Patwary (Sumon) who departed on
January 27, 2011.
iii
ACKNOWLEDGEMENTS
My deep sense of appreciation goes to Dr. John Jianhe Qu, Professor of Department of
Geography and Geoinformation Science, and Founding Director of Environmental
Science and Technology Center (ESTC) and EastFIRE Lab, College of Science, George
Mason University, for his supervision, suggestions, encouragements and assistance
throughout the progress of this dissertation. His guidance and support, encouragement
and patience, challenges and inspirations, enabled me finally complete this dissertation.
I am particularly indebted to Dr. Ruixin Yang for his valuable suggestions in data
analysis. My special thanks to Dr. Kirk D. Borne for his guidance and sustaining support
for this work and I am also thankful to Dr. Jeng-Eng Lin for his direction during the
comprehensive examination. I am grateful to Dr. Daniel Carr to serve as my dissertation
committee member and for the opportunity to expand my knowledge through his classes
on the Statistical Visualization and Data Exploration.
I am grateful to all ESTC and EastFIRE Lab members, specially, Drs. Xianjun Hao and
Lingli Wang for their helpful insights in presenting my dissertation. I am also indebted to
Michael Dvoroznak, Jose Pena and Johanna Breder of Bureau of Engraving and Printing,
for their valuable time to review this dissertation. I am grateful to my previous employer,
Science Applications International Corporation (SAIC) for providing tuition support for
the first four years of my Ph.D. study.
I also acknowledge the MODIS, MISR and AIRS mission scientists and associated
NASA personnel for the production of the data used in this research effort. I am
appreciative to The HDF Group (http://www.hdfgroup.org) for posting code for
numerous commercial and non-commercial platforms of powerful data visualization and
analysis on HDF files.
Finally, I wish to extend my gratitude and special thanks to my better half Shameema
Rahman for her constant encouragement and support, and to my children Nazmee and
Nabhan Kamal for their patience during the course of this dissertation.
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TABLE OF CONTENTS
Page
List of Tables ................................................................................................................... viii List of Figures .................................................................................................................... ix List of Abbreviations ......................................................................................................... xi Abstract ............................................................................................................................. xii Chapter One ........................................................................................................................ 1 1. INTRODUCTION ....................................................................................................... 2 1.1 Why Study Dust Aerosols for Hurricanes .......................................................... 2 1.2 Statement of Problems ........................................................................................ 5 1.3 Objectives ........................................................................................................... 7 1.4 Organization of Dissertation ............................................................................... 9 1.5 Major Data Sources........................................................................................... 10 1.6 Principal Results ............................................................................................... 12 Chapter Two...................................................................................................................... 14 2. LITERTAURE REVIEWS ........................................................................................... 15 2.1 Aerosol Optical Thickness (AOT) and Atmospheric Profiles .......................... 15 2.2 SHIPS Parameters ............................................................................................. 20 2.3 Chapter Summary ............................................................................................. 21 Chapter Three.................................................................................................................... 23 3. THEORY AND DATA COLLECTIONS .................................................................... 24 3.1 Satellite Retrieved Aerosol Products ................................................................ 25 3.2 Satellite Retrieved Atmospheric Profiles .......................................................... 28 3.4 MODIS Aerosol Products ................................................................................. 32 3.5 MISRAerosolProducts ................................................................................... 34 3.6 AIRSAtmosphericProfileProducts ............................................................... 35 3.7 DataCollection ................................................................................................. 36 3.7.1 Dynamic‐Coordinate ................................................................................... 41 v
3.7.2 Static‐Coordinate ......................................................................................... 41 3.7.3 SHIPSDataCollection ................................................................................. 42 3.7.4 AIRS,MODISandMISRLevel2DataCollection ........................................ 44 3.8 ChapterSummary........................................................................................ 45 Chapter Four ..................................................................................................................... 47 4. OVERVIEW OF HURRICANES................................................................................. 48 4.1 Saharan Air Layer (SAL) .................................................................................. 48 4.2 African Easterly Wave (AEW) ......................................................................... 49 4.3 Hurricanes........................................................................................................ 50 4.4 ChapterSummary............................................................................................ 54 Chapter Five ...................................................................................................................... 55 5. METHODOLOGY ....................................................................................................... 56 5.1 ConcentricCircleswithLeftandRightside .............................................. 56 5.2 VariableReductionandConcentricCirclesSpatialCoordinate .............. 61 Chapter Six........................................................................................................................ 70 6. DATA PROCESSING AND ANALYSIS ................................................................... 71 6.1 MODISAerosolandAtmosphericProfilesRetrieval .................................... 71 6.2 AIRSAtmosphericProfilesRetrieval ............................................................. 74 6.3 MISRAerosolRetrieval ................................................................................... 76 6.4 Principal Component Analysis (PCA) .............................................................. 79 6.5 Lasso and Elastic Net Variable Shrinkage Methods ......................................... 79 Chapter Seven ................................................................................................................... 82 7. RESULTS AND DISCUSSIONS ................................................................................. 83 7.1 DynamicandStatic‐CoordinateAnalysisforFourHurricanes.................... 83 7.2 AOTDistributionfortheLeftandtheRightside .......................................... 93 7.3 AIRSTemperatureandRHDifference ........................................................... 99 7.4 VariableReductionAnalysisViaPCA .......................................................... 104 7.5 VariableReductionAnalysisViaLassoandElasticNet ............................. 115 7.6 MISRandMODISAOTComparison.............................................................. 120 Chapter Eight .................................................................................................................. 124 8. CONCLUSIONS AND FUTURE WORK .............................................................. 125 8.1 FutureWork................................................................................................... 130 vi
8.1.1 VaryingConcentricCirclesRadii.............................................................. 131 8.1.2 UsingCALIPSOforDustAerosolRetrieval .............................................. 131 References ....................................................................................................................... 133 vii
LIST OF TABLES
Table
Page
Table 1.1 MODIS, MISR, AIRS data products used and their specifications .................. 11 Table 1.2 Data product and sources .................................................................................. 12 Table 3. 1 SHIPS parameters ............................................................................................ 30
Table 3. 2 Location of hurricane Isabel, between September 07 and 18, 2003 ................ 36 Table 3.3 Location of hurricane Frances, August 25 & September 06, 2004................... 38 Table 3.4 Location of hurricane Katrina, August 23 & August 29, 2005......................... 39 Table 3.5 Location of hurricane Helene between September 12 and 24, 2006 ................ 40 Table 3.6 Twenty four hurricanes were selected between 2003 and 2009 based on the
category 3, 4, and 5 ........................................................................................................... 43 Table 3.7 MODIS aerosol retrievals ................................................................................. 44 Table 5. 1 Reductions of variables by PCA ...................................................................... 66
Table 5. 2 MLR results for uncorrelated aerosol and temperature variables .................... 67 Table 5. 3 Reductions of variables .................................................................................... 68 Table 6. 1 Parallel Computing Results for Lasso and Elastic Net Analysis ..................... 81
Table 7. 1 Average AOT values in dynamic and static coordinate’s presentation for four
hurricanes .......................................................................................................................... 84
Table 7. 2 AOT, RH and temperature (in ºK) on the left and right side of each hurricane
averaged for static-coordinate. .......................................................................................... 88 Table 7. 3 AOT, RH and temperature (in ºK) on the left and right side of each hurricane
averaged for dynamic-coordinate. .................................................................................... 90 Table 7. 4 Histogram (Lognormal distribution) analysis for four hurricanes ................... 98 Table 7. 5 Histogram (Lognormal distribution) analysis for several segments 3 and 5 for
all four hurricanes ............................................................................................................. 99 Table 7. 6 Regression equations for each hurricane based on Figure 7.14 ..................... 103 Table 7. 7 Principal components for Aerosol, Wind, Relative Humidity, Vertical Shear
and Temperature parameters ........................................................................................... 107 Table 7. 8 Response and Predictor variables’ R2, Adjusted R2, F value, RMSE and
Residual Error ................................................................................................................. 114 Table 7. 9 Original variables and the reduced variable sets based on Lasso and Elastic Net
shrinkage ......................................................................................................................... 118 Table 7. 10 Regression analysis based on the predictors resulted from Lasso and Elastic
Net shrinkage .................................................................................................................. 119 viii
LIST OF FIGURES
Figure
Page
Figure 4. 1 Temperature at 850 mb for hurricane Frances on 08/28/2004 based on AIRS
Level 2 standard data. ....................................................................................................... 52 Figure 4. 2 MODIS Terra Level 2 product showing AOT around the hurricane Isabel at
0.55 µm on 09/14/2003. .................................................................................................... 53 Figure 4. 3 MODIS Aqua Level 2 product showing AOT around the hurricane Isabel at
0.55 µm on 09/15/2003. .................................................................................................... 53 Figure 5. 1 Showing two concentric circles with two regions left and right .................... 57
Figure 5. 2 Concentric circles representing Right side with 10 segments starting from
0ºto 180º ............................................................................................................................ 60 Figure 5. 3 (a) MODIS data was collected within the annulus of the two concentric circles
with radii r1 and r2. (b) Data collection following the motion of a hurricane .................. 63 Figure 6. 1 Flow diagram for EODBO (AOT) retrieval from MODIS Level 2 data
products ............................................................................................................................. 72
Figure 6. 2 Flow diagram for TAirStd, H2OMMRStd and H2OMMTSat retrieval from
AIRS Level 2 data products .............................................................................................. 75 Figure 6. 3 Flow diagrams for RegBestEstimateSpectralOptDepth retrieval from MISR
Level 2 data products ........................................................................................................ 78 Figure 7. 1 AIRS Temperature at 500 (Red), 700 (Green) and 850 (Purple) mb and
MODIS AOT (Red triangles) at 0.55 μm on the peak day of each hurricane .................. 86
Figure 7. 2 AIRS RH at 500 (Red), 700 (Green) and 850 (Purple) mb and MODIS AOT
(Red triangles) at 0.55 μm on the peak day of each hurricane ......................................... 86 Figure 7. 3 MODIS dynamic and static AOT at 0.55 μm for the left side of each hurricane
........................................................................................................................................... 89 Figure 7. 4 MODIS dynamic and static AOT at 0.55 μm for the right side of each
hurricane ........................................................................................................................... 89 Figure 7. 5 MODIS AOT at 0.55 m and AIRS RH and temperature at 700 mb for left
and right side of hurricane Isabel (2003) showing dynamic and static conditions ........... 92 Figure 7. 6 Two concentric circles with regions left and right and further segmented 30°
each. .................................................................................................................................. 93 Figure 7. 7 Histograms and QQ plots for Segment V of Frances showing Static and
Dynamic scenarios ............................................................................................................ 94 Figure 7. 8 Histograms and QQ plots for Segment V of Helene showing Static and
Dynamic coordinate scenarios .......................................................................................... 95 ix
Figure 7. 9 Histograms and QQ plots for Left and Right side of Isabel showing Static and
Dynamic coordinate scenarios .......................................................................................... 95 Figure 7. 10 Histogram and QQ plot for Left and Right side of Helene showing Static and
Dynamic coordinate scenarios. ......................................................................................... 96 Figure 7. 11 Histogram and Lognormal distribution curve (Blue) for the hurricane Isabel
showing Left and Right side Static and Dynamic coordinate scenarios ........................... 97 Figure 7. 12 Histogram and Lognormal distribution curve (Blue) for the hurricane Helene
showing Left and Right side Static and Dynamic coordinate scenarios ........................... 97 Figure 7. 13 Temperature and RH profile difference for Isabel, Frances, Katrina and
Helene from the pressure levels 500 and 1000 mb showing “PRE” (Blue) and “POST”
(Green) hurricane scenario. ............................................................................................. 101 Figure 7. 14 Scatter plot MIODIS AOT at 0.55 m and AIRS RH and temperature at 700
mb for hurricane Isabel (2003), Frances (2004), Katrina (2005) and Helene (2006)
selecting only the static coordinate and combining Terra, Aqua as well as left and right
sides................................................................................................................................. 103 Figure 7. 15 Contribution factors on the MLR between the FD48 and Original set of 55
variables (VMAX was removed from the analysis) ....................................................... 108 Figure 7. 16 Contribution factors on the MLR between the FD48 and Predictor_1 which
has 31 variables ............................................................................................................... 109 Figure 7. 17 Contribution factors on the MLR between the FD48 and Predictor_6 which
has 20 variables ............................................................................................................... 110 Figure 7. 18 R2 values and RMSE at eight lead time positions 06, 12, 18, 24, 30, 36, 42
and 48 hours .................................................................................................................... 111 Figure 7. 19 Average Root Mean Square Error (RMSE) for each FD ........................... 112 Figure 7. 20 Residual plots for FD06 and FD48 ............................................................. 113 Figure 7. 21 Mean Square Error (MSE) vs. Lambda - Lasso plot for response variable
FD48 and Original predictor (56 variables). ................................................................... 116 Figure 7. 22 Mean Square Error (MSE) vs. Lambda - Elastic Net plot for response
variable FD48 and Original predictor (56 variables) and  = 0.5. ................................. 117 Figure 7. 23 Dynamic-Coordinate and Static-Coordinate average AOT for each hurricane
......................................................................................................................................... 122 Figure 7. 24 Representing aerosol dust plots for hurricane Isabel on 09/09/2003 acquired
from MODIS Aqua ......................................................................................................... 123 x
LIST OF ABBREVIATIONS
Aerosol Robotic Network ................................................................................... AERONET
African Easterly Waves ............................................................................................... AEW
Atmospheric Infrared Sounder ..................................................................................... AIRS
Aerosol Optical Depth .................................................................................................. AOD
Aerosol Optical Thickness ............................................................................................ AOT
Atmospheric Science Data Center .............................................................................. ASDC
Algorithm Theoretical Basis Documents .................................................................... ATBD
Advanced Very High Resolution Radiometer .........................................................AVHRR
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation .................... CALIPSO
Effective Optical Depth Best Ocean ........................................................................ EODBO
Earth Observing System ................................................................................................ EOS
Goddard Earth Sciences Data and Information Services Center ......................... GES DISC
Hierarchical Data Format ...............................................................................................HDF
Level 2 Data ...................................................................................................................... L2
Level 1 and Atmosphere Archive and Distribution System .................................... LAADS
Least Absolute Shrinkage and Selection Operator ................................................... LASSO
MODIS Characterization Support Team ....................................................................MCST
Multiangle Imaging Spectro Radiometer ..................................................................... MISR
Multiple Linear Regression........................................................................................... MLR
MODoderate resolution Imaging Spectroradiometer................................................MODIS
National Aeronautics and Space Administration ........................................................NASA
National Hurricane Center ............................................................................................ NHC
Near InfraRed.................................................................................................................. NIR
National Weather Service ............................................................................................. NWS
Principal Component Analysis ...................................................................................... PCA
Probability Distribution Function .................................................................................. PDF
Relative Humidity ............................................................................................................ RH
Root Mean Square Error .............................................................................................RMSE
Saharan Air Layer .......................................................................................................... SAL
Statistical Hurricane Intensity Prediction Scheme ......................................................SHIPS
Top of the Atmosphere ................................................................................................. TOA
Vertical Feature Mask ................................................................................................... VFM
xi
ABSTRACT
AN INVESTIGATION OF DUST AEROSOLS AND ATMOSPHERIC PROFILES
ASSOCIATED WITH NORTH ATLANTIC HURRICANES USING MULTI-SENSOR
MEASUREMENTS
Mohammed Mostafa Kamal, Ph.D.
George Mason University, 2012
Dissertation Director: Dr. John J. Qu
MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical
Thickness (AOT) and the Atmospheric Infrared Sounder (AIRS) atmospheric profiles
(temperature and moisture) Level 2 products are studied to investigate dust aerosol
impacts on hurricanes through spatial analysis for four hurricanes Isabel (2003), Frances
(2004), Katrina (2005) and Helene (2006). Spatial regions were selected based on two
concentric circles (an annulus) and segmented by left and right regions of 180° each
around the hurricane’s eye. Statistical relationships between MODIS AOT, AIRS
temperature and percent relative humidity (RH) among these four hurricanes is presented.
The variation of temperature and RH values represented in static–left, static–right,
dynamic–left and dynamic–right for the 500 mb, 700 mb and 850 mb pressure levels
were analyzed to determine the impacts of dust aerosols on temperature and RH. For the
four hurricanes’ average, AOT was found highest in Helene as opposed to (0.27) Isabel
xii
(0.23), Frances (0.18) and Katrina (0.16). Dust aerosols showed negative impacts on the
atmospheric moisture levels and positive on the temperature.
Additionally, a systematic multivariate analysis of MODIS aerosol retrievals over
the North Atlantic spanning 7 hurricane seasons combined with Statistical Hurricane
Intensity Prediction Scheme (SHIPS) parameters is presented. My focus is on
investigating the effects of 5 categories that include aerosol, wind, relative humidity,
shear, and temperature on hurricane intensities. The primary goal is to be able to explain
the same physical phenomena equally well by significantly reducing the number of
parameters employed in the statistical analysis. Five categories which include, wind,
aerosol, shear, relative humidity, and temperature components were established by
reducing 56 variables to 20. Aerosol, wind, humidity, shear, and temperature were all
contributing factors in the regression equation with the ranking for the contribution found
as (1) wind, (2) aerosol, (3) shear, (4) relative humidity, and (5) temperature components.
A very important discovery that the rank of Aerosol has been preceded the humidity in
three cases with higher Adjusted R2. This demonstrated the fact that by removing
redundant variables from the predictor variable set improves the performance of the
models by refining the effect of the curse of dimensionality as well as enhancing the
interpretability of the model.
xiii
CHAPTER ONE
1
1. INTRODUCTION
Each year about 160 to 320 million tons African dust are transported from Africa to
the Atlantic Ocean (Kaufman, et al., 2005); about 132±44 million tons are deposited in
the Atlantic Ocean (Dunion & Velden, 2004). Extensive seasonal dust variability occurs
between June and October every year over West Africa and moves toward the tropical
Atlantic. Satellite data analysis and research of dust aerosol effects on hurricanes are
comparatively new, therefore it is still difficult to conclusively understand the impacts of
dust on hurricane’s intensity and lifetime. Although the detailed mechanisms linking dust
amount to hurricane activity is still unclear, negative influences of the Saharan Air Layer
(SAL) are pointed out by (Dunion & Velden, 2004). Thus, assumptions of the general
applicability of SAL impacts on hurricanes are discussed in several papers (Wu, et al.,
2006) (Jones, et al., 2007) (Shu & Wu, 2009).This suggests there may be considerable
evidence showing the suppression of hurricane development by the dry and dust-laden
SAL, as indicated as well by the reduction of Atlantic hurricane activity of 2006 and
2007 compared to 2004 and 2005 (Lau & Kim, 2007a) (Lau & Kim, 2007b) (Sun, et al.,
2008). A relationship of recent increase in hurricane and decrease in SAL activities were
also discovered by Wu 2007 (Wu, 2007).
1.1
WhyStudyDustAerosolsforHurricanes
There is an effect of aerosol dusts on regional weather and global climate systems
both directly and indirectly; therefore, they are important to study and monitor. It forces
climate in two ways (Hansen, et al., 1997), the scattering of radiation back to space and
2
the absorption of incoming radiation are the major components of indirect radiative
forcing; aerosols suppress form cloud droplets. This in turn, changes cloud properties and
can be seen as the effect of indirect radiative forcing. Additionally, a smaller effect of the
indirect radiative force includes the broad range of greenhouse gases chemistry to interact
aerosol on the land surface. This also contributes to change the global climate system
radiative characteristics. Several numbers of radiation budget effects can occur depending
on the characteristics of aerosols. Backscattering from the aerosol introduces a cooling
effect to the atmosphere while smoke aerosol particles absorb solar radiation. This blocks
radiation to propagate back to space and introduces atmospheric greenhouse effect.
Smoke aerosol also helps to condense water vapor during cloud formation which causes
smaller cloud droplet size, thereby, repressing cloud formation (Hansen, et al., 1997).
Large amounts of dust aerosol in the atmosphere backscatter incoming solar radiation
significantly, resulting in a reduction in ocean surface temperatures (Wong, et al., 2008).
Dust affects the weather system by interacting with incoming solar and outgoing long
wave radiation and thereby changing humidity and temperature (Wielicki, et al., 1996).
Any changes in atmospheric dust contents would create a change in the radiation balance
and, inevitably, surface temperatures (Houghton, et al., 2001) (Liao & Seinfeld, 1998).
The extent and the indication of the direct effect of dust depend on its optical
properties, vertical distribution, cloud cover, and the albedo of the underlying surface
3
(Liao & Seinfeld, 1998). Also, the optical properties of dust depend on its particle size
and the refractive index (Sinyuk, et al., 2003).
When Saharan dust normally travels over the main Atlantic hurricane development
region and sits during its life time, reduction of deep convection and increase in vertical
wind shear has been observed, which inhibits the formation of hurricanes. Although the
detailed mechanisms linking dust amount to hurricanes activity is unclear, there is
considerable evidence showing the suppression of hurricanes development (Dunion &
Velden, 2004) by the dry and dust-laden SAL (Carlson & Prospero, 1972) (Karyampudi
& Carlson, 1988). It is reported that the backscattering of incoming radiation could be
increased by heavily concentrated dust in the atmosphere causing a reduction in the ocean
surface temperature (Wong, et al., 2008).
To sustain a strong hurricane, water temperature above 26°C and warm water depths
of 150 feet are needed while strong vertical shear in the atmospheric horizontal winds
around the hurricane dampen its force (NOAA Hurricane Hunters
http://flightscience.noaa.gov/). Extremely dry conditions in the mid-atmosphere may act
as an agent of taming hurricane force as well (Kamal, et al., 2012) (Dunion & Velden,
2004). Houze, et al., (Houze, et al., 2006) reported the dynamics of the internal structure
of the vortex are responsible for hurricane intensity changes. Therefore, suggested
improvements on physical understanding in forecasting hurricane intensity modeling of
the internal structure of the vortex. The Statistical Hurricane Intensity Prediction Scheme
4
(SHIPS) model is known as the best of the intensity forecast models (Cangialosi &
Franklin, 2012) based on the 2012 National Hurricane Center (NHC) Forecast
Verification Report. However, the SHIPS’ model lack of dust aerosol effects on the
hurricane intensity.
Global climate system balance between the effects of these parameters are still being
worked and claimed, and are being heavily debated among the Earth Science
communities. Therefore, further data analysis is required to examine aerosol dusts
impact on global climate.
1.2
StatementofProblems
Between June and July 2006 over the Sahara Desert in Africa several dust events
were observed.1 Satellite data products have been used to confirm that the dust particles
played a significant role in blocking solar radiation and cooling the sea surface
temperature. Since hurricanes rely on warm water to form, this cooling effect may have
inhibited the development of some of the storms. Typical hurricane season consists of
approximately 13/14 hurricanes, however, because of this event, the 2006 hurricane
season concluded with only five hurricanes. NASA considered this as a relatively quiet
Atlantic hurricane season compared to 2005.
1
http://www.nasa.gov/mission_pages/hurricanes/archives/2007/hurricane_dust.html
5
Recent NASA studies put forward that miniature aerosol dust particles may have
obstructed the 2006 hurricane season to be another active one. Therefore, suggests such
dust outbreaks may inhibit hurricane formation.
Due to the significance of dust aerosols in climate and its effect on atmospheric
parameters such as temperature and moisture, studying dust aerosols is becoming a very
interesting topic in the scientific community. There are hundreds of sensors aboard the
Earth orbiting satellites; however, not all sensors meet the requirements for monitoring
dust aerosols. The MODIS, MISR, and CALIPSO are a few of the instruments suitable
for this task because of their good spectral, spatial, and temporal resolutions. (Seemann,
et al., 2006) (Diner, et al., 1998) (Xie, et al., 2010). In this dissertation the MODIS and
the MISR are selected as the primary sensors for aerosol dust and the AIRS sensor is used
for temperature and RH retrieval.
Currently, several approaches have been developed for dust aerosol data retrieval
using MODIS and other measurement sensors. However, there has only been a relatively
modest improvement in evaluating aerosol dust effects before, during, and after any
hurricanes in their reported span of life, even though satellite observations and numerical
weather prediction have advanced the forecasting of hurricane tracks over the last two
decades. hurricane strength forecasts are remarkably susceptible to moisture clarifications
and a through expansion in hurricane forecasting when water vapor profiling data is
incorporated (Hou, et al., 2001) (Kamineni, et al., 2003). Hurricane intensified when
6
there is more precipitation in the atmosphere and surface moisture differences are the
primary contributor to intensity forecast differences, while convective instability
differences play a lesser role (Sippel & Zhang, 2010).
Therefore, a new technique was developed for selection of spatial region around
the hurricane core described in Chapter 5, for collecting dust aerosols and atmospheric
profiles data combining MODIS, MISR and AIRS measurements.
1.3
Objectives
Various studies suggested (Evan, et al., 2006) (Sun, et al., 2008) that dust aerosols
have a negative impact on hurricane intensities without providing any detailed statistical
relationship between them and how they impact specific hurricane quantitatively. The
principal intent, therefore, is to study the impact of dust aerosols on the North Atlantic
hurricanes and the atmospheric profiles in the vicinity of the hurricane center. This
dissertation presents the quantitative results and statistical relationship between them.
The objectives to determine the quantitative values are presented below considering four
hurricanes:
1.
To examine the impact of dust aerosol and atmospheric profiles during
each hurricane within the spatial range chosen between the two concentric circles
called an annulus which was divided into left and right side. These two regions
are 180° degrees apart from next adjacent region:
7
a.
Staying along the path of a hurricane - “dynamic-coordinate”. The
dynamic- coordinate means measure of the dust aerosols and atmospheric profile
contents by following the direction of motion of a hurricane.
b.
Staying in a fixed point - “static-coordinate”. The static-coordinate means
measure of the dust aerosols and atmospheric profile contents staying in a same
point for a hurricane life span. The static point is selected at a time point when a
hurricane reaches its highest wind speed at lowest pressure.
c.
Relationship between hurricanes and dust aerosols as well as the
atmospheric profile elements for the left and right side scenarios are presented for
both “a” and “b” scenarios.
2.
To discuss the variation of temperature and RH values in static–left,
static–right, dynamic–left and dynamic–right for 500, 700, and 850 mb pressure
levels.
3.
To measure atmospheric profile difference around each hurricane to
determine warm, cold, dry and wet conditions based on “pre-hurricane” and
“post-hurricane” scenarios.
It is imperative to know the intensity change of the hurricane force in advance
based on temperature, moisture, vertical shear as well as aerosol retrievals. In this
dissertation, therefore, a systematic multivariate analysis of the MODIS aerosol retrievals
over the North Atlantic spanning 7 hurricane seasons combined with SHIPS parameters is
presented. The focus is on investigating the effects of 5 categories which include aerosol,
8
wind, relative humidity, shear, and temperature on hurricane intensities. The primary goal
is to be able to explain the same physical phenomena equally well by reducing
significantly the number of parameters employed in the statistical analysis. A reduction
of variables in two steps (1) the Correlation Coefficients (cc) and (2) the Principal
Component Analysis (PCA) are introduced. The idea is to describe same meaningful
physical phenomena by a smaller set of derived variables which will be linear
combinations of the original variables. Reducing the number of variables may lead to
some loss of original information of the dataset. However, PCA makes this loss minimal
and will present a precise meaning without losing original information. In this
dissertation, therefore, the important relationship between intensity change and the
combination of MODIS aerosol retrievals and SHIPS parameters over the North Atlantic
spanning seven hurricane seasons are focused on.
1.4
OrganizationofDissertation
There are eight chapters in this dissertation. The first four chapters contain the
background, literature review, existing approaches, and theories to follow as well as
understand the study. The next four chapter’s present methodology and various works
related to the objectives of the dissertation. This dissertation ends with a summary and
discussion of future work.
Chapter 1 is the general introduction, including the claim that dust plays an
important role in changing regional and global climates, research objectives, major data
sources and principle results of the dissertation.
9
Chapter 2 reviews the literature on dust aerosol, atmospheric profiles, hurricanes;
theory of dust aerosol and relative humidity are discussed.
Chapter 3 describes Aerosol Theory and retrieval of dust aerosol data by the
MODIS and MISR sensors along with RH and temperature from AIRS.
Chapter 4 Tropical Storms and hurricane formation conditions are discussed.
Chapter 5 discusses the Methodology.
Chapter 6 introduces the Data Processing and Analysis Methods.
Chapter 7 presents results and discussions based on the MODIS, AIRS and MISR
Level 2 data analysis. Comparison of dust data retrieval by the MODIS and MISR are
also described.
Assessment of the results is presented as Conclusions in Chapter 8, which include
discussions on boundaries, uniqueness, and recommendations for the future directions.
1.5
MajorDataSources
The major datasets used in this dissertation include the MODIS Level 2 Aerosol
products for Terra (MOD 04) and Aqua (MYD04). These products were selected because
they monitor the ambient Aerosol Optical Thickness (AOT) over the oceans globally.
MODIS Level 2 data are produced daily at 10x10 km pixel size (Tanré, et al., 1997)
(Remer, et al., 2002).
The MISR Level 2 aerosol data products from Aqua (MIL2ASAE) were selected.
Table 1.1 and Table 1.2 summarize the major data used in this study and their sources.
10
Table 1.1 MODIS, MISR, AIRS data products used and their specifications
Platform
Sensor
Total Band
Terra and Aqua
MODIS
36
Band # for
Atmospheric
Profile
20, 22, 23, 24, 25, 27,
28, 29, 30, 31, 32, 33,
34, 35, 36
Terra
MISR
4 bands (blue, green,
red, and nearinfrared: 0.446,
0.557, 0.671, and
0.866 µm
N/A
Band # for
Aerosol
Products
Aerosol
Products Used
0.47, 0.56, 0.65, 0.86,
0.446, 0.557, 0.671,
1.24, 1.64, and 2.13 µm and 0.866 µm
Number of
Pressure Levels
20
MOD04_L2 (Terra)
and MYD04_L2
(Aqua), 10 km × 10 km
at 0.47, 0.56, 0.65,
0.86, 1.24, 1.64, and
2.13 µm
MISR Level-2
Aerosol-Surface
(L2AS), 17.6 km ×
17.6 km.
MIANCAGP MISR Ancillary
Geographic Product
and
MIL2ASAE - MISR
Level-2 Aerosol
Parameters
N/A
Aqua
AIRS
2378 TIR bands and
4 VIS/NIR bands.
Temperature Profile
(Physical Retrieval)
# of channels: 103
Moisture Profile
(Physical Retrieval)
# of channels: 41
N/A
N/A
28 and 100
The AIRS Level 2 Atmospheric Profiles from the Aqua (AIRSX2RET) products
were also selected for this study to analyze temperature profiles. Atmospheric profile data
from Aqua is produced day and night for Level 2 at a horizontal resolution of 45 km and
vertical 28 pressure levels (Aumann, et al., 2003).
11
Table 1.2 Data product and sources
Data Product
Source
URL
MODIS L2 Aerosol
LAADS2
http://ladsweb.nascom.nasa.gov/
MODIS L2 Atmospheric Profile
LAADS2
http://ladsweb.nascom.nasa.gov/
AIRS L2 Standard
GES DISC3
http://disc.sci.gsfc.nasa.gov/
MISR L2 Aerosol
ASDC4
http://eosweb.larc.nasa.gov/
1.6
PrincipalResults
Among all four hurricanes, Helene had the lowest power and intensity. Dust
created an extremely dry atmosphere that was not conducive to intensifying a hurricane.
As such, this study suggests that the dust-ridden air influenced Helene to dissipate, which
resulted in the hurricane not being able to intensify. Average AOT was found higher in
Helene (0.27) when compared to Isabel (0.23), Frances (0.18) and Katrina (0.16).
Negative impacts on the atmospheric moisture levels and positive on the temperature was
found by dust aerosols. Elevated amounts of dust aerosol were found during the month
of July for all seven years of this analysis. Consistent temperature and relative humidity
when MODIS and MISR AOT as well as the AIRS atmospheric profiles data products
were studied for the month of July, August, and September between 2003 and 2009. Five
categories wind, aerosols, shear, relative humidity, and temperature components were
2
LAADS: Level 1 and Atmosphere Archive and Distribution System
Goddard Earth Sciences Data and Information Services Center
4
Atmospheric Science Data Center
3
12
established by reducing 56 variables to 20. Aerosol, wind, humidity, shear, and
temperature all contributing factors in the regression equation with the ranking for the
contribution found as (1) wind, (2) aerosols, (3) shear, (4) relative humidity, and (5)
Temperature components. The contribution factor components such as zonal winds,
estimated ocean heat content and sea surface components are the greatest contributors to
the Multiple Linear Regression (MLR) when all 55 variables were taken into
consideration. In addition, AOT, RH and Shear play important roles.
In the next chapter, I outline previous works on this topic and identify the areas
which have already been well-researched and what is not yet known – the 'gaps'.
13
CHAPTER TWO
14
2. LITERTAURE REVIEWS
As indicated in the previous chapter, the principal intent, therefore, is to study the
impact of dust aerosols on the North Atlantic hurricanes and atmospheric profiles in the
vicinity of the hurricane center and present the quantitative results and statistical
relationship between them. Therefore, it is imperative to outline previous work on this
topic.
Possible effects on hurricanes by the dust aerosol utilizing satellite remote sensing
measurements have been reported for the last three decades. There are different data
collection techniques for different satellite sensors. This chapter concentrates on the
review of these different methods described in several papers of dust aerosol effects on
hurricanes. After examining their merits and limitations I propose a new technique for
data collections and analysis to study the effect of dust aerosol on hurricanes.
2.1
AerosolOpticalThickness(AOT)andAtmosphericProfiles
Recently Dunion, et al., described quite a number of potentially negative effects
of the SAL (Dunion & Velden, 2004).
Three findings which may suppress hurricane activities in the Atlantic are (1)
SAL appears to introduce dry and stable air into the hurricanes, helping downdrafts and
disrupting the convective organization within the hurricane vortex. (2) Midlevel easterly
15
jet of SAL boosts the local vertical wind shear intensely. (3) The preexisting trade wind
inversion can act as a stabilizer for the environment may be enhanced by the SAL
(Dunion & Velden, 2004).
Evan et al., described dust coverage and hurricane activities are inversely
correlated over the tropical North Atlantic without conclusive evidence that the dust itself
is directly controlling hurricane activity. Because dust is a good tracer for the SAL, these
observed correlations may result from the effect of the SAL acting as an agent in the
hurricane activity (Evan, et al., 2006). This supports the hypotheses laid out by Dunion
and Velden in his paper in 2004.
According to an early study (Qu, et al., 2005) the dust cloud cooling/heating and
wetting/drying the surrounding atmosphere can be monitored combining both the MODIS
and the AIRS temperature water vapor profiles. According to these profiles, aerosol dust
is responsible for instilling warm and dry air into hurricanes and inhibits their formation.
Carlson and Prospero stated that near the top of the SAL, the temperature is
cooler than the surrounding tropical atmosphere (Carlson & Prospero, 1972). The SAL’s
lifetime is likely to be lengthened by the persistent temperature downturns that exist at its
base and top. During the daytime, thermal heating by dust pull within the SAL and
counter nighttime radiative cooling occurs. In turn, this maintains the SAL in a relatively
warm and steady state as it crosses the North Atlantic (Carlson & Prospero, 1972).
16
Hao, et al., investigated African dust storms by several MODIS thermal IR bands
and analyzed data for major dust events during the years 2004~2006 on the Atlantic.
Their analysis, which uses a 1 km thermal-infrared dust index (TDI) could be beneficial
in dust storm detection and analysis. Statistical analysis was also presented to enable the
ability to assess the potential impacts on Atlantic hurricanes and climate (Hao & Qu,
2007). Their approach to use thermal bands enabled them to monitor dust at night time,
however, validation of the feasibility needs to be investigated further.
According to Jenkins, et al., infusion of cloud condensation and ice nuclei can
potentially stimulate convection by SAL. In their study, AOT was derived from the
MODIS sensor of the Aqua satellite for identifying SAL outbreaks and to establish
possible relationships with Atlantic hurricane (Jenkins, et al., 2008).
Qu, et al., proposed a new approach to study the dust characteristics by
establishing the normalized difference dust index (NDDI) to identify dust and cloud.
Their results may help to understand the internal mechanism of dust storm and its effects
on the regional weather and environment changes (Qu, et al., 2006).
Madhavan, et al., presented an in-depth comparison between MODIS Terra and
Aqua AOT retrievals over the Ocean (Madhavan, et al., 2008).
17
Wong and Dessler, presented the temperature, moisture and the atmospheric
stability by using MODIS AOT as a proxy to trace the SAL. It was shown that it is the
increase in temperature and decrease in humidity associated with the SAL that leads to
suppression of deep convection over the tropical North Atlantic (Wong & Dessler, 2005).
Rosenfeld, et.al, described the relationship between the intensity change and sum
of anthropogenic aerosols which was calculated as the AOT for black carbon (BC),
organic carbon (OC), dust (DU) and sulfate (SU). The sum of DU, BC and OC is called
‘Pollution’ while and Total AOT (TAOT) as the sum of DU, BC, OC and SU (Rosenfeld,
et al., 2011). In his study, the response variable (intensity change) was examined against
the “pollution’ and “TAOT” (Rosenfeld, et al., 2011) and CCNO (Rosenfeld, et al.,
2012). But the combination of the SHIPS parameters and the aerosol retrievals was not
included.
Zipser, et al., discussed an improved understanding of the linkage between
African Easterly Waves (AEW), the SAL, and tropical cyclogenesis by pointing out (a)
the difference between AEWs that develop into hurricanes and those that do not? (b) Fate
of the AEW by the roles of SAL and (c) vertical distribution, microphysical and optical
properties characteristics in composition of the African dust (Zipser, et al., 2009).
18
Gao and Zha, (Gao & Zha, 2010) studied the influence of air pressure,
temperature, relative humidity, and wind velocity on predicting air pollution from
MODIS AOT data without employing SHIPS parameters.
Braun, S. A. (Braun, 2010) concluded that the SAL is just one of the many
possible influences and can be both positive and negative. He also emphasized that
aerosol is not the major negative influence on hurricanes.
Khain, et al., (Khain, et al., 2010) came up with an additional mechanism which is
related to the hurricane circulation and described that aerosols significantly affect the
spatial distribution of cloudiness and hydrometeor contents.
Wu, et al., utilized the National Center for Atmospheric Research (NCAR) fifthgeneration numerical models of Pennsylvania State University, (nonhydrostatic
mesoscale model (MM5)) to simulate the formation of tropical cyclones (Wu, et al.,
2006). They suggested that SAL influences on Atlantic hurricanes can be investigated
efficiently by utilizing the AIRS Level 2 products. The impact of relaxing the MM5
model to the AIRS retrieved temperature and humidity profiles in regions
uncontaminated by clouds on5 hurricane development was assessed (Wu, et al., 2006).
They suggested the SAL may have inhibited the formation of Hurricane Isabel and
hindered the development of another tropical disturbance. Their study concert with the
5
http://airs.jpl.nasa.gov/science/select_publications/
19
argument that Dunion &Velden (Dunion & Velden, 2004) made on the Atlantic
hurricanes: SAL helps to reduce relative humidity, increase vertical wind shear and trade
wind inversion operate as a stabilizer for the environment (Wu, et al., 2006).
2.2
SHIPSParameters
Dunion and Velden also stated that the operational SHIPS model (DeMaria &
Kaplan, 1994) (DeMaria & Kaplan, 1999) (DeMaria, et al., 2005) may not be well suited
for predicting hurricane intensity change influenced by the SAL, in other words dust
aerosols. Since SHIPS does not include the SAL in its methodology which may not
effectively characterize the thermodynamic properties associated with the SAL (Dunion
& Velden, 2004).
DeMaria, et al., described SHIPS and developed a model using a standard
multiple regression technique to investigate hurricanes from 1989 to 1992. They reported
four primary predictors as (a) hurricane intensity change determined from the sea surface
temperature, (b) the vertical shear of the horizontal wind, 3) persistence, and 4) the flux
convergence of eddy angular momentum evaluated at 200 mb (DeMaria & Kaplan,
1994).
In 1999, DeMaria, et al., presented updates to the existing SHIPS for the Atlantic
basin and the eastern North Pacific version was developed. In this update, they replaced
the climatological SSTs with weekly SST analyses and included synoptic predictors
20
estimated from initial and forecast fields, rather than from initial analyses alone. This
new version of SHIPS with the predictors from forecast fields name as the ‘‘statistical–
dynamical’’ model, while the older versions were ‘‘statistical–synoptic’’ (DeMaria &
Kaplan, 1999).
DeMaria, et al., in 2005 introduced the modifications to the National Hurricane
Centre (NHC) operational SHIPS intensity model for each year from 1997 to 2003. In
this update, several new predictors were also added (DeMaria, et al., 2005).
Wu, L., described practical applicability found by Dunion and Velden 2004. He
associated 1980s decreasing trend in hurricane intensity with the enhancing SAL activity.
As per him drying relative humidity and enhancing vertical shear inhibit hurricanes. Wu
(Wu, 2007) concluded that the SAL may act as a link between Atlantic hurricane activity
and the African monsoon.
2.3
ChapterSummary
In this chapter, publications were reviewed which included techniques and
approaches in aerosol retrieval and linking those techniques to the hurricanes. I found
abundant information on aerosol, temperature, and humidity independently. However, in
most cases the connection between these parameters pertaining to the hurricane, dust
aerosol, temperature, and humidity were absent. Also detailed statistical relationship and
21
how those parameters impact on the specific hurricanes quantitatively was NOT
commonly presented.
Based on the literature reviews above, there are definite ‘gaps’ discovered. Detail
statistical analysis utilizing most recent satellite sensors and quantitative analyses,
relating hurricanes, aerosol, humidity, temperature, and SHIPS parameters are not
commonly considered. Therefore, the rest of the chapters in this dissertation will focus on
those areas and their impacts on the major hurricanes will be discussed.
22
CHAPTER THREE
23
3. THEORY AND DATA COLLECTIONS
In the last chapter, the findings based on several relevant topics on dust aerosol on
tropical cyclones/hurricanes investigated by renowned scientists in this field of study
were presented. In order to understand the effects of dust aerosol on atmospheric profiles
and thereby on hurricanes, one needs to fully understand the detail about what really dust
aerosol means. This chapter, therefore, concentrate on describing the physical phenomena
of dust aerosols and atmospheric profiles including SHIPS parameters used in this
research.
A layer of gases surrounding the earth is the atmosphere; nitrogen, oxygen, carbon
dioxide and other trace gases are the primary constituent of this layer. Aerosols are the
suspended solids or liquids in the atmosphere (Levy & Pinker, 2007). The origin of
aerosols is both natural and man-made. Aerosols radii varies between 0.1 and 20 m.
Aerosol having radius < 0.5 m considered as “fine” and radius > 1.0 m as “coarse”
aerosols.
Two different processes happen when light strikes an aerosol particle: (1) the particles
can re-radiated incoming solar radiation without changing the wavelength which is
scattering. (2) The solar radiation may be transformed inside the particles into another
24
energy form, such as heat or reradiated at a different wavelength which is absorption.6
The sum of (1) and (2) is known as extinction. Aerosol Optical Depth (AOD, ) some
cases also known as Aerosol Optical Thickness (AOT) is a dimensionless parameter
which is a measure of radiation extinction within an atmospheric column.
3.1
SatelliteRetrievedAerosolProducts
There are several NASA satellite instruments such as MODIS, MISR, and
CALIPSO that are currently monitoring the Earth's atmosphere to measure aerosols. The
satellite cannot determine the aerosol concentration directly, but rather AOT, or the
amount of radiation that has been blocked by the aerosols through the atmosphere with its
scattering or absorption properties. The measure of radiation that reaches the Earth’s
surface depends on the dimensionless valued of the AOT and is inversely proportional to
the radiation reaches Earth's surface.
Dust aerosol concentration and its effect on the Earth’s energy budget are very
close when AOT values were measured at 0.55µm. Therefore, the MODIS Aerosol data
file attribute, “Effective_Optical_Depth_Best_Ocean” (EODBO) has been selected for
this study, because it is the Best Solution at 0.55µm out of the 7 bands of MODIS
Aerosol Products.
6
http://www.newmediastudio.org/DataDiscovery/Aero_Ed_Center/Charact/C.Optical_pro
25
Spectral radiance between 0.55 and 2.13µm contain three members of
independent information about the aerosol loading and size properties (Section 3.4. of
MOD04/MYD04 Retrieval algorithm, Levy, et al., (Levy, et al., 2009)
http://modis.gsfc.nasa.gov/data/atbd/atbd_mod02.pdf,) .
The aerosol algorithm derives three parameters described based on Levy, et al., (Levy, et
al., 2009) as follows:
(1) AOT at one wavelength
.
,
(2) Over the ocean definition of Fine Weighting (FW) which is ‘reflectance
weighting parameter’ at one wavelength (0.55 ) and
(3) The ratio of the 3rd and 2nd moments of size distribution of the aerosol particle,
‘effective radius’ (re).
Single ‘fine’ (f) and single ‘coarse’ (c) aerosol modes were chosen to represent the
effective radius for combining with the  parameter. Based on the look-up table (LUT) of
four fine modes and five coarse modes (ATBD MOD04/MYD04, (Levy, et al., 2009))
inversion is performed.78 Even though definition of LUT is given in terms of a particular
wavelength of optical thickness, the factors of each of the single mode models
characterize a unique spectral dependence for that model. This is being utilized to acquire
.
value to establish optical thickness at other wavelengths. A single fine mode and a
single coarse mode are necessary for this retrieval, therefore, twenty combinations of fine
7
8
http://modis.gsfc.nasa.gov/data/atbd/atbd_mod02.pdf
http://irina.eas.gatech.edu/EAS6145_Spring2011/Lecture6.pdf
26
and coarse modes. Their relative optical contributions that best imitates the MODISobserved spectral reflectance has been determined.8  is being used as the weighting
parameter the reflectance from each mode described above and presented as equation
(3.1) (Levy, et al., 2009),


.


+ 1
.

(3.1)
.
Where a weighted average reflectance of an atmosphere with a pure fine mode 'f'
and optical thickness
.
is represented by

.
in equation (3.1) (Levy, et al.,
2009) which is basically the reflectance of an atmosphere with a pure coarse mode 'c' also
with the same
.
. The pair of
.
and 0.55 that cut the ‘fitting error’ (+) represented
by equation (3.2) (Levy, et al., 2009), (this pair generated for each of the twenty
combinations of one fine mode and one coarse mode utilizing the inversion)9
∑





∑
.
where N is the sum of good pixels at wavelength, ,
measured from MODIS at wavelength, ,
(3.2)



is the reflectance
is the Rayleigh scattering factor, and

is determined by Equation (3.1). To prevent a division by zero for the larger wavelengths
9
http://modis.gsfc.nasa.gov/data/atbd/atbd_mod02.pdf
27
may happen under clean conditions (Tanré, et al., 1997), therefore, the ‘0.01’ term
introduced in the equation (3.1).
Based on the values of  twenty solutions are sorted and the ‘best’ solution is the
combination of modes with accompanying
.
and 0.55 that minimizes . Additional
consistency checks applied prior to production of the final output employing quality (QA)
values.
MISR Level 2 Aerosol Retrieval Algorithm Theoretical Basis March 10, 2008 was
also consulted and a Level 2 aerosol product MIL2ASAE with spatial resolution of 17.6
km grid was also selected for this study as per the MISR ATBD document (Diner, et al.,
1999) (Diner, et al., 2008).
3.2
SatelliteRetrievedAtmosphericProfiles
Retrieval of atmospheric profiles from combined radiance measurements of the
AIRS onboard the NASA Aqua satellite was analyzed. High spatial resolution
atmospheric temperature and moisture sounding data from AIRS offer abundant new
atmospheric structure information in clear skies. The AIRS Level 2 standard retrieval
products provide atmospheric profiling at 28 layers or 100 layers. Twenty eight levels
AIRS temperature, TAirSTD from AIRS were selected for this study.
28
The relative humidity is defined as the fraction of water vapor in the atmosphere at
any given instance that is not a smaller amount than that required to saturate the air mass.
RH is expressed is as a percentage value which is the ratio of the actual water vapor
pressure w to the equilibrium vapor pressure over a plane of water ws, known as
saturation vapor pressure (Lawrence, 2005).
In this dissertation, two AIRS attributes are used to determine the percent of relative
humidity. The moisture profile is represented by the term RH. The two attributes from
AIRS are, (1) Retrieved Water Vapor Mass Mixing Ratio Profile, (gm/kg) H2OMMRStd,
and (2) Saturation Water Vapor Mass Mixing Ratio Profile over equilibrium phase ,
(gm/kg) H2OMMRSat (Aumann, et al., 2003). RH was calculated based on the formula
RH = (H2OMMRStd/H2OMMRSat)*100.
SHIPS parameters were gathered based on DeMaria, et al., (DeMaria & Kaplan,
1994) (DeMaria & Kaplan, 1999) (DeMaria, et al., 2005). SHIPS model combines
climatology, atmospheric environmental parameters, and sea surface temperature as its
predictors to forecast intensity changes using a multiple regression scheme (Rosenfeld, et
al., 2011). Selected SHIPS parameters based on the website at
ftp://rammftp.cira.colostate.edu/demaria/SHIPS/ and presented in the table below
29
Table 3. 1 SHIPS parameters
Name
SST
RHLO
RHMD
RHHI
SHRS
VMAX
MSLP
INCV
SST
DTL
PHCN
U200
U20C
V20C
E000
EPOS
ENEG
EPSS
ENSS
PSLV
Z850
D200
REFC
PEFC
T000
R000
Z000
TWAC
Description
Climatological SST (deg C * 10) vs time
850-700 mb relative humidity (%) vs time (200-800 km)
700-500 mb relative humidity (%) vs time (200-800 km)
500-300 mb relative humidity (%) vs time (200-800 km)
850-500 mb shear magnitude (kt *10) vs time
The current maximum wind intensity in kt
Mean sea level pressure (hPa)
Intensity change (kt) -18 to -12, -12 to -6, ... 114 to 120 hr.
SST (deg C*10) vs time
Distance to nearest major land mass (km) vs time
Estimated ocean heat content (kJ/cm2) from climo OHC and current SST
anomaly.
Designed to fill in for RHCN when that is missing.
200 mb zonal wind (kt *10) vs time (r=200-800 km)
Same as U200 but for r=0-500 km)
Same as U20C, but for the v component of the wind
1000 mb theta_e (r=200-800 km) vs. time (deg K*10)
The average theta_e difference between a parcel lifted from the surface and
its environment (200-800 km average) versus time (deg C * 10). Only
positive differences are included in the average
Same as EPOS, but only negative differences are included. The minus sign is
not included.
Same as EPOS, but the parcel theta_e is compared with the saturated theta_e
of the environment
Same as ENEG, but the parcel theta_e is compared with the saturated theta_e
of the environment
Pressure of the center of mass (mb) of the layer where storm motion best
matches environmental flow (t=0 only)
850 mb vorticity (sec-1 * 10**7) vs time (r=0-1000 km)
Same as above for 200 mb divergence
Relative eddy momentum flux convergence (m/sec/day, 100-600 km avg) vs
time
Planetary eddy momentum flux convergence (m/sec/day, 100-600 km avg) vs
time
1000 mb temperature (dec C* 10) (200-800 km average)
1000 mb relative humidity (200-800 km average)
1000 mb height deviation (m) from the U.S. standard atmosphere
0-600 km average symmetric tangential wind at 850 mb from NCEP analysis
(m/sec *10)
30
Name
TWXC
PENC
SHDC
SDDC
SHGC
DIVC
T150
T200
T250
SHRD
SHTD
SHTS
SHRG
PENV
VMPI
VVAV
VMFX
VVAC
IRXX
IR00
IRM3
RD20
RD26
RHCN
Description
Maximum 850 mb symmetric tangential wind at 850 mb from NCEP analysis
(m/sec *10)
Azimuthally averaged surface pressure at outer edge of vortex ( (mb1000)*10)
Same as SHRD but with vortex removed and averaged from 0-500 km
relative to 850 mb vortex center
Heading (deg) of above shear vector
Same as SHRG but with vortex removed and averaged from 0-500 km
relative to 850 mb vortex center
Same as D200, but centered at 850 mb vortex location
200 to 800 km area average 150 mb temperature (deg C *10) versus time
Same as above for 200 mb temperature (deg C *10)
Same as above for 250 mb temperature (deg C *10)
850-200 mb shear magnitude (kt *10) vs time (200-800 km)
Heading (deg) of above shear vector
Heading of above shear vector
Generalized 850-200 mb shear magnitude (kt *10) vs time (takes into account
all levels
200 to 800 km average surface pressure ((mb-1000)*10)
Maximum potential intensity from Kerry Emanuel equation (kt)
Average (0 to 15 km) vertical velocity (m/s * 100) of a parcel lifted from the
surface where entrainment, the ice phase and the condensate weight are
accounted for. Note: Moisture and temperature biases between the operational
and reanalysis files make this variable inconsistent in the 2001-2007 samples,
compared 2000 and before.
Same as VVAV, but a density weighted vertical average.
Same as VVAV but with soundings from 0-500 km with GFS vortex
removed
Same as IR00 below, but generated from other predictors (not satellite data).
These should only be used to fill in for IR00 as needed.
Predictors from GOES data (not time dependent).
Same as IR00 but at three hours before initial time
Ocean depth of the 20 deg C isotherm (m), from satellite altimetry data
Ocean depth of the 26 deg C isotherm (m) from satellite altimetry data
Ocean heat content (KJ/cm2) from satellite altimetry data
31
3.4 MODISAerosolProducts
The MODIS sensor was first launched on Terra platform in December 1999.
MODIS made it possible to initiate a new era in Earth science satellite remote sensing.
As the first MODIS sensor was already orbiting in the space on the Terra (EOS AM-1)
satellite, the second MODIS sensor was launched on the Aqua (EOS PM-1) platform in
2002. Both MODIS instruments aboard the NASA Earth Observing System (EOS) Terra
and Aqua platforms for monitoring the environment continuously by assimilating aerosol
density, mapping the surface of clouds and tracing atmospheric constituent gases in a
wide-range of spectral frequencies from the blue to the thermal infra-red. MODIS is an
exceptional source for monitoring the Earth's water cycle and our environment (Levin, et
al., 1996).
Terra and Aqua satellites both have a sun-synchronous with a 705km orbit. Both
are in operations near polar and circular orbit. Aqua in its ascending mode crosses the
equator daily at 1:30 p.m. as it heads north. While Terra in descending mode crosses the
equator at 10:30 a.m. daily as it heads south. From this wealth of information it is
expected that combining Aqua PM and Terra AM observations will provide valuable
insights into the daily cycling of global precipitation and ocean circulation (Madhavan, et
al., 2008).
Thirty six spectral bands between wavelength from 0.4 µm to 14.4 µm and at varying
spatial resolutions data are acquired by MODIS instrument. Several bands at resolution
250 m, 500 m and 1 km correspond to 2, 5, and 29 bands respectively can be found in
32
MODIS. MODIS on Terra platform has been in the orbit for over a decade and bring
exceptional opportunity to compare fully collocated pixel-level MODIS aerosol retrievals
directly and globally (Levin, et al., 1996).
The MODIS Aerosol Product (MOD 04 and MYD04) was selected because it
monitors the ambient AOT over the oceans globally. Daily Level 2 data are produced at
the spatial resolution of a 10 x 10km pixel array (Tanré, et al., 1997) (Remer, et al.,
2002).
MODIS, MOD04/MYD04 aerosol products measured over the ocean (Tanré, et al.,
1997) are assimilated based on an algorithm for the remote sensing of tropospheric
aerosol is different than the aerosol over land (Kaufman, et al., 1997). MODIS observed
reflectances were matched to a lookup table of pre-computed reflectances for a wide
range of normally observed aerosol conditions for both algorithms (King, et al., 1999).
Small surface reflectance considered to the overall reflectance, which is calculated from
the geometry pertaining to the state of the ocean (Tanré, et al., 1997). Better ocean
surface characterization enables (King, et al., 2003) the use of reflectances at 0.47, 0.56,
0.65, 0.86, 1.24, 1.64, and 2.13 µm in the equation. The aerosol products retrieved are
then represented by the best fits between observed reflectance and the lookup table. AOT
at 0.47, 0.56, 0.65, 0.86, 1.24, 1.64, and 2.13 µm with 10X10 km pixel size can be
retrieved from ocean aerosol products.
33
The accuracy of satellite estimates of AOT was first suggested based on theoretical
analyses (Kaufman, et al., 1997) (Tanré, et al., 1997), and presented as a bias of 0.03 over
ocean because of the doubt in the estimate of surface albedo also in the aerosol
absorption and scattering phase function uncertainty, a bias as 0.05 over ocean report.
Several literatures reported the comparison of aerosol products between MODIS and
Aerosol Robotic Network (AERONET) sun photometers (Holben, et al., 1998). Remer,
et, al., confirms MODIS retrieval with an accuracy of effective radius 0.10 µm over the
ocean in non-dusty region (Remer, et al., 2002). Smaller deviations were reported by
Levy et al. (Levy, et al., 2003) in the mid-visible wavelengths where less accuracy in
determining particle radius was presented. This decreased level of accuracy may exist
because of the non sphericity effects were not accounted in the lookup tables (King, et al.,
2003). Better cirrus recognition technique over the ocean has been reported (Kaufman, et
al., 2002) to combine cold cloud masking, spatial consistency tests (Martins, et al., 2002)
based on the 1.38 µm band at a high cloud mask test.
3.5
MISRAerosolProducts
MISR was launched aboard the NASA Earth Observing System’s Terra spacecraft in
December 1999. It has a sun synchronous orbit that crosses the equator at about 10:30
A.M. local time, descending on the dayside of the planet. The subspacecraft point reaches
to about 82 latitude (Analytics, 2012). The orbit number is a sequential counter related
to the time of data acquisition; about 14.56 orbits are completed each day, which amounts
to about 5315 orbits per year (Kahn, et al., 2009). MISR has nine cameras pointed toward
34
the Earth at nine look-angles ranging from +70◦ through nadir to −70◦ along the
spacecraft ground track. Each camera focal plane contains four line arrays of 1504
photoactive pixels each, which have blue, green, red, and near-IR filters (Diner, et al.,
1998). The instrument images reflected sunlight, covering equatorial latitudes about once
in nine days and polar latitudes about once every two days. Each of the nine cameras
view angles of MISR has four wavelengths 446, 558, 672, and 866 nm. In this study for
aerosol dust AOD measurements, MISR Level 2 aerosol products, MIL2ASAE with
spatial resolution of 17.6 km grid was also selected in addition to the MODIS Level 2,
MOD04/MYD04 products.
3.6
AIRSAtmosphericProfileProducts
The Atmospheric Infrared Sounder (AIRS) lunched on the Aqua platform also
measures atmospheric profiles with its infrared spectrometer scanning between 3.7 and
15.4µm spectral range with 2378 spectral channels (Aumann, et al., 2003). AIRS uses a
physical algorithm for the retrieval of atmospheric profiles dependent on fast and
accurate radiative transfer algorithm for computing clear-air radiances which uses
approximately 300 channels to determine temperature, water, and ozone profiles (Strow,
et al., 2003). The cross-track scanner of AIRS with 15km resolution at nadir and 708 km
orbital altitude produces highly accurate global atmospheric profiles product twice every
day.
35
3.7
DataCollection
Two methods were applied to study the dust aerosol impacting on hurricanes and
atmospheric profiles. (1) Dynamic-Coordinate and (2) Static-Coordinate: Data were
collected based on the hurricanes center locations as described by the table 3.2, 3.3, 3.4,
and 3.6. Latitude, Longitude of the hurricane center corresponding wind speed, pressure
and dates were consulted from the NHC of National Weather Service (NWS) website
(http://www.nhc.noaa.gov/).
Table 3. 2 Location of hurricane Isabel, between September 07 and 18, 2003
LAT
LON
Time
Wind (mph) Pressure (mb)
CAT
15.2
15.8
16.5
17.1
17.6
18.2
18.9
19.4
20.0
20.5
20.9
21.1
21.1
21.2
21.3
21.4
21.5
21.6
21.7
21.6
-38.5
-39.7
-40.9
-42.0
-43.1
-44.1
-45.2
-46.3
-47.3
-48.3
-49.4
-50.4
-51.4
-52.3
-53.2
-54.0
-54.8
-55.7
-56.6
-57.4
09/07/18Z
09/08/00Z
09/08/06Z
09/08/12Z
09/08/18Z
09/09/00Z
09/09/06Z
09/09/12Z
09/09/18Z
09/10/00Z
09/10/06Z
09/10/12Z
09/10/18Z
09/11/00Z
09/11/06Z
09/11/12Z
09/11/18Z
09/12/00Z
09/12/06Z
09/12/12Z
70
80
95
110
110
115
115
115
115
110
110
115
120
125
125
135
145
140
140
140
1
1
2
3
4
4
4
4
4
3
3
4
4
4
4
4
5
5
5
5
36
984
976
966
952
952
948
948
948
948
952
952
948
942
935
935
925
915
920
920
920
LAT
LON
Time
Wind (mph) Pressure (mb)
CAT
21.7
21.8
21.9
22.1
22.5
22.9
23.2
23.5
23.9
24.3
24.5
25.3
25.7
26.3
26.8
27.4
28.1
28.9
29.7
30.6
31.5
-58.2
-59.1
-60.1
-61.0
-62.1
-63.3
-64.6
-65.8
-67.0
-67.9
-68.8
-69.8
-70.2
-70.5
-70.9
-71.2
-71.5
-71.9
-72.5
-73.0
-73.5
09/12/18Z
09/13/00Z
09/13/06Z
09/13/12Z
09/13/18Z
09/14/00Z
09/14/06Z
09/14/12Z
09/14/18Z
09/15/00Z
09/15/06Z
09/15/18Z
09/16/00Z
09/16/06Z
09/16/12Z
09/16/18Z
09/17/00Z
09/17/06Z
09/17/12Z
09/17/18Z
09/18/00Z
140
135
130
135
140
135
135
135
140
130
125
115
105
100
95
95
95
95
90
90
90
5
4
4
4
5
4
4
4
5
4
4
4
3
3
2
2
2
2
2
2
2
37
920
925
935
935
932
935
939
935
933
937
940
949
952
955
959
959
957
957
957
955
953
Table 3.3 Location of hurricane Frances, August 25 & September 06, 2004
LAT
11.5
11.9
12.3
12.8
13.3
13.7
14.2
14.7
15.4
15.9
16.6
17.2
17.7
18.1
18.4
18.6
18.8
18.9
19.0
19.2
19.4
19.6
19.8
20.0
20.3
20.6
21.0
21.4
21.8
22.2
22.7
23.2
23.8
24.2
24.7
25.3
25.7
26.0
26.4
LON
-39.8
-41.5
-42.9
-44.5
-45.8
-46.8
-47.8
-48.5
-49.3
-50.0
-50.9
-51.6
-52.3
-52.9
-53.6
-54.4
-55.0
-55.8
-56.8
-58.1
-59.3
-60.7
-62.1
-63.5
-65.0
-66.4
-67.9
-69.1
-70.4
-71.4
-72.5
-73.5
-74.3
-75.0
-75.7
-76.3
-77.1
-77.5
-77.9
Time
08/25/18Z
08/26/00Z
08/26/06Z
08/26/12Z
08/26/18Z
08/27/00Z
08/27/06Z
08/27/12Z
08/27/18Z
08/28/00Z
08/28/06Z
08/28/12Z
08/28/18Z
08/29/00Z
08/29/06Z
08/29/12Z
08/29/18Z
08/30/00Z
08/30/06Z
08/30/12Z
08/30/18Z
08/31/00Z
08/31/06Z
08/31/12Z
08/31/18Z
09/01/00Z
09/01/06Z
09/01/12Z
09/01/18Z
09/02/00Z
09/02/06Z
09/02/12Z
09/02/18Z
09/03/00Z
09/03/06Z
09/03/12Z
09/03/18Z
09/04/00Z
09/04/06Z
Wind (mph)
35
40
45
55
65
70
75
90
100
100
100
105
115
115
115
115
110
105
100
100
110
110
115
120
125
120
120
120
120
120
125
120
115
105
100
95
90
85
85
38
Pressure (mb)
1005
1003
1000
994
987
984
980
970
962
962
962
958
948
948
948
948
948
954
958
956
948
946
950
949
942
941
939
937
941
939
937
939
948
948
954
958
960
960
960
CAT
1
1
1
2
3
3
3
3
4
4
4
4
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
3
3
2
2
2
2
LAT
26.8
26.8
27.0
27.2
27.4
27.8
28.1
LON
-78.5
-79.1
-79.4
-80.2
-80.7
-81.7
-82.3
Time
09/04/12Z
09/04/18Z
09/05/00Z
09/05/06Z
09/05/12Z
09/05/18Z
09/06/00Z
Wind (mph)
90
90
95
90
80
60
55
Pressure (mb)
962
962
958
960
969
975
978
CAT
2
2
2
2
1
Table 3.4 Location of hurricane Katrina, August 23 & August 29, 2005
LAT
23.1
23.4
23.8
24.5
25.4
26.0
26.1
26.2
26.2
25.9
25.4
25.1
24.9
24.6
24.4
24.4
24.5
24.8
25.2
25.7
26.3
27.2
28.2
29.5
31.1
LON
-75.1
-75.7
-76.2
-76.5
-76.9
-77.7
-78.4
-79.0
-79.6
-80.3
-81.3
-82.0
-82.6
-83.3
-84.0
-84.7
-85.3
-85.9
-86.7
-87.7
-88.6
-89.2
-89.6
-89.6
-89.6
Time
08/23/18Z
08/24/00Z
08/24/06Z
08/24/12Z
08/24/18Z
08/25/00Z
08/25/06Z
08/25/12Z
08/25/18Z
08/26/00Z
08/26/06Z
08/26/12Z
08/26/18Z
08/27/00Z
08/27/06Z
08/27/12Z
08/27/18Z
08/28/00Z
08/28/06Z
08/28/12Z
08/28/18Z
08/29/00Z
08/29/06Z
08/29/12Z
08/29/18Z
Wind (mph)
30
30
30
35
40
45
50
55
60
70
65
75
85
90
95
100
100
100
125
145
150
140
125
110
80
39
Pressure (mb)
1008
1007
1007
1006
1003
1000
997
994
988
983
987
979
968
959
950
942
948
941
930
909
902
905
913
923
948
CAT
1
1
1
2
2
2
3
3
3
4
5
5
5
4
3
1
Table 3.5 Location of hurricane Helene between September 12 and 24, 2006
LAT
11.9
11.9
11.9
12.0
12.2
12.5
12.9
13.2
13.6
14.0
14.4
14.8
15.5
16.2
17.0
17.7
18.4
18.9
19.4
19.9
20.3
20.8
21.4
22.0
22.7
23.6
24.0
24.3
24.4
24.4
24.5
24.9
25.4
25.9
26.8
27.8
29.0
30.2
LON
-22.0
-23.2
-24.6
-26.1
-28.0
-30.0
-31.9
-33.8
-35.6
-37.0
-38.3
-39.6
-40.8
-42.1
-43.3
-44.3
-45.3
-46.2
-47.1
-47.9
-48.4
-48.8
-49.1
-49.4
-49.7
-50.4
-51.0
-51.7
-52.5
-53.5
-54.4
-55.2
-56.0
-56.7
-57.0
-57.0
-57.0
-57.0
Time
09/12/12Z
09/12/18Z
09/13/00Z
09/13/06Z
09/13/12Z
09/13/18Z
09/14/00Z
09/14/06Z
09/14/12Z
09/14/18Z
09/15/00Z
09/15/06Z
09/15/12Z
09/15/18Z
09/16/00Z
09/16/06Z
09/16/12Z
09/16/18Z
09/17/00Z
09/17/06Z
09/17/12Z
09/17/18Z
09/18/00Z
09/18/06Z
09/18/12Z
09/18/18Z
09/19/00Z
09/19/06Z
09/19/12Z
09/19/18Z
09/20/00Z
09/20/06Z
09/20/12Z
09/20/18Z
09/21/00Z
09/21/06Z
09/21/12Z
09/21/18Z
Wind (mph)
25
30
30
30
30
30
35
35
40
40
40
45
50
55
60
60
65
65
70
75
80
90
100
105
105
95
95
95
95
90
90
90
90
80
80
75
75
75
40
Pressure (mb)
1007
1007
1007
1007
1007
1006
1005
1005
1003
1003
1002
1000
997
995
992
990
987
986
983
979
976
970
962
955
958
962
962
960
958
956
958
958
959
960
962
964
968
970
CAT
1
1
1
1
1
2
3
3
3
2
2
2
2
2
2
2
2
1
1
1
1
1
LAT
31.4
32.5
33.6
34.9
35.7
36.4
37.1
37.7
38.6
39.5
40.6
LON
-56.6
-55.9
-55.1
-53.7
-51.7
-49.6
-47.5
-45.4
-43.2
-40.9
-38.2
Time
09/22/00Z
09/22/06Z
09/22/12Z
09/22/18Z
09/23/00Z
09/23/06Z
09/23/12Z
09/23/18Z
09/24/00Z
09/24/06Z
09/24/12Z
Wind (mph)
75
70
70
70
75
80
80
75
70
65
65
Pressure (mb)
970
970
970
968
964
962
962
963
964
964
964
CAT
1
1
1
1
1
1
1
1
1
1
1
3.7.1 Dynamic‐Coordinate
In the dynamic-coordinate, dust aerosols and atmospheric profile contents measured
by following the direction of motion of a hurricane. MODIS (Terra (MOD) and Aqua
(MYD)) and AIRS data (Remer, et al., 2005) were collected from
http://ladsweb.nascom.nasa.gov and http://disc.sci.gsfc.nasa.gov. Vertical information of
Ocean to Top of the Atmosphere (TOA) of temperature and relative humidity profile
were compiled from the AIRS Data. AIRS Level 2 Standard Product provides 28 level
(0.1 to 1100.00 mb) temperature and moisture profiles. AOT and 20 level atmospheric
profiles between 0.5 and 1000 mb from MODIS Level 2 MOD04_L2 and MYD04_L2
provide AOT between 0.47 and 2.13μm.
3.7.2 Static‐Coordinate
In the static-coordinate, dust aerosols and atmospheric profile contents were
measured staying in the same point for a hurricane’s life span. The static point is selected
at a time point when a hurricane reaches its highest wind speed at lowest pressure. The
41
same methodology was described in the methodology section of this chapter for
measuring AOT, temperature, and humidity during the span of 7 to 10 days for each
hurricanes lifetime was applied. Vertical information from ocean to TOA of temperature
and relative humidity profile from AIRS and MODIS data were compiled. MISR Level 2
aerosol product MIL2ASAE field 'RegBestEstimateSpectralOptDepth' was retrieved to
compare MODIS MOD04 and MYD04 AOT. MODIS (Terra, MOD04 and Aqua,
MYD04) AIRS (AIRS2RET), and MISR (MIL2ASAE) data (Remer, et al., 2005) were
collected from http://ladsweb.nascom.nasa.gov, http://disc.sci.gsfc.nasa.gov and
http://eosweb.larc.nasa.gov ,respectively.
3.7.3 SHIPSDataCollection
SHIPS data was collected based on DeMaria, et al., (DeMaria & Kaplan, 1994)
(DeMaria & Kaplan, 1999) (DeMaria, et al., 2005) and data files can be found via the
Internet (ftp://rammftp.cira.colostate.edu/demaria/SHIPS/).
SHIPS model combines climatology, atmospheric environmental parameters, and
sea surface temperature as its predictors to forecast intensity changes using a multiple
regression scheme (Rosenfeld, et al., 2011).
The NHC of NWS issues public advisories for Atlantic tropical cyclones every six
hours. Based on the NHC website (http://www.nhc.noaa.gov/), Table 3.6 was compiled
to describe the anatomy of the twenty four selected hurricanes between the years 2003
and 2009. The time frame for this selection was chosen for example only.
42
Table 3.6 Twenty four hurricanes were selected between 2003 and 2009 based on the category 3, 4, and 5
Year
CAT
Hurricane
Life Span
Press
(mb)
27 AUG-08 SEP
06-19 SEP
25 SEP-07 OCT
Wind
Speed
(mph)
125
140
110
939
920
952
Start
[LATLON]
14.6-31.5
14.0-34.0
11.7-38.3
End
[LATLON]
49.8-39.2
42.0-80.7
49.3-45.8
2003
2003
2003
4
5
3
FABIAN
ISABEL
KATE
2004
2004
2004
2004
2004
2004
3
4
4
5
3
4
ALEX
CHARLEY
FRANCES
IVAN
JEANNE
KARL
31 JUL-06 AUG
09-15 AUG
25 AUG-09 SEP
02-24 SEP
13-28 SEP
16-24 SEP
105
124
125
145
110
120
957
941
935
910
985
938
30.6-78.6
11.7-61.1
11.2 -36.0
9.7-29.1
16.0-60.4
11.4-32.8
47.5-34.6
43.0-69.0
41.4-79.4
31.0-94.9
37.0-80.3
47.3-40.4
2005
2005
2005
2005
2005
2005
4
4
5
3
5
5
DENNIS
EMILY
KATRINA
MARIA
RITA
WILMA
05-13 JUL
11-21 JUL
23-31 AUG
01-10 SEP
18-26 SEP
15-25 OCT
130
135
150
100
150
150
930
929
902
960
897
882
12.5-63.1
10.8-42.9
23.2-75.5
19.00-46.1
22.0-69.7
17.6-78.8
38.6-86.8
25.0-101.2
41.1-81.6
43.6-38.6
40.8-86.8
41.7-62.8
2006
2006
3
3
GORDON
HELENE
11-20 SEP
12-24 SEP
105
105
955
954
20.2-54.5
12.5-23.0
39.2-16.6
40.9-37.5
2007
2007
5
5
DEAN
FELIX
13-23 AUG
31 AUG-05 SEP
145
145
918
929
12.0-31.6
11.8-58.6
20.5-100.0
14.0-87.0
2008
2008
2008
3
4
4
BERTHA
GUSTAV
IKE
03-20 JUL
25 AUG-04 SEP
01-14 SEP
105
130
125
948
941
935
12.6-22.7
15.5-70.1
17.6-39.5
51.3-35.7
35.6-93.2
36.4-92.5
2009
2009
4
3
BILL
FRED
15-24 AUG
07-12 SEP
115
105
945
958
11.5-34.0
12.5-24.5
48.6-50.2
17.7-33.7
43
Table 3.7 MODIS aerosol retrievals
Description
Effective Optical Depth Best Ocean
Mass Concentration for Best and Average Solutions
Effective Radius of Both Solutions at 0.55 µm
Column Number of Cloud Condensation Nuclei (CCN) of Both Solutions at
0.55 µm
Asymmetry Factor for Best Solution1
Backscattering Ratio of Best Solution1
Mean Reflectances1
1
at 7 bands 0.47, 0.55, 0.66, 0.86, 1.24, 1.63, and 2.13 µm
Name
AOT
MCO
ERO
CCNO
AFBO
BRBO
MRO
Spatial and temporal data products for all hurricanes were collected focusing on
the hurricane’s center while it was moving toward the west and north-west above the
ocean. Hurricanes near landfall were not in the scope of this study.
Table 3.7 has the selected aerosol retrievals (over the ocean only) for the
MOD04_L2 and MYD04_L2 Scientific Data Set (SDS), List of Collection 051 (Tanré, et
al., 1997).
3.7.4 AIRS,MODISandMISRLevel2DataCollection
MODIS Level 2 aerosol product MOD04/MYD04_L2: Format & Content,
Cell_Along_Swath = 203 (typical size), Cell_Across_Swath = 135 (typical size),
MODIS_Band_Ocean = 7, Effective_Optical_Depth_Best_Ocean, AOT for Best Solution
at 7 bands, Dimensions: (MODIS_Band_Ocean, Cell_Along_Swath,
Cell_Across_Swath)
44
AIRS Level-2 Version 005 Standard Products, AIRS2RET, Retrieved Water Vapor
Mass Mixing Ratio Profile, (gm/kg) H2OMMRStd, H2OPressure Level=14 and
Saturation Water Vapor Mass Mixing Ratio Profile over equilibrium phase, (gm/kg)
H2OMMRSat, H2OPressure Level=14.
MISR Level 2 aerosol product, MIL2ASAE was selected because it contains aerosol
optical depth on a 17.6 km grid.10
3.8
ChapterSummary
Physical phenomena of dust aerosols and atmospheric profiles including SHIPS
parameters used in this research are described. Data used in study was selected based on
the following sources:
a) The MODIS Level 2, MOD04/MYD04 products for dust aerosol data.
AOT at 0.55 µm from MODIS level 2 aerosol products (MOD04_L2 and
MYD04_L2) provides AOT between 0.47 and 2.13 μm. MISR Level 2
aerosol products, MIL2ASAE with spatial resolution of 17.6 km grid were
also collected.
b) AIRS level 2 standard products, AIRS2RET with 28 level (0.1 to 1100.00
mb) temperature and moisture profiles. Vertical information from ocean to
TOA of temperature and relative humidity profiles from AIRS data were
compiled.
10
http://eosweb.larc.nasa.gov/PRODOCS/misr/products/level2.html
45
The following chapter discusses the formation of hurricanes in detail and their
effects of the SAL and AEW.
46
CHAPTER FOUR
47
4. OVERVIEW OF HURRICANES
Between chapters 1 and 3, I discussed objectives, theory and experiments of dust
aerosol, atmospheric profiles, data source of aerosol, temperature, and relative
humidity as well as the impacts on hurricanes without touching the introduction to the
hurricanes. In this chapter, therefore, I describe the SAL, AEW (Chen, et al., 2008)
(Berry & Thorncroft, 2004) and formation of hurricanes in detail.
Generally, a mass of very dry air called the SAL forms over the Sahara Desert and
picks up sand and dust particles that act as cloud seeds as it moves continuously northwest toward the Atlantic.11 Simultaneously, moisture rich system, AEW proceeded
over the Atlantic causing a hurricane to build up.
4.1
SaharanAirLayer(SAL)
The role of SAL influencing the atmospheric environment over the tropical Atlantic
has become increasingly acknowledged and documented (Rothman, et al., 2004) for the
last few decades. The SAL occurs primarily from late spring through early fall over
extensive portions of the North Atlantic Ocean between the Sahara desert, the Caribbean,
and the United States (Carlson & Prospero, 1972).12
11
12
http://weather.about.com/od/weatherfaqs/f/hurricanefaq1.htm
http://www.aemichalak.com/work/pdfs/atoc4750.pdf
48
The SAL develops between June and October when there is a flow of maritime air
from the Mediterranean across North Africa (Karyampudi & Carlson, 1988). As the air
pass through the dry African continent, the intense heating introduces dynamic dry
convection within the layer of air which is in convective contact with the ground (Burpee,
1972). Because of the extent of the desert topography and the amount of the solar
heating, a column of air entering the Sahara remains in convective contact with the
ground for a period of at least a few days (Carlson & Ludlam , 1965).13 As it reaches the
coast the bottom rises rapidly with distance to 900m to 2km between Africa and the
Caribbean (Diaz, et al., 1976).
When Saharan dust carried over the main Atlantic hurricane development region and
sits during its life time, a reduction of deep convection and increase in vertical wind shear
occurred and inhibits the formation of hurricane.
4.2
AfricanEasterlyWave(AEW)
AEW originates from west coast of Africa during early spring and late fall in a
location between 5 and 15N. These wave disturbances have a propensity to be the most
powerful in the surrounding area of the coast (Karyampudi & Carlson, 1988).
Wavelength of AEW varies between 2000 to 2500 km and frequency 3-4 days. AEW
propagates towards the west at a speed between 18 and 36 km/h (Burpee, 1974) (Engle,
2001-2003). There are two AEWs that move every week towards North America from
Africa between the months of August and October. The AEW waves decay while passing
13
http://www.aemichalak.com/work/pdfs/atoc4750.pdf
49
through the cool Eastern Atlantic, however bits and pieces of it may survive and
redevelop when reach to the Western Atlantic and Caribbean. Severe thunderstorms and
the impacts of low pressure level winds strengthen the wave upon contact with favorable
atmospheric conditions. Several severe thunderstorms start to form, and ultimately a
tropical storm may develop (Engle, 2001-2003).
4.3
Hurricanes
By definition, a hurricane is a storm system of low-pressure. Most hurricanes
begin in the Atlantic as a result of tropical waves that move westward off the coast of
Africa14,15, in the Intertropical Convergence Zone (ITCZ) (Grodsky & Carton, 2003), an
area of low pressure between 5 and 15°N of the Equator. A category 1 hurricane is born
when a tropical storm reaches a wind speed of 74 mph. Hurricanes that form above the
equator have wind that circulate counterclockwise. A moist environment provides the
necessary energy from the ocean surface, ensuring growth and development. A rapid rise
of warm, moist air from the ocean surface come in contact with cooler air, and condense
into water vapor. This helps to form storm clouds; in turn, heat is released by the system
into the atmosphere. This heat prolongs the condensation process, allowing warmer,
moist air to flow into the brewing storm, thus intensifying and producing a spiral wind
pattern around the eye of the hurricane (Anon., 2012). Generally, intense winds can be
found around the hurricane eye wall, which varies in size, between 20 to 25 miles in
diameter. The winds and rain around a hurricane are strongest on the right side of the
14
15
http://www.usatoday.com/weather/resources/askjack/archives-hurricane-science.htm
http://www.wbir.com/news/article/222015/235/Ask-Todd-Hurricane-movements
50
storm (Aguado & Burt, 2010). In general, hurricanes proceed in a north or northeast
direction from its place of birth, along the southeast coast of North America. It loses all
of its energy within days of reaching landfall.
Studies show that North Atlantic hurricanes move northward at approximately 2030 mph. The winds move in the same rightward direction of the storm track, creating a
net-wind speed between 131 and 155 mph for a Category 4 hurricane (Aguado & Burt,
2010) (NHC, 2012) (Anon., 2012). On the left side of the storm, the net winds move
southward at approximately 93 mph (Nelson, 2012). Despite satellite observations and
numerical weather predictions that have advanced the forecasting of hurricane tracks over
the last two decades, there have been few improvements in forecasting hurricane
strengths (DeMaria, et al., 2005). Hurricanes intensify when there is more precipitation in
the atmosphere and surface moisture differences are the significant contributor to
intensity forecast differences, while convective instability differences play a less
significant role (Sippel & Zhang, 2010).
51
Fiigure 4. 1 Temp
perature at 850 mb for hurrica
ane Frances on 08/28/2004 baseed on AIRS Levvel 2 standard d
data.
Figuree 4.1 is the air
a temperatu
ure at 850 mbb on 08/28/22004 showinng temperatuure
raange between 284° and 304°K.
3
AOT values between 0 and 1.0 around thee hurricane IIsabel on 09//14/2003
reespectively at
a 0.55 µm.
52
70 W 65 W 60 W 55 W 50 W
0.8
30 N
25 N
0.6
20 N
0.4
15 N
0.2
Aerosol Optical Thickness (AOT)
1
0
Figure 4. 2 MODIS Terra Level 2 product showing AOT around the hurricane Isabel at 0.55 µm on 09/14/2003.
35 N
90 W 85 W 80 W 75 W 70 W
1
0.8

30 N
0.6

25 N
0.4
20 N
0.2
15 N
Aerosol Optical Thickness (AOT)

0
Figure 4. 3 MODIS Aqua Level 2 product showing AOT around the hurricane Isabel at 0.55 µm on 09/15/2003.
Figure 4.3 is displaying AOT because they are directly retrieved from the HDF
files. On the other hand RH was calculated based on the data file attributes, therefore, it is
not shown in this dissertation.
53
4.4
ChapterSummary
This chapter concludes with the illustrations of the AIRS temperature at 850 mb,
MODIS AOTs at 0.55 µm from both Terra and Aqua platform. Discussion on the
formation of hurricanes in detail and effects of the SAL and AEW also are included.
Chapter 5 describes the methodology for spatial data collection utilizing concentric
circles concept as well as how do I calculated the response variables “Future Difference
(FD) or Intensity Change” on SHIPS parameters.
54
CHAPTER FIVE
55
5. METHODOLOGY
This chapter introduce the spatial data collection technique utilizing concentric
circles concept as well as how to calculate the response variables “Future Difference
(FD) or Intensity Change” on SHIPS parameters followed by PCA on the combination of
SHIPS parameters and MODIS aerosol retrieval.
5.1
ConcentricCircleswithLeftandRightside
Pixels close to the hurricane center are usually covered by cloud, making it
impossible to retrieve the AOT with MODIS measurements. Thus, for this study, a
unique technique has been established to select spatial coordinates to investigate aerosol
optical thickness, temperature and relative humidity profiles around each hurricane.
Two concentric circles with radii r1 and r2, in Figure 5.1, were drawn with a
common center approximately at the hurricane center.
56
Figure 5. 1 Showing
S
two co
oncentric circless with two regioons left and righ
ht
The spatiial regions fo
or this analysis were choosen betweenn the two concentric circcles
ulus which was
w divided into left andd right side. These two rregions are 1180°
caalled an annu
degrees apart from next adjacent
a
region. The conncentric circles annulus tthickness caan be
djusted by varying
v
the raadii r1 and r2
2.
ad
udy, the inteernal propertiies about thee structure of the hurricaanes are not
In this stu
co
onsidered, th
herefore, r1 and
a r2 were selected as 8 and 5 degrrees respectiively to prodduce
a ring with 3 degrees annulus size. Th
he selected rregion is far away from ccenter of
hu
urricane, butt is still arou
und hurricane edge, and can get enouugh valid rem
mote sensingg
measurement
m
ts for analysiis.
57
The two region spatial technique was applied to retrieve AOT and temperature;
calculate RH from AIRS data files. For each region in Figure 5.1 data were averaged and
then presented for both AIRS and MODIS sensors.
An early study suggested (Carlson & Prospero, 1972) most of the dust is present
between 500 and 850 mb pressure levels. In this study, therefore, temperature and RH
was selected at 500, 700, and 850 mb pressure levels for each AIRS and MODIS data file
for analysis. Results are presented based on the two spatial regions, left and right (Figure
5.1). MODIS AOT data at 0.55 µm and MISR AOT data at 0.558 µm were averaged and
associated with the corresponding RH and temperature data for each of the regions. This
technique has been employed on spatial area for studying hurricanes Isabel, Katrina and
Helene, between the day they formed and the day they dissipated. The center of the
concentric circles corresponds to the approximate location of the hurricane core. The
angle within this region was further spaced out into 18 segments of 10 each. Data for
each 10 segment was retrieved and averaged for this study resulting 18 data points for
each region at a particular time and date. These 18 segment readings were then further
averaged to present final average to demonstrate the values between 0 and 180 (left
side); 180 and 360 (right side). For the dynamic-coordinate analysis, this concentric
circles center was programmed to move with the hurricane center for each day.
AOT from MOD04/MYD04 Level 2 MODIS; temperature, H2OMMRStd and
H2OMMRSat data was retrieved from Level 2 AIRS Standard product for pressure levels
58
from 100 to 1000 mb. RH was calculated from the two AIRS data fields H2OMMRStd
and H2OMMRSat as
RH = (H2OMMRStd/H2MMRSat)*100
AOT, temperature and RH results were then statistically presented in this paper.
MISR Level 2 aerosol product were also selected and compared against MODIS
Level 2 Aerosol products. Temporal and Spatial regions were selected for four Atlantic
hurricanes, Isabel (2003), Frances (2004), Katrina (2005) and Helene (2006).
Datasets were subset by latitude/longitude bounding area (Figure 5.1) constructed on
the spatial interest for the hurricanes and calculate daily average AOT, temperature and
percent relative humidity (RH).
Temporal and spatial data were collected for hurricanes: Isabel between
September 07 and 18, 2003 from (14.50N,-37.70W) to (31.50N, -76.10W); Frances
between August 25 and September 06, 2004 from (11.40N,-37.60W) to (28.60N, 83.30 W); Katrina between August 23 and 29, 2005 from (23.20N,-75.50W) to
(29.70N, -89.60W) and Helene between September 12 and 24, 2006 along (12.5N,23.00W) to (40.90N, -37.50W).
.
59
Figure 5.
5 2 Concentric circles represen
nting Right sidee with 10 segm
ments starting frrom 0ºto 180º
Figuree 5.2, representing conceentric circless for Right side betweenn angles 0 annd
mate locationn of the hurriicane
180. Center of the conceentric circless correspondd to approxim
ore. The ang
gle within thiis region waas further spaaced out intoo 18 segmennts of 10 eacch.
co
Data
D for each
h 10 segmen
nt was retriev
ved and averraged for thiis study resuulting 18 dataa
points for eacch region at a particular time
t
and datte. These 18 segments reeadings weree
hen further averaged
a
to present
p
final average to ddemonstrate the values bbetween 0 aand
th
180; 180 an
nd 360.
Radii of the conceentric circless selected as 5 and 8 deggrees produced 3 degreess
an
nnulus size. For the Dyn
namic-Coord
dinate analyssis this conceentric circless center wass
prrogrammed to move witth the hurricaane center foor each day. The concenntric circles
60
center was fixed while hurricane center was heading toward the west each day for the
fixed space analysis.
AOT from MOD04/MYD04 Level 2; Temperature and Dew Point Temperature
from MOD07/MYD07; temperature, H2OMMRStd and H2OMMRSat data were retrieved
from Level 2 AIRS Standard product for pressure levels from 100 to 1000 mb. MODIS
Temperature and Dew Point Temperature data field and AIRS H2OMMRStd and
H2OMMRSat data were utilized for calculating RH. AOT, temperature and RH results
were then statistically explained and presented in this dissertation.
For validation purposes, MISR Level 2 aerosol product were selected and
compared against MODIS Level 2 Aerosol products. Temporal and Spatial regions were
selected for four hurricanes, Isabel (2003), Frances (2004), Katrina (2005) and Helene
(2006).
5.2
VariableReductionandConcentricCirclesSpatialCoordinate
Combination of SHIPS model (DeMaria & Kaplan, 1994) (DeMaria & Kaplan,
1999) (DeMaria, et al., 2005) and MODIS (http://modisatmos.gsfc.nasa.gov/MOD04_L2/index.html) and (Madhavan, et al., 2008) variables
created a large set of variables. It is difficult to clearly explain the physical phenomena
with such a large number of variables. A reduction of variables in two steps (1)
Correlation Coefficients (cc) and (2) PCA are introduced. The idea is to describe same
meaningful physical phenomena by a smaller set of derived variables which will be linear
61
combinations of the original variables. Reducing the number of variables may lead to
some loss of original information of the dataset. However, PCA makes this loss minimal
and will present a precise meaning without losing original information.
Two concentric circles with radii r1 and r2, as shown in Figure 5.3(a), were drawn
with a common center. These circles were drawn to be approximately at the hurricane
center. The spatial regions for this analysis were chosen between the two concentric
circles called an annulus. The concentric circles annulus thickness can be adjusted by
varying the radii r1 and r2. In this study r1 and r2 were selected as 8 and 5 degrees
respectively to produce a ring with 3 degrees annulus size. The selected region is far
away from center of hurricane, but still around hurricane edge, and can get enough valid
remote sensing measurements for analysis.
The phenomena being investigated are three-dimensional in case of the variables such
as Relative Humidity and Temperature where data is available between sea level and
TOA (between 100 to 1000 mb). Vertical Shear and Wind are also three-dimensional
phenomena. Although MODIS sensor on both the Aqua and Terra satellites provides a
measure of the vertically integrated dust concentration (Braun, S. A., 2010), the vertical
distribution of the dust frequency was not considered in this study. In this study I selected
MODIS Atmospheric retrievals as two-dimension at seven wavelengths (0.47, 0.56, 0.65,
0.86, 1.24, 1.64, and 2.13 µm), therefore, circles being used instead of spheres.
Therefore, for this investigation of aerosol retrievals around a hurricane that involves
"concentric circles" with the hurricane eye is appropriate.
62
I investigaated that the 3 to 4 degree range of annnulus size w
would be ann appropriatee
sp
patial coordiinate selectio
on process because aerossol parameteers were retrrieved around
eaach hurrican
ne by followiing the direcction of motiion of a hurrricane. Sincee linear motion of
a hurricane iss very slow, such as, a hu
urricane's forrward speedd averages arround 15-20 mph
(T
Tan, et al., 2006), selectiing a large annulus
a
size w
would mostlly overlap thhe spatial reggion
while
w
retrievaal happens ev
very day at 1200
1
and 18800 hours. A
Again, selectiing a narrow
w
an
nnulus size such
s
as 1 to 2 degrees would
w
introduuce significaant error while averagingg the
values within
n the annuluss. Therefore, 3 degrees iss the best sellection for thhis study.
(b)
(a)
Fiigure 5. 3 (a) MODIS
M
data wass collected withiin the annulus oof the two conccentric circles w
with radii r1 and
d r2.
(b
b) Data collectio
on following thee motion of a hu
urricane
d at 0.55 µm
µ was averraged in the vvicinity of 1200 and 18000
MODIS aerosol data
hours and asssociated with
h the corresp
ponding SHIIPS data at 1200 and 18000 for each dday.
This
T techniqu
ue was emplo
oyed on a sp
patial area foor studying aall 24 hurricaanes betweenn the
63
day they formed and the day they dissipated. The center of the concentric circles
corresponds to the approximate location of the hurricane core. The angle within this
region was spaced out into 36 segments of 10 each. Data for each 10 segment was
retrieved and averaged resulting 36 data points at a particular time and date. The readings
from these 36 segments were then further averaged to present a final average to
demonstrate the values between 0 and 360. For this analysis, this concentric circles
center was programmed to move with the hurricane center for 1200 and 1800 hours.
The response variables “Future Difference (FD) or Intensity Change” has been
calculated based on the following formula: FDfuture = VMAXfuture – VMAXcurrent, for
example, FD06 = VMAX06 – VMAXcurrent, where VMAX is the maximum 1-minute wind
speed. Similarly, FD12, FD18, FD24, FD30, FD36, FD42 and FD48 are calculated. I started
this analysis by combining the 49 SHIPS parameters from DeMaria, et al., model
(DeMaria & Kaplan, 1994) (DeMaria & Kaplan, 1999) (DeMaria, et al., 2005) with the 7
aerosol retrievals as shown in Table 3.7. Correlation analysis was performed for the
intensity change lead time at 06, 12, 18, 24, 30, 36, 42 and 48 hours (which are basically
the eight response variables between FD06 and FD48). For each FDfuture set, correlation
analysis will be performed with each of the 56 variables to determine the correlation
coefficient (cc) between each variable and the FDfuture. Variables having small correlation
(|cc| < 0.165) were filtered out. These correlation based filtering create the first set of
predictor, Predictor_1 which comprised 31 variables.
64
As the second step of data reduction, PCA is carried out on selected variable
groups. The reduction of variables for each group by PCA is described in Table 3. Prior
to carrying out PCA on the five categories variables were normalized to avoid skewness
caused by units of the variables.
PCA is known as a variable reduction procedure and is useful when variables are
significantly correlated. In each group, the variables describe the same physical
mechanisms. The numbers of same group variables were shrunk to a reduced number of
principal components. Although the details may be different among variables, their
overall trends are the same based on their values. Therefore, using PCA to identify a
reduced number of variables in the same group is a natural step. In this case, AOT, MCO,
CCNO variables reduced to Aero_PC1 and Aero_PC2 for the Aerosol group and
presented in the combination as Predictor_2. Similarly, for the Wind group, V20C U200
U20C TWAC TWXC are reduced to Wind_PC1 Wind_PC2 Wind_PC3 and presented as
a combination of Predictor_3.
There will be some information loss when a variable reduction is performed.
Therefore, when this technique was applied I made sure to select a group of variables
which exhibit similar physical phenomena to minimize the loss of information. I have
analyzed MODIS aerosol retrievals and SHIPS parameters for 24 hurricanes spanning 7
hurricane seasons. By combining MODIS and SHIPS data, 56 variables were compiled
and selected as predictors. Variable reduction from 56 to 31 was performed via
65
correlation coefficients. Among these 31 variables, there are highly correlated or
"redundant" with one another. For example, Sea Surface Temperature, Air Temperature
and Ocean depth of the (20 and 26 deg C) isotherm for the Temperature group are usually
very strongly correlated. Therefore, one or two of these variables or the combination of
the variables (potentially for a newly defined, more representative variable) could be used
as a substitution for all the others. For this study I selected the variable which is most
likely to be the direct cause of categorical response and relevant to the hurricane intensity
studies and of course highly correlated.
Table 5. 1 Reductions of variables by PCA
Category
Aerosol
Wind
Relative Humidity
Shear
Temperature
Variables before PCA
AOT MCO CCNO
V20C U200 U20C TWAC
TWXC
RHLO RHMD R000
SDDC SHDC SHGC SHRD
SHRG SHRS SHTD SHTS
SST T250 T200 RD20 ENEG
ENSS
Variables after PCA
Aero_PC1 Aero_PC2
Wind_PC1 Wind_PC2 Wind_PC3
RH_PC1 RH_PC2
Shear_PC1 Shear_PC2 Shear_PC3
Shear_PC4
Temp_PC1 Temp_PC2 Temp_PC3
Identification and comparison of the impact of this approach on uncorrelated and
correlated variables described in Table 5.2 by considering, for example, Aerosol and
Temperature components. Among the original set of Aerosol variables (AOT AFBO
BRBO MRO MCO ERO CCNO) only (AOT MCO CCNO) were highly correlated. I
have excluded (AFBO BRBO MRO ERO) variables because they were not correlated as
highly as (AOT MCO CCNO). Similarly, original temperature set of variables was (E000
66
EPOS EPSS T000 RD26 T150 SST T250 T200 RD20 ENEG ENSS) and only (SST
T250 T200 RD20 ENEG ENSS) were highly correlated. Uncorrelated variables (E000
EPOS EPSS T000 RD26 T150) were excluded from this analysis. For comparison I
performed PCA on both correlated and uncorrelated variables followed by MLR by the
Predictor_2 and Predictor_6 at FD48. The results are presented in the Table 5.2.
Table 5. 2 MLR results for uncorrelated aerosol and temperature variables
Response Predictors
FD48
Uncorr_Predictor_2
Corr_Predictor_2
Uncorr_Predictor_6
Corr_Predictor_6
R2
64.80%
65.00%
63.40%
63.70%
Adjusted
R2
60.90%
61.10%
60.80%
61.20%
F
value
16.85
17.00
24.69
25.02
RMSE
20.43
20.37
20.45
20.36
Residual
Error
417.30
414.90
418.00
415.00
When comparing the results I see for Predictor_2 and Predictor_6, R2, Adjusted R2
and F Values had decreased for uncorrelated case while Root Mean Square Error
(RMSE) had creased increased. This illustrated that uncorrelated variables had lost more
information than the correlated variables.
For the Relative Humidity group, RH_PC1 RH_PC2 principal components were
extracted from the variables RHLO RHMD R000 and presented as Predictor_4.
Predictor_5 and Predictor_6 were presented similarly for the shear and temperature
groups. For each predictor the combination of variables along with the principal
67
Table 5. 3 Reductions of variables
Predictor
Set Name
Original Set
Predictor_1
Predictor_2
Predictor_3
Predictor_4
Predictor_5
Predictor_6
Variable Set
Action Performed
MSLP INCV SST DTL PHCN U200 U20C
V20C E000 EPOS ENEG EPSS ENSS
RHLO RHMD RHHI PSLV Z850 D200
REFC PEFC T000 R000 Z000 TWAC
TWXC PENC SHDC SDDC SHGC DIVC
T150 T200 T250 SHRD SHTD SHRS SHTS
SHRG PENV VMPI VVAV VMFX VVAC
IR00 IRM3 RD20 RD26 RHCN AOT AFBO
BRBO MRO MCO ERO CCNO
AOT MCO CCNO PENC V20C MSLP
PEFC PSLV U200 U20C Z850 REFC RHLO
RHMD R000 SDDC SHDC SHGC SHRD
SHRG SHRS SHTD SHTS SST T250
TWAC TWXC T200 RD20 ENEG ENSS
Aero_PC1 Aero_PC2 PENC V20C MSLP
PEFC PSLV U200 U20C Z850 REFC RHLO
RHMD R000 SDDC SHDC SHGC SHRD
SHRG SHRS SHTD SHTS SST T250
TWAC TWXC T200 RD20 ENEG ENSS
Aero_PC1 Aero_PC2 Wind_PC1 Wind_PC2
Wind_PC3 PENC MSLP PEFC PSLV Z850
REFC RHLO RHMD R000 SDDC SHDC
SHGC SHRD SHRG SHRS SHTD SHTS
SST T250 T200 RD20 ENEG ENSS
Aero_PC1 Aero_PC2 Wind_PC1 Wind_PC2
Wind_PC3 PENC MSLP PEFC PSLV Z850
REFC RH_PC1 RH_PC2 SDDC SHDC
SHGC SHRD SHRG SHRS SHTD SHTS
SST T250 T200 RD20 ENEG ENSS
From the 56 variables, less
correlated predictors (|cc|<=0.165)
were filtered out to present
Predictor_1 (31 variables)
PCA on AOT MCO CCNO to
reduce them into Aero_PC1
Aero_PC2 make Predictor_2 (30
variables)
PCA on V20C U200 U20C
TWAC TWXC to reduce them
into Wind_PC1 Wind_PC2
Wind_PC3 make Predictor_3 (28
variables)
PCA on RHLO RHMD R000 to
reduce them into RH_PC1
RH_PC2 Predictor_4 (27
variables)
PCA on SDDC SHDC SHGC
SHRD SHRG SHRS SHTD
SHTS to reduce them into
Shear_PC1 Shear_PC2
Shear_PC3 Shear_PC4,
Predictor_5 (23 variables)
Aero_PC1 Aero_PC2 Wind_PC1 Wind_PC2 SST T250 T200 RD20 ENEG
ENSS reduce to Temp_PC1
Wind_PC3 PENC MSLP PEFC PSLV Z850
Temp_PC2 Temp_PC3 make
REFC RH_PC1 RH_PC2 Shear_PC1
Shear_PC2 Shear_PC3 Shear_PC4 SST T250 Predictor_6 (20 variables).
T200 RD20 ENEG ENSS
Aero_PC1 Aero_PC2 Wind_PC1 Wind_PC2
Wind_PC3 PENC MSLP PEFC PSLV Z850
REFC RH_PC1 RH_PC2 Shear_PC1
Shear_PC2 Shear_PC3 Shear_PC4
Temp_PC1 Temp_PC2 Temp_PC3
68
components are shown in Table 5.2. The PEFC REFC Z850 PENC MSLP PSLV was
excluded from PCA because they described diverse physical processes. The PCA
technique can only produce outcomes with very limited benefits from such a data set.
69
CHAPTER SIX
70
6. DATA PROCESSING AND ANALYSIS
The computational part of this research was addressed by writing programs in the
commercially available scientific data analysis and numerical computing tool MatLab.
Fourth generation computer code was developed to retrieve data for each sensor product
(MODIS, AIRS, and MISR). This chapter depicts the steps of how MODIS, AIRS, and
MISR sensors level 2 data were collected, processed, and presented by the data flow
diagrams.
6.1
MODISAerosolandAtmosphericProfilesRetrieval
EODBO data field was selected from MODIS aerosol product swath name ‘mod04’
employing following steps and presented in figure 6.1:
1. AOT values were retrieved from the EODBO data values from each MODIS level
2 HDF data files into a three dimensional 32-bit floating-point array of size
203x135x7 corresponding to the cell along swath, cell across swath and MODIS
ocean band numbers. Simultaneously geo-locations data (longitude and latitude)
are retrieved into a two dimensional array of size 203x135.
2. EODBO data values are then filtered out based on the MODIS ocean band
number ( in this case at 0.55µm) and three dimensional array was converted to
one dimensional array, resulting 203x135 elements of a column vector. Two
dimensional longitude and latitude array were also converted to one dimensional
array resulting 203x135 elements of a column vector for each.
71
Fiigure 6. 1 Flow diagram for EO
ODBO (AOT) retrieval
r
from M ODIS Level 2 data products
72
3. A three dimensional array containing EODBO, LON and LAT column vectors are
then constructed to send them to the main program for further analysis.
4. Each 10° segments contain coordinate values for upper-left, lower-left, upperright and lower-left latitude and longitude information
5. Subset of the above three dimensional array was constructed based on the
coordinate of the hurricane center, inner and outer radius of concentric circles and
values for EODBO, LON and LAT vectors and returned for each 10° segments of
the annulus (Figure 5.5).
6. EODBO, LON, LAT, and corresponding temporal coordinate values were then
stored as workspace variables into MatLab .mat files format used for regression
analysis and plotting various figures presented in this dissertation.
7. Graphs, histograms, fitted lines showed in this dissertation are produced using
MatLab16 and Minitab17 software tools.
A four dimensional array containing temperature, moisture, longitude and latitude
column vectors are then constructed to send them to the main program for further
analysis. Each 10° segments contain coordinate values for upper-left, lower-left, upperright and lower-left latitude and longitude information. Subset of the above four
dimensional array was constructed based on the coordinate of the hurricane center, inner
and outer radius of concentric circles and values containing temperature, moisture,
longitude and latitude vectors and returned for each 10° segments of the annulus (Figure
16
17
http://www.mathworks.com
http://www.minitab.com
73
5.2). Graphs, histograms, fitted lines showed in this dissertation are produced using
MatLab and Minitab software tools.
6.2
AIRSAtmosphericProfilesRetrieval
TAirStd, H2OMMRStd and H2OMMTSat data fields were selected from AIRS
atmospheric profiles product swath name ‘L2_Standard_atmospheric&surface_product’
employing following steps and presented in Figure 6.2:
1. TAirStd, H2OMMRStd and H2OMMTSat data fields were retrieved from each
AIRS level 2 HDF data files into a three dimensional 32-bit floating-point array
of size 45x30x28 corresponding to the cell along swath, cell across swath and
Pressure Levels of Profiles (1100.0 1000.0 925.0 850.0 700.0 600.0 500.0 400.0
300.0 250.0 200.0 150.0 100.0 70.0 50.0 30.0 20.0 15.0 10.0 7.0 5.0 3.0 2.0 1.5
1.0 0.5 0.2 0.1mb). Simultaneously geo-locations data (longitude and latitude) are
retrieved into a two dimensional array of size 45x28.
2. Both temperature and moisture data values are then filtered out based on the
MODIS Pressure Levels of Profiles (in this case at 500, 700 and 850mb). A three
dimensional array was converted to one dimensional array, resulting 45x28
elements of two column vectors. Two dimensional longitude and latitude array
were also converted to one dimensional array resulting 45x28 elements of a
column vector for each.
74
AirStd, H2OMM
MRStd and H2O MMTSat retrieval from AIR
RS Level 2 data
Fiigure 6. 2 Flow diagram for TA
prroducts
75
A four dimensional array containing temperature, moisture, longitude, and latitude
column vectors are then constructed to send them to the main program for further
analysis. Each 10° segments contain coordinate values for upper-left, lower-left, upperright and lower-left latitude and longitude information. Subset of the above four
dimensional array was constructed based on the coordinate of the hurricane center, inner
and outer radius of concentric circles and values containing temperature, moisture,
longitude and latitude vectors and returned for each 10° segments of the annulus (Figure
5.5). Temperature, moisture, longitude and latitude and corresponding temporal
coordinate values were then stored as workspace variables into MatLab .mat files format
used for regression analysis and plotting various figures presented in this dissertation.
Graphs, histograms, fitted lines showed in this dissertation are produced using MatLab
and Minitab software tools.
6.3
MISRAerosolRetrieval
RegBestEstimateSpectralOptDepth data field
(http://eosweb.larc.nasa.gov/PRODOCS/misr/Quality_Summaries/L2_AS_Products_200
51201.html) was selected from MISR aerosol product grid name ‘RegParamsAer’
employing following steps and presented in figure 6.3:
1. AOT values were retrieved from the RegBestEstimateSpectralOptDepth data
values from each MISR level 2 HDF data files into a four dimensional array of
size 180x8x32x4 corresponding to Space Oblique Marketer (SOM), number of
lines, number of samples and MISR four band numbers. Simultaneously geo-
76
locations data (longitude and latitude) are retrieved into a two dimensional array
of size 8x32 utilizing the corresponding XML file and MISR Ancillary
Geographic Product (AGF) HDF file.
2. AOT data values are then filtered out based on the MISR band number (in this
case at 0.55µm) and three dimensional array was converted to one dimensional
array, resulting 8x32 elements of a column vector. Two dimensional longitude
and latitude array were also converted to one dimensional array resulting 8x32
elements of a column vector for each.
3. A three dimensional array containing AOT, LON, and LAT column vectors are
then constructed to send them to the main program for further analysis.
4. Each 10° segments contain coordinate values for upper-left, lower-left, upperright and lower-left latitude and longitude information
5. Subset of the above three dimensional array was constructed based on the
coordinate of the hurricane center, inner, and outer radius of concentric circles
and values for AOT, LON, and LAT vectors and returned for each 10° segments
of the annulus (Figure 5.5).
6. AOT, LON, LAT, and corresponding temporal coordinate values were then stored
as workspace variables into MatLab .mat files format used for regression analysis
and plotting various figures presented in this dissertation.
7. Graphs, histograms, fitted lines showed in this dissertation are produced using
MatLab and Minitab software tools.
77
Figure 6. 3 Flow
w diagrams for RegBestEstima
ateSpectralOptD
Depth retrievall from MISR Level 2 data prod
ducts
78
6.4
PrincipalComponentAnalysis(PCA)
PCA is known as a variable reduction procedure and is useful when variables are
significantly correlated. In each group, the variables describe the same physical
mechanisms were shrunk to a reduced number of principal components. Although the
details may be different among the variables, their overall trends are the same based on
their values. Therefore, using PCA to identify a reduced number of variables in the same
group is a natural step. There will be some loss of information when a variable reduction
is performed, therefore, when this technique was applied I made sure to select group of
variables which exhibit similar physical phenomena to minimize loss of information.
About 3GB of data files for each (MOD, MYD, MISR, AIRS) were selected for this
study and following the steps below to perform the PCA calculation.
Step 1: I normalized the data by subtracting mean and dividing by standard deviation.
Step 2: Determine the matrix
Step 3: Find the eigenvectors and eigenvalues of the matrix
Step 4: Order the eigenvectors highest to lowest by eigenvalue
Step 6: Select the most significant components with large eigenvalues which reduces
the dimensions.
6.5
LassoandElasticNetVariableShrinkageMethods
In addition to the PCA technique, I used a regularization and shrinkage technique
including Least Absolute Shrinkage and Selection Operator (Lasso) (Tibshirani, 1996)
and Elastic net (Zou & Hastie, 2005) as an alternative method in this dissertation.
79
Lasso is a regularization method to determine linear regression for highly correlated
predictors. This method tends to pick one from a same group of correlated predictors and
ignore the rest. Therefore, this method sets more coefficients value to zero as the penalty
term grows. Therefore, lasso may be an alternative to PCA and other dimensionality
reduction methods.
The Elastic net penalty is a compromise between the ridge regression penalty ( = 0)
and the lasso penalty ( = 1). This technique can also perform dimensionality reduction
by generating zero-valued coefficients. A study suggested (Friedman, Hastie, &
Tibshirani, 2010) this technique can handle highly correlated predictors much efficiently
than the Lasso and Ridge techniques.
Based on (Mathworks, 2012) and (Tibshirani, 1996), the Lasso method solves
regularization problem for a given value of . Also based on Zou and Hastie, (Zou &
Hastie, 2005) , the Elastic Net technique solves regularization problem for a  strictly
between 0 and 1, and a nonnegative .
80
Table 6. 1 Parallel Computing Results for Lasso and Elastic Net Analysis
Hardware and
Operating
System
Intel i3-M370
processor
2.4GHz with 2
cores, 8GB
RAM. Windows
7 Home Premium
Intel i5-2400S
processor
2.5GHz with 4
cores, 6GB
RAM. Windows
7 Home Premium
Lasso for 56 variables, 5 fold
and 100 





Regular computing
cost 261.509242
seconds
Parallel computing is
turned on and with 2
labs cost 187.531574
seconds
Regular computing
cost 184.093275
seconds
Parallel computing is
turned on and with 2
labs cost 115.933410
seconds.
With 4 labs cost
90.528875 seconds.
Elastic Net for 56 variables, 5
fold,  equal 0.5 and 100 





Regular computing cost
60.759665 seconds
Parallel computing is turned
on and with 2 labs cost
38.075036 seconds
Regular computing cost
48.904063 seconds
Parallel computing is turned
on and with 2 labs cost
27.752173 seconds.
With 4 labs cost 21.986734
seconds.
Table 6.1 shows the regular and parallel computing analysis for a cross-validated
sequence of models with Lasso and Elastic Net analysis. Two different processors were
used to measure computing power to demonstrate the Lasso and Elastic Net operations.
The Intel i5-2400S processor 2.5GHz with 4 cores demonstrated best performance when
using all four cores, that is parallel computing is turned on with 4 labs. When compared
to the Lasso against the Elastic net operations in the four labs scenarios, CPU utilizations
were found to be four times larger in Lasso than Elastic Net.
81
CHAPTER SEVEN
82
7. RESULTS AND DISCUSSIONS
The significance of dust aerosols in climate and its impact on atmospheric parameters
such as temperature and moisture have been very attractive in the scientific community
for the last three decades. Several sensors aboard the NASA Earth Observing System
(EOS) satellites such as MODIS, MISR and CALIPSO are suitable for aerosol dust
measurements because of their excellent spectral, spatial, and temporal resolutions
(Seemann, et al., 2006) (Diner, et al., 1998) (Xie, et al., 2010). Additionally, AIRS
sensors are excellent source for atmospheric temperature and moisture measurements.
Results gathered based on the Methodology (chapter 5) and Data Processing and Analysis
(chapter 6) are presented in this chapter in a systematic way.
7.1
DynamicandStatic‐CoordinateAnalysisforFourHurricanes
Dust aerosols, temperature and RH were observed for static and dynamic
coordinate scenarios. The idea is to find a pattern as well as the influences by dust on air
temperature and RH profiles. Reduction of the RH was observed due to the increase in
dust AOT levels. Dust aerosol impacts negatively on the atmospheric moisture levels.
Dust aerosol impacts atmospheric temperature positively, thereby increasing AOT levels,
which fuel the temperature to increase.
83
However, an increase in temperature is not as significant as AOT impacts when
looking at reducing the moisture level. The magnitude of dust influences are the same for
both dynamic-coordinate and static-coordinate observations.
Hurricane Isabel reached the Outer Banks of North Carolina as a Category 2
hurricane with winds of 105 mph on September 18, 2003. I found average AOT 0.20 for
dynamic coordinate during its lifetime. Frances made landfall in the Gulf of Mexico and
the Florida Panhandle on September 04, 2004. Average AOT for Frances was 0.17 for
dynamic coordinate. On August 28, 2005, Katrina was a Category 5 storm on the SaffirSimpson hurricane scale with winds speed approximately 150 mph and make landfall in
the Gulf of Mexico. Katrina created the most damages and carried the lowest amount of
dust, with an average AOT of about 0.16 for dynamic coordinate. Hurricane Helene did
not reach landfall and had sustained winds of 110 mph. AOT for the Helene was highest
0.27 for dynamic coordinate. AOT values here are the average on both regions and
reported in Table 7.1.
Table 7. 1 Average AOT values in dynamic and static coordinate’s presentation for four hurricanes
AOT
Dynamic-coordinate
Hurricane MISR
MODIS
0.23
0.20
Isabel
0.18
0.17
Frances
0.16
0.16
Katrina
0.27
0.20
Helene
84
Static-coordinate
MISR MODIS
0.17
0.18
0.14
0.14
0.19
0.13
0.20
0.18
The daily average values for Level 2 MODIS MOD04 and MYD04 AOT at 0.55
µm have been calculated and compared against MISR Level 2 aerosol product
(MIL2ASAE) values. In Table 7.1, static and dynamic-coordinates AOT values from
MODIS and MISR are presented. Differences in AOT values between MODIS and MISR
are negligible except for Helene’s dynamic- coordinate values for MISR data. Among the
four hurricanes, Isabel, and Katrina reached category 5 strengths, however, the most
severe damage was caused by Katrina.
Table 7.1 shows AOT values for each hurricane utilizing level 2 aerosol data from
MODIS sensor on the peak day of each hurricane. Hurricane Isabel became a Category 5
on September 12, 2003; Frances reached as a Category 4 on September 01, 2004;
hurricane Katrina at peak strength on August 28, 2005 and hurricane Helene near peak
intensity was on September 18, 2006. Figure 7.1 and 7.2 illustrates AIRS Temperature,
RH at 500, 700, and 850mb and MODIS AOT at 0.55μm on those peak days by only
selecting static coordinate for the analysis and left and right side data was combined to
calculate the average value.
85
Fiigure 7. 1 AIRS
S Temperature at 500 (Red), 70
00 (Green) and 850 (Purple) m
mb and MODIS AOT (Red triaangles)
att 0.55 μm on thee peak day of ea
ach hurricane
Fiigure 7. 2 AIRS
S RH at 500 (Reed), 700 (Green)) and 850 (Purp
ple) mb and MO
ODIS AOT (Red
d triangles) at 00.55
μm
m on the peak day
d of each hurrricane
Katrin
na exhibits the
t highest temperature
t
and RH whhile AOT taakes a nose dive,
when
w
compaared with th
he other hu
urricanes tim
me frame. Moreover, significant AOT
variations at about
a
50% had been ob
bserved durinng Isabel annd Katrina. T
Temperature, RH,
an
nd AOT haave been sh
hown for eaach hurricanne on theirr peak day. Only the static
86
coordinate were selected for the analysis; left and right side was combined to calculate
the average values. Red triangles represent AOT values for the hurricanes, with Katrina
conveying the lowest AOT value.
I broke down the analysis further to represent the left and right side within the
static coordinate, as well as the dynamic coordinate scenarios. Table 7.2 and Table 7.3
correspond to the average AOT, RH and Temperature of both sides for hurricanes Isabel
2003, Frances 2004, Katrina 2005 and Helene 2006. AIRS temperature and RH are at
500, 700, and 850 mb while MODIS AOT is at 0.55 μm.
Higher AOT values were observed for Isabel when considering the right side of
the dynamic coordinates. Also, for dynamic coordinates, the right side AOT of Helene is
lower than left side. For static coordinates, left side AOT values are higher for Isabel,
Frances and Katrina. This might be due to the fact that the wind is in the same direction
of the storm track along the right-hand side. Hence there is a net-wind speed
corresponding to Saffir-Simpson hurricane scale category and its wind speed (NHC,
2012). For hurricane Frances, on the right side, the AOT value difference between static
(Table 7.2, =0.13) and dynamic (Table 7.3, =0.20) coordinates was maximum (0.07).
When the AOT values for each hurricane were averaged and plotted in Figure 7.3,
the lowest amount of dust for dynamic and static for both left and right side in case of
87
hurricane Katrina in 2005 was observed. Isabel and Helene contained the highest amount
of dust for the dynamic and static coordinate, as well as the left and right side scenarios.
Table 7. 2 AOT, RH and temperature (in ºK) on the left and right side of each hurricane averaged for staticcoordinate.
Hurricane
Isabel
Frances
Katrina
Helene
Pressure
mb
500
700
850
500
700
850
500
700
850
500
700
850
Static-Coordinate
Left Side
Right Side
AOT
RH
Temp
AOT
RH
Temp
0.19 24.86 266.53
0.17 28.65 265.94
43.73 281.87
50.11 281.55
63.71 290.57
63.62 289.44
0.16 35.21 266.25
0.13 31.65 266.35
50.88 282.18
47.13 282.08
64.87 290.72
66.52 289.94
0.13 48.66 267.91
0.12 48.36 267.85
68.42 283.22
64.48 283.41
72.73 292.26
71.00 291.92
0.17 22.47 267.09
0.18 23.36 267.06
40.39 282.33
42.13 282.26
51.00 290.37
49.34 289.97
On September 12, 2003, the peak day of the hurricane Isabel, as in Figures 7.3
and 7.4, lower AOT (0.16) on the right side and higher AOT (0.21) on the left side for
dynamic coordinate were found. The temperature was lower (282.51K) for dynamic
coordinate on the right side while higher (283.26K) on the left side. For static
coordinate, temperature was 282.39K on the right and 283.02K on the left side. RH for
dynamic coordinate was 42.86 on the right and 45.17 on the left while for static
coordinate it was 37.20 on the left and 39.91 on the right side.
88
Figurre 7. 3 MODIS dynamic and sttatic AOT at 0.555 μm for the leeft side of each hurricane
Figuree 7. 4 MODIS dynamic
d
and sta
atic AOT at 0.5 5 μm for the rigght side of each
h hurricane
89
Table 7. 3 AOT, RH and temperature (in ºK) on the left and right side of each hurricane averaged for dynamiccoordinate.
Hurricane
Isabel
Frances
Katrina
Helene
Pressure
mb
500
700
850
500
700
850
500
700
850
500
700
850
AOT
0.19
0.14
0.17
0.21
Dynamic-Coordinate
Left Side
Right Side
RH
Temp AO
RH
Temp
T
30.13 267.51 0.21 38.99 267.34
47.35 282.68
54.84 282.27
61.24 290.76
65.58 290.49
22.74 267.49 0.20 26.52 267.56
50.31 282.84
54.3
282.34
61.49 291.18
60.86 290.73
45.34 268.62 0.15 51.11 268.08
64.41 283.40
62.89 282.19
70.58 291.52
70.63 291.15
22.47 266.53 0.20 28.6
267.27
40.39 282.97
53.52 282.48
43.41 290.49
54.36 290.54
When considering the peak day (September 1, 2004) of hurricane Frances, lower
AOT (0.15) in the left side while higher (0.23) in the right side for dynamic coordinate
was found. Values for static coordinate AOT were 0.16 and 0.25 for the left and right
sides. The temperature was lower (281.56K) for dynamic coordinate on the right side
while higher (282.56K) on the left side. For static coordinate, temperature was 282.03K
on the right and 283.73K on the left side. RH for dynamic was 57.27 on the right and
46.03 on the left while for static it was 54.71on the left and 43.06 on the right side.
On August 28, 2005, the peak day of hurricane Katrina, the same amount of AOT,
0.15, both in the left side and the right side for dynamic coordinate was found. Values for
90
static coordinate AOT were 0.17 and 0.20 for the left and right sides. Temperature was
lower (283.53K) for dynamic coordinate on the right side while higher (282.42K) on
the left side. For static coordinate, temperature was 283.54K on the right and 282.81K
on the left side. RH for dynamic was 69.52 on the right and 58.70 on the left while for
static it was 68.0 on the left and 68.04 on the right side.
For the peak day (September 18, 2006) of hurricane Helene, lower AOT (0.10) in
the left side while higher (0.17) in the right side for dynamic coordinate was found.
Clearly, for the left side and right side AOT, temperature and RH of the four
hurricanes show maxima and minima for both static and dynamic coordinate scenarios,
which exhibits opposite cyclic phenomena. Figure 7.5 shows such cases for hurricane
Isabel in 2003 for AOT and atmospheric profile at 700 mb.
91
Figure 7. 5 MODIS AOT at 0.55 m and AIRS
A
RH and t emperature at 700 mb for left and right side of
hurricane Isabel (2003) showing dyn
namic and staticc conditions
92
7.2
7
AOTDistributiionforthe
eLeftandtheRightside
The circlee in Figure 7.6
7 shows thee regions bettween the tw
wo concentric circles whhich
iss an annulus and divided
d into a left and
a a right siide (either siide of the redd vertical linne).
These
T
two reg
gions are 180° degrees apart
a
from neext adjacent region. Eachh side was
fu
urther segmeented equally
y (30° each) into six regions and maarked as I, II,, III, IV, V aand
VI.
V
Figure 7. 6 Two concentric circles witth regions left aand right and fu
urther segmentted 30° each.
To determine wheether two datta sets originnated from thhe same poppulations witth
ommon distrribution scieentist often use
u the quanttile-quantile (q-q) plot. The followinng
co
illlustration deepicts such plots
p
to comp
pare q-q plott of the segm
ment V of thee left and rigght
siide for hurriccanes Francees (2004) an
nd Helene (2006). Figurre 7.7 illustraates a q-q ploot of
93
th
he quantiles of the left siide data set against
a
the qquantiles of tthe right sidee data sets off
AOT
A
distribu
ution for the hurricane
h
Frrances. A 455° reference lline (red) as well as a bluue
liine which paasses through
h the 1st and 3rd quantiless has been plotted.
Figure 7. 7 Histograms and
d QQ plots for Segment V of F
Frances showingg Static and Dyynamic scenarioos
In Fig
gures 7.8, 7.9
9, and 7.10, the
t right sidee plots, the y axis is the estimated
qu
uantiles from
m left side AOT
A
and the x axis is thee estimated qquantiles from
m right side
AOT.
A
A point on the QQ plot is the quantile
q
for bboth axes buut not real quuantile. The
saample size for
fo the left an
nd right side was not equual, thereforee, the quantiiles are seleccted
co
orrespond to
o the sorted values
v
from the smaller ssample size and quantilee for large
saample was in
nterpolated.
94
Figure 7. 8 Histo
ograms and QQ
Q plots for Segm
ment V of Helenee showing Statiic and Dynamicc coordinate sceenarios
Figure
F
7. 9 Histtograms and QQ
Q plots for Leftt and Right sidee of Isabel show
wing Static and D
Dynamic coord
dinate
scenarios
95
Figure
F
7. 10 Hisstogram and QQ
Q plot for Left and Right side of Helene show
wing Static and Dynamic coord
dinate
scenarios.
In Fig
gures 7.8, 7.9
9, and 7.10, the
t left side plots represent the histoograms for leeft
(b
blue) and rig
ght (green) siide of the distribution. T
The data sets, in this casee, Dynamic-L
Left,
Dynamic-Rig
D
ght, Static-Leeft and Staticc-Right havee common loocations, scaales, and
distributionall shapes with
h similar tail behaviors. The histograam from all these cases
when
w
consideering the ind
dependent seg
gment as weell as the enttire left and eentire right
reegions shows the populaation being sampled is skkewed to thee right and fits as lognorm
mal
distribution as in Figure 7.11
7 and 7.12
2.
96
0.00 0.15 0.30 0.45 0.60 0.75 0.90
Static-Left
Static-Right
Frequency
4000
4000
3000
3000
2000
2000
1000
1000
0
0
Dynamic-Left
3000
3000
2000
2000
1000
1000
0
Dynamic-Right
0
0.00 0.15 0.30 0.45 0.60 0.75 0.90
Figure 7. 11 Histogram and Lognormal distribution curve (Blue) for the hurricane Isabel showing Left and
Right side Static and Dynamic coordinate scenarios
0.00 0.15 0.30 0.45 0.60 0.75 0.90
6000
Static-Left
Static-Right
4000
Frequency
4500
3000
3000
2000
1500
1000
0
0
Dynamic-Left
3000
3000
2000
2000
1000
1000
0
Dynamic-Right
0
0.00 0.15 0.30 0.45 0.60 0.75 0.90
Figure 7. 12 Histogram and Lognormal distribution curve (Blue) for the hurricane Helene showing Left and
Right side Static and Dynamic coordinate scenarios
97
Table 7.4 shows the lognormal distribution results for all four hurricanes when
entire left (0° to 180°) and right (180° to 360°) side was considered. Location, scale and
sample size, N is also provided. Location values showed negative values across the
hurricanes and scale is between 0.30 and 0.84.
Table 7. 4 Histogram (Lognormal distribution) analysis for four hurricanes
Static-Left
Static-Right
Dynamic-Left
DynamicRight
Isabel
Loc
Scale
N
Loc
Scale
N
Loc
Scale
N
Loc
Scale
N
-1.870
0.6102
26046
-2.017
0.8354
27918
-1.713
0.4826
20865
-1.605
0.4832
18381
Frances
Loc -2.001
Scale 0.4777
N
21445
Loc -2.145
Scale 0.6017
N
22964
Loc -1.999
Scale 0.4091
N
23901
Loc -1.868
Scale 0.3790
N
18240
Katrina
Loc -1.946
Scale 0.5622
N
4807
Loc -2.143
Scale 0.5566
N
2026
Loc -1.833
Scale 0.5263
N
3736
Loc -2.098
Scale 0.3011
N
2797
Helene
Loc -1.947
Scale 0.6003
N
26835
Loc -1.919
Scale 0.5945
N
23158
Loc -1.929
Scale 0.6608
N
20481
Loc -1.626
Scale 0.3740
N
14558
When investigating the skewness, positive values for all cases except the StaticRight scenario of Katrina was observed. Positive and negative values for the skewness
correspond to the data that are skewed right and left. Therefore, the Static-Right scenario
of Katrina shows data skewed to the left.
Table 7.5 shows the lognormal distribution results for segment III and V for four
hurricanes. Location, scale and sample size, and N are also provided. Location values
showed negative values across the hurricanes and the scale is between 0.32 and 0.87. As
98
it can be seen the scale parameter is less than 1 for all cases here. This is the indication of
a compressed probability density function (pdf) which may have a spike as the scale
parameter approaches to zero.
Table 7. 5 Histogram (Lognormal distribution) analysis for several segments 3 and 5 for all four hurricanes
Static-LeftIII
Static-RightIII
Static-Left-V
Static-RightV
DynamicLeft-III
DynamicRight-III
DynamicLeft-V
DynamicRight-V
7.3
Isabel
Loc
Scale
N
Loc
Scale
N
Loc
Scale
N
Loc
Scale
N
Loc
Scale
N
Loc
Scale
N
Loc
Scale
N
Loc
Scale
N
-1.630
0.5439
3558
-1.492
0.6013
1673
-1.857
0.4537
5641
-1.546
0.3837
4345
-1.778
0.7434
4344
-2.167
0.9566
2572
-1.891
0.6590
6413
-1.798
0.8682
5987
Frances
Loc -2.013
Scale 0.3568
N
4253
Loc -1.980
Scale 0.3621
N
1061
Loc -2.025
Scale 0.4488
N
7439
Loc -1.862
Scale 0.3187
N
5525
Loc -1.995
Scale 0.4079
N
2979
Loc -2.342
Scale 0.5603
N
2228
Loc -1.902
Scale 0.3769
N
5719
Loc -1.859
Scale 0.4126
N
4973
Katrina
Loc -1.841
Scale 0.7536
N
644
Loc -2.021
Scale 0.3036
N
427
Loc -1.843
Scale 0.5541
N
1218
Loc -1.964
Scale 0.1824
N
178
Loc -1.476
Scale 0.3978
N
657
Loc -2.416
Scale 0.6569
N
93
Loc -1.997
Scale 0.5331
N
1598
Loc -1.968
Scale 0.3985
N
1033
Helene
Loc -2.122
Scale 0.7187
N
3193
Loc -1.597
Scale 0.2952
N
882
Loc -1.929
Scale 0.6850
N
6424
Loc -1.573
Scale 0.2518
N
4897
Loc -2.145
Scale 0.5404
N
3071
Loc -2.091
Scale 0.6579
N
2074
Loc -1.839
Scale 0.5814
N
8320
Loc -1.732
Scale 0.5542
N
5156
AIRSTemperatureandRHDifference
Temperature and RH difference were calculated for each hurricane based on the
“pre-hurricane” and “post-hurricanes” scenarios. The day in which a hurricane acquires
99
its highest wind at its lowest pressure is considered “during-hurricane” or the reference
point to calculate the “PRE” and “POST” temperature and RH profile difference. The
“pre-hurricane” and the “post-hurricane” are the temporal data collection start and end
dates respectively from the “during-hurricane” reference point. Figure 7.13 presents
vertical plots for temperature and RH profile difference at pressure levels from 500 and
1000 mb based on the AIRS data products. “PRE” which is [=”during-hurricane” - “prehurricane”] and “POST” [=”during-hurricane”-“ post-hurricane”] scenario for Isabel,
Frances, Katrina and Helene is presented. In Figure 7.6, upper-left and lower-left plots
are corresponding to Isabel and Katrina and upper-right and lower-right are
corresponding to Frances and Helene. The “PRE” and “POST” curve for RH difference
intersects at 550 and 925 mb for Isabel; and intersects at 600 and 750 mb for Frances.
The highest “POST” RH difference observed at 700 mb for hurricane Katrina,
2005 (lower left figure) and the lowest for hurricane Frances, 2004 (upper right figure).
Also negative RH difference in “PRE” and “POST” scenarios for hurricanes Isabel and
Frances and positive values for hurricanes Katrina and Helene were observed.
Temperature difference at 700 mb overlapped for hurricanes Frances and Katrina.
Temperature and RH difference between pressure levels 500 and 1000 mb showed small
variation (~2ºK) when compared to the significant change in RH (~15).
100
Figure
F
7. 13 Tem
mperature and RH profile diffference for Isab
bel, Frances, Kaatrina and Helene from the preessure
levells 500 and 1000 mb showing “P
PRE” (Blue) an
nd “POST” (Grreen) hurricane scenario.
ur cases, RH ranging in ppressure leveels from 5000 to 1000 mbb
Overaall for all fou
sh
howed consiistent variatiion within th
he vicinity off 15 units; hoowever, tem
mperature
difference ran
nge was 2K
K and consisttent in all fouur events.
Therefore, profilee differences for all four events negleecting the vaalues closer tto
th
he surface deemonstrates positive for temperaturee and negativve for RH. T
The positive
101
temperature difference exhibits warmer and negative RH difference exhibits dryer
condition for both “PRE” and “POST” conditions.
As demonstrated in Figure 7.14, dust aerosol impacts negatively on the
atmospheric moisture levels, thereby reducing moisture levels are observed for increasing
dust AOT levels when considering the scatter plot for static coordinates. Dust aerosol
impacts atmospheric temperature positively, thereby, increasing AOT levels influence the
temperature to increase. However, increase in temperature is not as significant as AOT
impacts reducing the moisture level. For dynamic coordinates results did not follow the
same trend.
102
Figure 7. 14 Scatter plot MIO
ODIS AOT at 0.55 m and AIR
RS RH and tem
mperature at 7000 mb for hurriccane
Isabel (2003
3), Frances (200
04), Katrina (20
005) and Helenee (2006) selectin
ng only the statiic coordinate an
nd
combining Terra,, Aqua as well as left and righ
ht sides.
Table 7. 6 Regression
R
equa
ations for each h
hurricane base d on Figure 7.114
anes
Hurrica
Isabel
Frances
Katrina
Helene
Regreession Equa
ations
Temp
p = 281 + 4.8
83*AOT andd RH = 57.2 - 20.7*AOT
T
Temp
p = 282 + 0.8
88*AOT andd RH = 67.99 - 38.8*AO
OT
Temp
p = 282 + 2.6
66*AOT andd RH = 95.44 – 120*AO
OT
Temp
p = 281 + 4.6
68*AOT andd RH = 57.66 - 12.0*AO
OT
Table 7.6 presentts the regresssion equatioons for eachh hurricane. Insignificannt R2
und for all the
t four casees. This is ddue to the faact that hurriicane dependds on
values are fou
d aerosols and atmospheric profi les but also other variabbles such as wind
not only the dust
sp
peed, verticaal shear, sea surface teemperature, and relativee vorticity. These addittional
103
parameters were not considered in this study. Future work will be performed to consider
more variables along with moisture, temperature and AOT to investigate the dependency
of hurricane intensity.
7.4 VariableReductionAnalysisViaPCA
As shown in Table 7.7 PCA for AOT, MCO and CCNO the cumulative results
explain the variability for the first two components as 80.4% and 98.4%. For V20C U200
U20C TWAC TWXC, the top three principal components demonstrate a variability of
52.8%, 82.8%, and 98.4%. When PCA was performed on RHLO RHMD R000, the
cumulative variability for the first two components as 66.67% and 95.20% was found.
PCA for SDDC SHDC SHGC SHRD SHRG SHRS SHTD SHTS gives variability for the
four components as 50.7%, 72.0%, 81.4%, and 88.8%. For SST T250 T200 RD20 ENEG
ENSS, PCA results give variability for the first three components as 58.5%, 76.1%, and
91.2%.
Dimensionality reduction infers loss of information; therefore, the goal is to preserve
as much information as possible by minimizing the difference between the higher
(original) and the lower dimensional variables representation. One of the commonly used
methods to determine lower dimensional variables is principal component analysis. This
is where the principal components, linear combination of the originals, ranked based on
the contribution to the total variance, are chosen as new variables. The first few new
104
variables are responsible for interpreting most of the physical phenomena described by
the original variables that have been reduced.
Two Aerosol principal components explain 98.4% (loss = 1.6%) variability when
variable reduction happened from three to two. For Wind, five variables were reduced to
three principal components resulted cumulative variability of 98.4% (loss = 1.6%). When
PCA was performed on three Relative Humidity variables, it gave the cumulative
variability for the two principal components as 95.20% (loss = 4.8%). PCA for eight
Shear variables gives variability for the four reduced components as 88.8% (loss =
11.2%). Six Temperature variables were reduced to three components with 91.2% ((loss
= 8.8%)) cumulative variability.
Reducing the variables does not always lead to a better result, but it is comparable.
Reduction of variables removes irrelevant features and dampens noise; it also leads to
more comprehensible model because the model involves fewer variables (Tan, et al.,
2006).
For the aerosol category the first two components have the proportionality of 0.80
and 0.18 respectively. Most of the weight is on the Aero_PC1 component which is about
four times larger than Aero_PC2. In the case of the Wind category, the first component
is less than two times the second component and over three times larger than the third
component. The proportion for Humidity shows the first component is twice as large as
105
the second component. Shear has a proportion of about 51% for the first component. The
first component of the temperature has about 59% weight. When comparing the first
component of the Aerosol, Wind, Relative Humidity, Shear, and Temperature it was
found that aerosol had the highest proportion followed by Humidity, Temperature, Wind
and Shear. Therefore, aerosol might have some influence based on the first component
comparison.
In Table 7.7, the cumulative variability percentage for each extracted component
is presented, where the cumulative % threshold was set at 88%.
Multiple Linear Regression (MLR) technique was applied for the model forecast
lead time of 06, 12, 18, 24, 30, 36, 42 and 48 hours. For each FDfuture, six predictor sets
(Predictore_1 through Predictor_6) variables were analyzed. Table 7.8 shows the
common measures of MLR, and from this table, it can be seen for FD06, R2 varied
between 15.0% and 18.8% which is about 25.3% variation. For FD12, R2 varied between
24.4% and 27.8% which is about 22.7% variation. The smallest variation for FD48 is
9.3% between the highest and lowest values.
Let the FD48 be the response and explanatory variables as Original set of 56,
Predictor_1 as 31 and Predictor_6 as 20. For the Original variables, 55 degrees of
freedom (DF) provide with the RMSE = 19.47, R2= 71.1%, R2 (adj) = 64.5%, F =
10.72 and P = 0.000. For Predictor_1, DF = 30, RMSE = 20.38 R2= 65.1%, R2 (adj) =
106
61.1%,
F = 16.46 and P = 0.000. For Predictor_6, DF = 19 RMSE = 20.36 R2 =
63.7% R2 (adj) = 61.2%
F = 25.02 P = 0.000.
Table 7. 7 Principal components for Aerosol, Wind, Relative Humidity, Vertical Shear and Temperature
parameters
Category
Component
Aerosol
Aero_PC1
Aero_PC2
Wind_PC1
Wind_PC2
Wind_PC3
RH_PC1
RH_PC2
Shear_PC1
Shear_PC2
Shear_PC3
Shear_PC4
Temp_PC1
Temp_PC2
Temp_PC3
Wind
Relative
Humidity
Vertical
Shear
Temperature
Cumulative %
Variability
80.40%
98.40%
52.80%
82.80%
98.40%
66.67%
95.20%
50.70%
72.00%
81.40%
88.80%
58.50%
76.10%
91.20%
One interesting finding is that the adjusted R2 with 20 variables is larger (or equal
to) the corresponding value with 31 variables. At least in this special case, reducing the
number of variables does not reduce the effectiveness of the MLR model but increases
the efficiency.
Figure 7.15, 7.16, and 7.17 illustrates the contribution factor based on the MLR
performed between FD48 and the 55 original variables (VMAX was taken out from the
107
an
nalysis because its contrributing facto
or was high)), Predfictor__1 of 31 variables and
Predictor_6 of
o 20 variablees respectiveely.
Figure
F
7. 15 Co
ontribution facttors on the MLR
R between the F
FD48 and Origiinal set of 55 vaariables (VMAX
X was
rem
moved from the aanalysis)
It wass found that Aerosol,
A
Wiind, Humiditty, Shear, annd Temperatuure all
co
ontributing factors
f
in thee regression equation. T
The ranking ffor the contrribution was
fo
ound as (1) Aerosols,
A
(2)) Wind, (3) Relative
R
Hum
midity, (4) S
Shear, and (55) Temperatuure
co
omponents. Further breaak down prov
vided as in F
Figure 4 thatt U200 and P
PHCN has thhe
highest contriibution then V20C follow
wed by SST
T.
Figuree 7.15 tells about
a
the effe
fect of the SH
HIPS and MODIS variabbles used on the
out 71% of th
the variationn in FD48 cann be accountted
FD48. R2 = 71.1% indicaating that abo
fo
or by the 56 predictors. Based
B
on thee results of thhe Sequentiaal Sum of Sqquares
co
omponents such
s
as zonaal winds, estiimated oceann heat contennt and sea suurface
108
co
omponents are
a the greateest contributtors to the M
MLR were foound. This is also true for the
in
ntensity chan
nges at 06, 12, 18, 24, 30
0, 36, and 422 hours.
In Figure 7.16,
7
R2 = 65
5.1% indicatting that aboout 65% of thhe variation in FD48 cann be
acccounted forr by the 31 explanatory
e
variables.
v
Thhe contributiion factor in this case is
mainly
m
goverrned by the same variablees as shown in Figure 7..15 except A
AOT and relaative
hu
umidity are playing sign
nificant roless as well. Alsso Shear andd Eddy play important rooles
in
n the case off Predictor_1
1.
Figure 7. 16 Contribution fa
actors on the MLR
M
between th
he FD48 and Preedictor_1 which
h has 31 variab
bles
109
Figure 7. 17 Contribution fa
actors on the MLR
M
between th
he FD48 and Preedictor_6 which
h has 20 variab
bles
The effect of the SHIP
PS and MOD
DIS variabless used on thee FD48 as illlustrated in
Figure 7.17, R2 = 63.7% indicating
i
th
hat about 64%
% of the varriation in FD
D48 can be
acccounted forr by the 20 explanatory
e
variables.
v
Thhe contributiion factor in this case is
governed by tangential
t
an
nd zonal win
nd in additionn to AOT annd RH.
Figuree 7.18 showss the R2 and adjusted R2 values along with the R
RMSE and thhe
Residual
R
Erro
ors for the MLR
M
perform
med between the eight response variaables and sixx
prredictor setss.
At 48 hour forecast intervals as
a in Figure 7.18, R2, adjjusted R2 annd RMSE aree
laargest and att 06 hours fo
ound smallesst. The rangee of values of R2, adjusteed R2 and RM
MSE
between 06 an
nd 48 hours for Predicto
or_6 found aas (15.0% annd 63.7%), (99.1% and
61.2%), (8.35
5 and 25.02) and (69.64 and
a 415.0) rrespectively.. The RMSE
E and Residuual
n
fo
or all six pred
dictors, how
wever, signifiicant R2 valuues were fouund
errrors found negligible
110
laarger when considering
c
the
t 42 and 48 hours leadd time whichh is longer foorecast intervvals.
This
T may be due
d to the reesults of disccretization off the intensitty of values as per DeMaaria
an
nd Kaplan (D
DeMaria & Kaplan,
K
1994) (DeMariaa & Kaplan, 1999) and thhe regressioons
fo
or the shorter forecast in
ntervals may have been eexposed to soome noise.
Figure 7. 18 R2 values an
nd RMSE at eig
ght lead time poositions 06, 12, 118, 24, 30, 36, 422 and 48 hours
Averaage RMSE fo
or the six Preedictors for 06, 12, 18, 224, 30, 36, 42, and 48
hours was fou
und as 8.33, 12.28, 14.76
6, 16.27, 17..96, 19.28, 19.69, and 200.38
reespectively as
a illustrated
d in Figure 7.19. The RE
ED line is thee logarithmicc fit for the
111
eiight data poiints showing
g significant R2 value. Thhe variation among the P
Predictors
RMSE
R
varied
d between 0.01 through 0.05.
0
This small
s
variatio
on suggests, reducing thhe number off variables did not
ch
hange the co
ore physical information. Therefore, the same phhenomena caan be
ex
xplained by the reductio
on of a variab
ble.
Figure 7. 19 Average Ro
oot Mean Squarre Error (RMSE
E) for each FD
7 “Residu
uals versus fits”
f
are pressented to shoow the residuuals versus thhe
In Figure 7.20
fiitted values at
a FD06 and
d FD48. Resiiduals variedd between ±110 for FD06 whereas forr
FD48 its ±50 and FD06 has
h lesser ou
utlier than FD
D48.
112
In addition
n, for this stu
udy MODIS aerosol retrrievals were averaged, thherefore, it iss
im
mportant to articulate
a
thee statistical uncertainty
u
ffor the three variables ussed in the aerosol
PCA. For exaample, the qu
uoted uncerttainties for F
Fabian 2003 found for A
AOT (0.23±0.02),
MCO
M
(15.54±
±1.89) and CCNO
C
(3.98 ± 0.781) X11008 when 955% confidennce interval w
was
co
onsidered.
Figure 7. 20 Residual
R
plots foor FD06 and FD
D48
113
Table 7. 8 Response and Predictor variables’ R2, Adjusted R2, F value, RMSE and Residual Error
Response Predictors
Predictor_1
Predictor_2
Predictor_3
FD06
Predictor_4
Predictor_5
Predictor_6
Predictor_1
Predictor_2
Predictor_3
FD12
Predictor_4
Predictor_5
Predictor_6
Predictor_1
Predictor_2
Predictor_3
FD18
Predictor_4
Predictor_5
Predictor_6
Predictor_1
Predictor_2
Predictor_3
FD24
Predictor_4
Predictor_5
Predictor_6
Predictor_1
Predictor_2
Predictor_3
FD30
Predictor_4
Predictor_5
Predictor_6
Predictor_1
Predictor_2
Predictor_3
FD36
Predictor_4
Predictor_5
Predictor_6
FD42
Predictor_1
R2
18.80%
18.80%
17.70%
17.60%
16.40%
15.00%
27.80%
27.80%
26.90%
26.80%
25.40%
24.40%
37.90%
37.90%
37.10%
37.10%
36.10%
34.90%
45.40%
45.40%
44.70%
44.60%
43.80%
43.10%
50.90%
50.90%
50.20%
49.70%
49.40%
48.30%
54.90%
54.60%
54.10%
53.70%
53.40%
53.00%
61.80%
Adjusted R2
9.60%
9.90%
9.40%
9.60%
9.60%
9.10%
19.60%
19.90%
19.50%
19.70%
19.30%
19.10%
30.80%
31.10%
30.80%
31.00%
30.80%
30.30%
39.30%
39.40%
39.10%
39.20%
39.30%
39.10%
45.40%
45.50%
45.10%
44.90%
45.20%
44.70%
49.80%
49.90%
49.40%
49.20%
49.60%
49.70%
57.50%
114
F value
2.05
2.12
2.13
2.20
2.41
2.52
3.40
3.52
3.63
3.77
4.17
4.60
5.39
5.59
5.84
6.08
6.91
7.63
7.36
7.62
7.99
8.29
9.57
10.78
9.18
9.50
9.96
10.19
11.96
13.31
10.76
11.14
11.64
11.95
14.07
16.04
14.31
RMSE
8.32
8.31
8.33
8.32
8.32
8.35
12.27
12.25
12.28
12.27
12.30
12.31
14.76
14.73
14.77
14.74
14.76
14.81
16.27
16.24
16.29
16.27
16.27
16.29
17.92
17.90
17.97
18.01
17.94
18.04
19.25
19.22
19.32
19.36
19.27
19.28
19.64
Residual Error
69.23
68.98
69.38
69.26
69.22
69.64
150.50
150.10
150.90
153.40
151.20
151.60
217.70
217.00
218.00
217.20
217.80
219.40
264.60
263.80
265.30
264.80
264.60
265.40
321.10
320.30
322.70
324.20
322.00
325.40
370.40
369.50
373.40
374.80
371.70
371.60
385.80
Predictor_2
Predictor_3
Predictor_4
Predictor_5
Predictor_6
Predictor_1
Predictor_2
Predictor_3
Predictor_4
Predictor_5
Predictor_6
FD48
61.70%
61.20%
60.70%
60.60%
60.20%
65.10%
65.00%
64.80%
64.30%
64.00%
63.70%
57.50%
57.20%
56.90%
57.30%
57.40%
61.10%
61.10%
61.20%
60.80%
61.10%
61.20%
14.75
15.59
15.92
18.82
21.51
16.46
17.00
18.20
18.55
21.84
25.02
19.64
19.70
19.78
19.68
19.67
20.38
20.37
20.35
20.45
20.38
20.36
385.80
388.10
391.10
387.30
387.10
415.30
414.90
414.00
418.20
415.30
415.00
7.5 VariableReductionAnalysisViaLassoandElasticNet
The Lasso operator results between the response variable FD48 and original predictor
of 56 variables has been plotted in Figure 7.21. This plot is an illustration of Mean Square
Error (MSE) vs. Lambda () behavior. The 100 red dots in the plot are the MSE for the
resulting model along with corresponding error bars shown as the vertical lines stretching
out from each side of the red dots.
115
Fiigure 7. 21 Mea
an Square Errorr (MSE) vs. Lam
mbda - Lasso p
plot for responsee variable FD488 and Original
prredictor (56 varriables).
For the Laasso plot in Figure
F
7.21, the value off lambda thaat minimizess the cross
validated MS
SE is 0.89781
1. The corressponding inddex is at 66 aand illustrateed by the greeen
ght. The deg
gree of freedo
om at this inndex point is 20. Therefoore, denote
liine on the rig
variable shrin
nkage from 56
5 to 20 (Lassso48-MinM
MSE). The bllue line on thhe left at lam
mbda
= 2.7418 is within
w
one staandard error of the minim
mum MSE. T
The correspoonding indexx is
78. The degreee of freedom
m at this index point is 111 (Lasso48--1SE). Thereefore, denotee
variable shrin
nkage from 56
5 to 11. Plo
ot below is sshowing MS
SE vs.  for E
Elastic Net
perations forr the responsse variable FD48
F
with orriginal prediictor of 56 vvariables at  =
op
0.5.
116
Fiigure 7. 22 Mea
an Square Errorr (MSE) vs. Lam
mbda - Elastic Net plot for ressponse variable FD48 and Origginal
prredictor (56 varriables) and  = 0.5.
mizes the crooss
For the Ellastic Net plot in Figure 7.22, the vaalue of lambdda that minim
validated MS
SE is 0.58798
8. The corressponding inddex is at 54 aand illustrateed by the linne on
th
he right (greeen). The deg
gree of freed
dom at this inndex point iss 44. Therefoore, denote
variable shrin
nkage from 56
5 to 44 (Elaastic48-MinM
MSE). The lline on the leeft (blue) at
laambda = 1.97
707 and is within
w
one staandard errorr of the minim
mum MSE. The
co
orresponding
g index is 67
7. The degreee of freedom
m at this indeex point is 38 (Elastic4881SE). Therefo
ore, denote variable
v
shrin
nkage from 56 to 38.
117
Table 7. 9 Original variables and the reduced variable sets based on Lasso and Elastic Net shrinkage
Original
Lasso48-MinMSE
 = 100,  = 1
Lasso48-1SE
 = 100,  = 1
Elastic48-MinMSE
 = 100,  = 0.5
Elastic48-1SE
 = 100,  = 0.5
Elastic48-1SE
 = 100,  = 0.9
Predictors
MSLP INCV SST DTL PHCN U200 U20C V20C E000 EPOS
ENEG EPSS ENSS RHLO RHMD RHHI PSLV Z850 D200
REFC PEFC T000 R000 Z000 TWAC TWXC PENC SHDC
SDDC SHGC DIVC T150 T200 T250 SHRD SHTD SHRS
SHTS SHRG PENV VMPI VVAV VMFX VVAC IR00 IRM3
RD20 RD26 RHCN AOT AFBO BRBO MRO MCO ERO
CCNO
MSLP U20C V20C RHHI PSLV Z850 PEFC TWAC SDDC
T150 SHRS SHTS SHRG PENV VMPI IR00 IRM3 MRO MCO
ERO
MSLP U20C V20C Z850 TWXC T150 SHRS SHTS SHRG
PENV VMPI
MSLP INCV SST DTL U200 U20C V20C EPOS EPSS RHLO
RHMD RHHI PSLV Z850 D200 REFC PEFC R000 Z000
TWAC TWXC PENC SDDC SHGC T150 T200 T250 SHTD
SHRS SHTS SHRG PENV VMPI VVAV VMFX IR00 IRM3
RD20 RD26 AOT BRBO MRO MCO ERO
MSLP SST DTL U200 U20C V20C RHLO RHHI PSLV Z850
REFC PEFC Z000 TWAC TWXC PENC SDDC SHGC DIVC
T150 T200 T250 SHRD SHTD SHRS SHTS SHRG PENV
VMPI VVAV VMFX VVAC IR00 IRM3 RD20 AOT MRO
MCO
MSLP U200 U20C V20C PSLV Z850 PEFC Z000 TWAC
TWXC PENC SDDC T150 SHRS SHTS SHRG PENV VMPI
IRM3 MCO
It can be seen from the both Lasso and Elastic plots (Figures 7.21 and 7.22), as  goes
toward the left the MSE jumps to higher values rapidly. Therefore, the coefficients tend
to minimize so much that they do not effectively fit the responses. On the other hand as
lambda move towards the right the model have abundant nonzero coefficients and the
higher values in MSE describe that that they are over fitted.
118
Table 7. 10 Regression analysis based on the predictors resulted from Lasso and Elastic Net shrinkage
# of
Variables
R2
Original
Lasso48MinMSE
 = 100,  = 1
56
20
F
Value
or t2
10.28
28.15
RMSE
Adjusted Category
R2
Rank
19.87
19.60
69.8%
66.4%
63.0%
64.0%
Lasso48-1SE
 = 100,  = 1
11
45.41
20.26
62.9%
61.6%
Elastic48MinMSE
 = 100,  = 0.5
44
13.05
19.75
68.8%
63.5%
Elastic48-1SE
 = 100,  = 0.5
38
15.26
19.61
68.5%
64.0%
Elastic48-1SE
 = 100,  = 0.9
20
26.99
19.87
65.4%
63.0%
N/A
(1) Wind, (2)
Shear, (3)
Temperature,
(4) Aerosol
and (5)
Humidity
(1) Wind, (2)
Shear, (3)
Temperature
(1) Wind, (2)
Shear, (3)
Temperature,
(4) Aerosol
and (5)
Humidity
(1) Wind, (2)
Shear, (3)
Temperature,
(4) Aerosol
and (5)
Humidity
(1) Wind, (2)
Shear, (3)
Aerosol and
(4)
Temperature
Table 7.9 shows original (56) and reduced variable set after Lasso and Elastic Net
techniques were applied. For the Lasso technique, Figure 7.21, the Green and Blue
vertical lines are corresponding to the Lasso48-MinMSE = 20 and Lasso48-1SE = 11
variables. For the Elastic Net technique, Figure 7.22, the Green and Blue vertical lines are
corresponding to the Elastic48-MinMSE = 44 and Elastic48-1SE = 38 variables.
119
Table 7.10 is the results based on Multiple Linear Regression of the FD48
response variables vs. the predictors for the original set of variable as well as the Lasso
and Elastic Net scenario variables.
Most interesting finding from this analysis is that the Adjusted R2 for each
scenario. The Adjusted R2 values Lasso48-MinMSE (20 variables), Lasso48-MinMSE
(44 variables) and Elastic48-1SE (38 variables) corresponding to 64.0%, 63.5% and
64.0% as opposed to the Original 56 variable set resulted lower Adjusted R2 = 63.0%.
This is a very important finding which can be explained from the Table 7.10 that the
rank of Aerosol preceded humidity in three cases with higher Adjusted R2. This also
demonstrated the fact that by removing redundant variables from the predictor variable
set improves the performance of the models by refining the effect of the curse of
dimensionality as well as enhancing the interpretability of the model.
7.6
MISRandMODISAOTComparison
AOT validation can be accomplished by comparing daily instances among Terra-
MODIS, Aqua-MODIS, Terra-MISR, CALIPSO and Aura OMI. In this study, AOT at
0.55 µm from Terra and Aqua MODIS sensors were compared against AOD at 0.55 µm
(Green Band) from the Terra-MISR sensor. Atmospheric profile products from Terra and
Aqua MODIS sensors were compared against Aqua-AIRS Level 2 Standard atmospheric
120
profile products. Specifically, retrieved temperatures from Terra and Aqua MODIS
sensors were compared against retrieved air temperature from the Aqua AIRS sensor. RH
values were calculated for (1) MODIS using retrieved atmospheric temperature and
corresponding dew point temperature and (2) AIRS using H2O Standard and H2O
Saturated values. Both temperatures and RH were then compared between MODIS and
AIRS at pressure levels 500, 700, and 850 mb.
The daily average values for Level 2 MODIS MOD04 and MYD04 AOT at 0.55µm
have been calculated and compared against MISR Level 2 aerosol product (MIL2ASAE)
values. In Table 7.1, Dynamic and Static-Coordinate AOT values from MODIS and
MISR are presented. For Static-Coordinate, change in AOT values between MODIS and
MISR are 8.33%, 5.26%, 0.00%, and 9.38% corresponding to 2003, 2004, 2005, and
2005. Change in AOT values were 13.64%, -6.67%, 5.26%, and 25.64% for 2003, 2004,
2005, and 2005 respectively for Static-Coordinate consideration.
Among the four hurricanes, Isabel and Katrina reached category 5 strengths,
however, the most severe damage was caused by Katrina. Figure 7.23 shows AOT values
for each hurricanes utilizing level 2 aerosol data from MODIS and MISR sensors. As
discussed earlier the pattern of the AOT presention for these hurricanes for the year 2003,
2004, 2005, and 2006 did not change. It shows negligible AOT variation between the
year 2004 and 2005. Significant variations, however, is observed for the year 2003 and
2006.
121
Figurre 7. 23 Dynamiic-Coordinate and
a Static-Coorrdinate averagee AOT for each hurricane
MISR AO
OD resolutio
on is 17.6 km
m based on thhe Table 1.11. Data pointts representeed by
th
he average AOT
A
over an
n area of sizee 17.6×17.6 kkm2 can be rretrieved usiing the radiaance
in
nformation within
w
the scene. On the othe hand, rresolution off the MODIS
S AOT is 10 km.
Data
D points reepresented by
b the averag
ge AOT overr an area of ssize 10×10 kkm2. To com
mpare
MODIS
M
and MISR
M
AOT obtained AO
OT pixels neeed to be oveerlapped botth spatially aand
teemporally. However,
H
thee available-M
MISR and M
MODIS data ooverlapped iis available iin a
16-day cycless. As a resullt overlapped
d MISR andd MODIS AO
OT sample iss very small as
sh
hown in Figu
ure 7.23.
122
0.16
54 W
53 W
0.14
0.12
29 N
0.1

28 N
0.08
27 N
Aerosol Optical Thickness
55 W
30 N
0.06
26 N
0.04
Figure 7. 24 Representing aerosol dust plots for hurricane Isabel on 09/09/2003 acquired from MODIS Aqua
Figure 7.24 presenting dust aerosol data points for hurricane Isabel on 09/09/2003
at 17:35. There are 37 valid data points from
MYD04_L2.A2003252.1735.005.2007085171231.hdf file acquired to plot the figure. On
the other hand, MISR had 50 valid data points from
MISR_AM1_AS_AEROSOL_P232_O019823_F12_0022.hdf file at start time 14:04.
MODIS AOT resented by diamonds and MISR AOD are Squares between (26N,-55W)
and (30N,-53W). Range of AOT and AOD is between 0.04 and 0.16.
123
CHAPTER EIGHT
124
8. CONCLUSIONS AND FUTURE WORK
This study investigated dust aerosol impacts on hurricanes through spatial analysis of
aerosol optical thickness, air temperature and relative humidity at related pressurer levels.
Major findings are:
1. Average AOT was lowest for the strongest hurricane while RH was significantly
higher when compared with the other three hurricanes as depicted in Table 7.1.
Suppression of RH values was observed when elevated AOT values were found
(Table 7.1). This suppression was highest during Isabel and Helene. With the
increasing AOT value there is a slight change in temperature observed in the range
between 0.5 and 1.5 degrees Kelvin.
2. The daily variation of AOT, temperature and RH values in static–left, static–right,
dynamic–left and dynamic–right for 500, 700 and 850 mb pressure levels are
presented in and Figure 7.5. Left side and right side AOT, temperature and RH
generally exhibit opposite cyclic phenomena as illustrated in Figure 7.5. This
behavior has been observed for all four hurricanes. For the left side and right side
AOT, temperature and RH of the four hurricanes show maxima and minima for both
static and dynamic coordinate scenarios, which exhibits opposite cyclic phenomena.
Figure 7.5 shows such cases for hurricane Isabel in 2003 for AOT and atmospheric
profile at 700 mb. In the Tables 7.2 and 7.3, higher AOT values for Isabel when
125
considering the right side of the dynamic coordinates was observed. Also, for
dynamic coordinates, the right side AOT of Helene is lower than the left side. For
static coordinates, left side AOT values are higher for Isabel, Frances, and Katrina.
3. When considering the profile difference plot in Figure 7.6, significant difference in
the RH values and a small variation in the temperature for all hurricanes were found.
Based on the difference plot, the values found to be positive for temperature and
negative for RH which corresponds to warm and dry conditions for all four hurricanes
as in Figure 7.6.
Among all four hurricanes, Helene had the lowest power and intensity. Dust
created an extremely dry atmosphere that was not conducive to intensifying a hurricane.
As such, this study suggests that dust-ridden air influenced Helene to dissipate so that it
could not intensify.
In this study aerosol dust values played a role in suppressing the Atlantic
hurricanes’ intensity by reducing the RH and increasing temperature as described in
Figure 7.1, 7.2 and 7.7. The implication of the present observations addressed that aerosol
dust inhibits hurricane because it makes the air dry and increases the atmospheric
temperature. Fueling a hurricane requires both warm and moist air, but dust is only
fueling the temperature and inhibits moisture to rise. Therefore, it is dust that serves to
dampen the already formed hurricane. Although Isabel and Katrina reached Category 5
strengths, Katrina created the most severe damage because of its lower pressure and
126
higher wind speed. Measureable lower AOT level discovered during hurricane Katrina
demonstrates its intensity while significantly higher AOT observed for Isabel and Helene
suggest that dust dampen their intensities.
The effect of dust on temperature and RH based on the left and right side spatial
regions methodology developed for the purpose of this study, prior to this analysis, was
not explored in any published material other than general comments made by authors in
different text books (Aguado & Burt, 2010) (Ackerman & Knox, 2010). These textbooks
only emphasized that the winds are the strongest around the hurricane eye wall.
It appears that, if the strongest winds are on the right side then the heaviest rains
are usually on the left side (Figure 7.5). Some definitive patterns were found for AOT
and atmospheric profiles for both sides. However, for conclusive results, further
experimentation and observation are needed. The time frame for this analysis was
limited. The results in this paper are solely based on the analyses of the four hurricanes
selected between 2003 and 2006. It is to be expected, therefore, that an increase in the
time span, perhaps over several decades, may reveal more decisive findings.
By combining MODIS and SHIPS data, 56 variables were compiled and selected
as predictors for this study. Variable reduction from 56 to 31 was performed via
correlation coefficients (cc) followed by Principal Component Analysis (PCA) extraction
techniques to further reduce these 31 variables to 20. Among the 31 variables, PCA
127
candidates were selected for the variables describing the same physical mechanism and
the PCA procedure reduces the numbers from 3-8 to 1-4 for each group of variables. Five
categories wind, aerosol, shear, relative humidity, and temperature components were
established by reducing 56 variables to 20. Aerosol, wind, humidity, shear, and
temperature all contributing factors in the regression equation with the ranking for the
contribution found as (1) Wind, (2) Aerosol, (3) Shear, (4) Relative Humidity, and (5)
Temperature components. Indicating that aerosol predictor surpass the other predictors
especially shear. But from dynamics point of view, it is impossible for aerosol to be more
important than shear and temperature. The aerosol rank preceding the shear could be due
to the sample size being too small (306 data points) when compared to the original
SHIPS dataset (over 6000 data points) and inadvertently the value ranges of shear and
temperature are not large. As a result, the limited variance in those parameters makes it is
difficult to demonstrate the importance of those parameters. This is practically similar to
a study with other parameter values being controlled. When the coefficient of variations
(cv) was calculated, cv for AOT is found 40.29%, Wind is 37.61%, Shear is 35.50%, SST
is 3.65% and Relative Humidity (RH) is 6.8%. SST and RH cv values are so low that it
can be considered the experiment is controlled in a specific value; therefore, it is not
surprising to have AOT considered as second dominated factor in this study because
AOT are of the largest variability. When MLR is performed on all 56 variables (without
any variable reduction) as illustrated in Figure 7.11, interestingly, aerosol is ranked in the
last place. This means that the original definition of aerosol related parameters cannot be
128
represented. The newly derived variable based on PCA not only reduce the dimension of
the problem but also signify the importance of the aerosol effects.
There are plenty of benefits of the curse of dimensionality. Original variables may
demonstrate better results but the reduced variables gave similar results with much lower
dimensionality and improved efficiency. For computational purpose improved efficiency
is much more important than the highly precision results.
One interesting finding is that the adjusted R2 with Predictor_6, 20 variables is
larger (or equal to) the corresponding value with Predictor_1 of 31 variables. At least in
this special case, reducing the number of variables does not reduce the effectiveness of
the MLR model but increases the efficiency.
The variation among the Predictors RMSE ranged between 0.01 and 0.05. This
implies that reducing the number of variables did not change the core physical
information because variation is from the mean for all sets of predictors and very small.
Therefore, the same phenomena can be explained by the reduction of the variable. R2
values were found larger when considering the 42 and 48 hours lead time. R2, adjusted
R2, RMSE, and residual error among Predictor 1 through 6 were negligible. The RMSE
and residual errors difference among the six predictor groups were found negligible.
129
The results based on MLR in the Table 7.10 for response variables FD48 vs. (1)
the original set of variables, (2) the Lasso, and (3) the Elastic Net 38 variables predictors
show most fascinating finding for the Adjusted R2 values. The Adjusted R2 values for
Lasso48-MinMSE (20 variables), Lasso48-MinMSE (44 variables) and Elastic48-1SE
(38 variables) were 64.0%, 63.5% and 64.0% respectively as opposed to the Original set
of 56 variables resulted in a lower Adjusted R2 = 63.0%.
This is a very important finding and can be explained from the Table 7.10 that the
rank of Aerosol preceded humidity in three cases with higher Adjusted R2. This also
demonstrated the fact that by removing redundant variables from the predictor variable
set improves the performance of the models by refining the effect of the curse of
dimensionality as well as enhancing the interpretability of the model.
8.1 FutureWork
The vertical profile of the AOT measurements may be taken into consideration to
introduce tremendous opportunity to study AOT vertical profiles. The same methodology
described in Chapter 5 of selecting spatial coordinates may be employed. Potential
breakthrough about finding relationships between Atlantic hurricanes and atmospheric
dust is underlying in the future research on vertical profile of the AOT as well as for the
atmospheric profiles. The following two suggestions can be adapted for the future
enhancement of the current work.
130
8.1.1 VaryingConcentricCirclesRadii
Varying concentric circles radii, AOT, temperature and RH relationship can be
further investigated. This will facilitate to establish relationships among AOT and
atmospheric profile parameters. Measurement can be taken starting between the vicinity
of the hurricane eye wall and gradually moving away from the hurricane center.
Atmospheric profiles and AOT modulation based on these varying radii could produce a
promising outcome to establish concrete relationships between dust and hurricanes.
8.1.2 UsingCALIPSOforDustAerosolRetrieval
CALIPSO data can be used for AOT vertical information. Studies based on
CALIPSO represent the role of aerosols and cloud in the earth’s climate system (Winker
et al., 2003). It provides the vertical information about aerosols and cloud. The CALIPSO
level 2 Vertical Feature Mask (VFM) (Liu, et al., 2004) product provides detail vertical
information about aerosol and cloud layer. It is easily used to separate dust from ground
surface (Xie, 2009).
Current remote sensing capabilities can be enhanced by multi-sensor utilization
and by its global repetitive measurement potentials. Various Earth observation
applications can be improved by the CALIPSO which can provide global, verticallyresolved measurements of aerosol distribution (Xie, 2009) and provide a large amount of
valuable information for solving problems.
131
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CURRICULUM VITAE
Mohammed Mostafa Kamal a Ph.D. candidate in George Mason University (GMU),
majored in Computational Sciences and Informatics (CSI). He received the M.S. degree
in Physics and Astronomy from Howard University, Washington, DC, in 1994. He is
working as an Information Technology Specialist at Bureau of Engraving and Printing,
U.S. Department of the Treasury. His major research area: Effect of Dust variability,
Temperature and Moisture Profiles on Tropical cyclones; Remote Sensing, Atmospheric,
Oceanic and Earth Sciences.
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