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Electronic Theses, Treatises and Dissertations The Graduate School
2007 Atlantic Reconnaissance Vortex Message Climatology and Composites and Their Use in Characterizing Eyewall Cycles David J. Piech
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COLLEGE OF ARTS AND SCIENCES
ATLANTIC RECONNAISSANCE VORTEX MESSAGE CLIMATOLOGY AND
COMPOSITES AND THEIR USE IN CHARACTERIZING EYEWALL CYCLES
By
DAVID J. PIECH
A Thesis submitted to the Department of Meteorology in partial fulfillment of the requirements for the degree of Master of Science
Degree Awarded: Fall Semester, 2007
The members of the Committee approve the Thesis of David J. Piech defended on 6 November 2007.
______Robert Hart Professor Directing Thesis
______Carol Anne Clayson Committee Member
______Henry Fuelberg Committee Member
The Office of Graduate Studies has verified and approved the above named committee members.
ii ACKNOWLEDGEMENTS
I would like to thank my committee members Dr. Carol Anne Clayson and Dr. Henry Fuelberg, and I would also like to thank Ryan Maue, Clark Evans, Danielle Manning, Elizabeth Minter, and Jessica Fieux for assistance and suggestions. Many thanks are also in order for the wonderful members of the TA lab and the Hart/Reasor lab, both past and present, for their continued support and laughter. Even more appreciation is due to my wonderful parents Gerald and Paula, my two brothers Andrew and Philip, all the friends I have made at FSU, especially the best roommates I ever had, Christine Haman and Sadie. Last but certainly not least I would like to thank my major professor, Dr. Robert Hart, who has been a wonderful mentor and friend over the past year and a half.
iii TABLE OF CONTENTS
List of Tables ...... vi List of Figures...... vii Abstract...... xv
1. INTRODUCTION ...... 1
a. Datasets...... 1 b. Climatology and Persistence Models...... 2 c. Statistical-Synoptic Models ...... 3 d. Statistical-Dynamical Models...... 4 e. Climatology of Atlantic Basin Storm Data...... 8 f. Satellite Based study...... 10 g. Reconnaissance Based Study...... 10 h. Objectives ...... 11
2. DATA AND METHODOLOGY...... 18
a. Data ...... 18 b. Visualization ...... 21 i. Distribution and Frequency ...... 21 ii. Eyewall Phase Diagram...... 22
3. VORTEX MESSAGE CLIMATOLOGY ...... 28
a. Atlantic Basin Distribution ...... 28 i. All Data Points...... 28 ii. Category...... 29 iii. Month...... 30 iv. Eye Size ...... 31 v. Eye Type ...... 31 vi. Rapid Intensifiers/Rapid Weakeners ...... 32 b. Trend and Number of Occurrences...... 33 c. Mean MSLP, Eye Size, and Frequency of Concentric/Elliptical Eyewalls...35 i. Entire Atlantic Basin...... 35 ii. Atlantic Basin Minus Caribbean and Gulf of Mexico...... 36 iii. Caribbean...... 37 iv. Gulf of Mexico...... 37 d. Representativeness of Results...... 38 e. Climatology Summary...... 38
4. EYE CHARACTERISTIC CLIMATOLOGY ...... 59
a. All Flight Levels ...... 59 b. 700 hPa Maps...... 62
iv c. 850 hPa Maps...... 63
5. EYEWALL PHASE DIAGRAM ...... 76
a. Rapid Intensifiers, Rapid Weakeners, Non-Rapid Weakeners ...... 77 b. Mean Plots ...... 77 i. Entire Atlantic Basin...... 77 ii. Atlantic Basin Minus Caribbean and Gulf of Mexico...... 78 iii. Caribbean...... 79 iv. Gulf of Mexico...... 79 c. Case Study 1: Eyewall Replacement Cycle (Rita, 2005)...... 80 d. Case Study 2: Rapid Weakening (Charley, 2004) ...... 82 e. Case Study 3: Rapid Intensification/Most Intense Storm (Wilma, 2005) ....83
6. FORECASTING...... 110
7. CONCLUSION...... 116
REFERENCES ...... 119
BIOGRAPHICAL SKETCH ...... 123
v LIST OF TABLES
Table 1.1 ...... 15 SHIPS predictors from Kaplan and DeMaria (1999).
Table 1.2 ...... 15 Variables from SHIPS (DK99) and NHC HURDAT database used in Kaplan and DeMaria (2003).
Table 1.3 ...... 16 Variables and descriptions for the National Hurricane Center’s Extended Best Track Dataset, from Pennington et al. (2000) and utilized by Kimball and Mulekar (2004).
Table 1.4 ...... 17 Wind profile types established by Samsury and Rappaport (1991).
Table 2.1 ...... 25 Storms included in the vortex message dataset.
Table 2.2 ...... 27 Listing of bins used in the development of frequency diagrams. For the eye diameter column, the values in parenthesis indicate the range of diameters assigned to each bin.
Table 3.1 ...... 58 Number of Atlantic storms 1989-2005 and number that hit United States.
Table 3.2 ...... 58 Percentage of all storms, rapid weakeners, and rapid intensifiers by month according to the vortex message database.
Table 3.3 ...... 58 Average eye size and MSLP for each eye type.
Table 4.1 ...... 75 Julian day/Calendar day equivalent.
Table 6.1 ...... 115 Correlation between MSLP and temperature inside eye at the three most common flight levels.
Table 6.2 ...... 115 Correlation between vortex parameter (at point A) and future change of vortex parameter (between points B and C) at 700 hPa.
vi LIST OF FIGURES
Figure 1.1 ...... 13 Schematic of primary/secondary circulation (Salby 1996).
Figure 1.2 ...... 13 Cross-section through the core of a tropical cyclone, which shows the secondary circulation (Palmen and Newton 1969). Pressure (solid) and temperature (dashed) are on the left side and temperature deviations from a standard atmosphere are on the right side (dashed-dotted).
Figure 1.3 ...... 14 Secondary circulation induced in a balanced vortex by a source of heat. Distortions induced by I2 (inertial stability) (Elsberry 1995).
Figure 1.4 ...... 14 Graphical representation of the secondary circulation and distribution of precipitation for a tropical cyclone (Willoughby 1988).
Figure 2.1 ...... 23 Eye diameter frequency in the Atlantic basin from all vortex messages.
Figure 2.2 ...... 24 Example of the eyewall phase diagram (Hurricane Rita, 2005).
Figure 3.1 ...... 40 Location of all vortex message reports between 1989 and 2005.
Figure 3.2 ...... 40 Location of all NHC best track data points between 1989-2005.
Figure 3.3 ...... 41 Vortex message points by category, 1989-2005.
Figure 3.4 ...... 41 NHC best track points by category, 1989-2005.
Figure 3.5 ...... 42 NHC best track points, category 1-5, 1989-2005.
Figure 3.6 ...... 42 Comparison of vortex message database and NHC BT by category, 89-05.
Figure 3.7 ...... 43 Vortex message points by month, 1989-2005.
vii Figure 3.8 ...... 43 NHC BT points by month, 1989-2005.
Figure 3.9 ...... 44 Vortex message points by eye diameter, 1989-2005.
Figure 3.10 ...... 44 Vortex message points by eyewall type, 1989-2005.
Figure 3.11 ...... 45 Satellite image of a circular eyewall (Hurricane Rita, 2005), courtesy of the Space Science and Engineering Center, University of Wisconsin-Madison (http://www.ssec.wisc.edu/~gumley/modis_gallery/).
Figure 3.12 ...... 45 Satellite image of a concentric eyewall (Typhoon Amber, 1997), courtesy of Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, (http://cimss.ssec.wisc.edu/tropic/archive/1997/storms/ amber/).
Figure 3.13 ...... 46 Satellite image of an elliptical eyewall (Super Typhoon Ma-On, 2004), courtesy of the MODIS Rapid Response System, (http://rapidfire.sci.gsfc.nasa.gov/gallery/ ?2004282-1008/Ma-On.A2004282.0405.2km).
Figure 3.14 ...... 47 Storms with eyewall replacement, 1989-2005.
Figure 3.15 ...... 47 Rapid intensifiers by month, 1989-2005.
Figure 3.16 ...... 48 Rapid weakeners by month, 1989-2005.
Figure 3.17 ...... 48 Number of vortex messages compared to both total number of storms and number of storms involved with this study. Black line is the trend line for each plot.
Figure 3.18 ...... 49 Frequency of occurrence of temperature inside the eye.
Figure 3.19 ...... 49 Frequency of occurrence of temperature outside the eye.
Figure 3.20 ...... 50 Frequency of occurrence of dewpoint inside the eye.
viii Figure 3.21 ...... 50 Frequency of occurrence of eye dewpoint depression (temperature inside eye- dewpoint inside eye) for all flight levels.
Figure 3.22 ...... 51 Frequency of occurrence of maximum flight level pressure altitude inside the eye.
Figure 3.23 ...... 51 Frequency of occurrence of maximum flight level pressure altitude outside the eye.
Figure 3.24 ...... 52 Frequency of occurrence of mean sea level pressure.
Figure 3.25 ...... 52 Frequency of occurrence of maximum winds at flight level.
Figure 3.26 ...... 53 Frequency of occurrence of location of maximum flight level winds by storm quadrant.
Figure 3.27 ...... 53 Distribution of eye type.
Figure 3.28 ...... 53 Percentage of eye type.
Figure 3.29 ...... 54 Mean MSLP, mean eye diameter, frequency of elliptical eyewalls and frequency of concentric eyewalls for the entire Atlantic basin, 1989-2005.
Figure 3.30 ...... 55 Mean MSLP, mean eye diameter, frequency of elliptical eyewalls and frequency of concentric eyewalls for the entire Atlantic basin minus Caribbean and Gulf of Mexico, 1989-2005.
Figure 3.31 ...... 56 Mean MSLP, mean eye diameter, frequency of elliptical eyewalls and frequency of concentric eyewalls for the Caribbean Sea, 1989-2005.
Figure 3.32 ...... 57 Mean MSLP, mean eye diameter, frequency of elliptical eyewalls and frequency of concentric eyewalls for the Gulf of Mexico, 1989-2005.
ix Figure 4.1 ...... 65 Frequency distribution of all vortex message reports. C = circular, IC = inner concentric, E = elliptical.
Figure 4.2 ...... 65 Frequency distribution of all vortex message reports (greater than 3 per bin).
Figure 4.3 ...... 66 Frequency distribution of circular eyewalls.
Figure 4.4 ...... 66 Frequency distribution of elliptical eyewalls.
Figure 4.5 ...... 67 Frequency distribution of concentric eyewalls (inner diameter).
Figure 4.6 ...... 67 Frequency distribution of concentric eyewalls (outer diameter).
Figure 4.7 ...... 68 Frequency distribution of Julian day.
Figure 4.8 ...... 68 Frequency distribution of latitude.
Figure 4.9 ...... 69 Frequency distribution of longitude.
Figure 4.10 ...... 69 Frequency distribution of time of day.
Figure 4.11 ...... 70 Vortex message parameters used to quantify the hurricane intensity theories of Eliassen (1951) and SW82.
Figure 4.12 ...... 70 Frequency distribution of 700 hPa temperature inside the eye.
Figure 4.13 ...... 71 Frequency distribution of 700 hPa temperature outside the eye.
Figure 4.14 ...... 71 Frequency distribution of 700 hPa temperature difference from inside eye and outside eye.
x Figure 4.15 ...... 72 Frequency distribution of dewpoint temp. inside the eye at 700 hPa.
Figure 4.16 ...... 72 Frequency distribution of eye dewpoint depression (temp. inside eye-dewpoint temp. inside eye) at 700 hPa.
Figure 4.17 ...... 73 Frequency distribution of temperature inside the eye at 850 hPa.
Figure 4.18 ...... 73 Frequency distribution of dewpoint temp. inside the eye at 850 hPa.
Figure 4.19 ...... 74 Frequency distribution of eye dewpoint depression (temp. inside eye-dewpoint temp. inside eye) at 850 hPa.
Figure 5.1 ...... 87 All rapid intensifiers represented in the eyewall phase space.
Figure 5.2 ...... 87 All rapid weakeners represented in the eyewall phase space.
Figure 5.3 ...... 88 All non-rapid intensifiers represented in the eyewall phase space.
Figure 5.4 ...... 89 Mean path of all vortex messages represented in the eyewall phase diagram for entire Atlantic basin, 1989-2005 (full version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development. A time designation for every 24 h from an initial time of zero up to 120 h is noted on the diagram.
Figure 5.5 ...... 90 Mean path of all vortex messages represented in the eyewall phase diagram for entire Atlantic basin, 1989-2005 (zoomed-in version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
xi Figure 5.6 ...... 91 Mean path of all vortex messages represented in the eyewall phase diagram for Atlantic basin minus Caribbean and Gulf of Mexico, 1989-2005 (full version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
Figure 5.7 ...... 92 Mean path of all vortex messages represented in the eyewall phase diagram for Atlantic basin minus Caribbean and Gulf of Mexico, 1989-2005 (zoomed-in version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
Figure 5.8 ...... 93 Mean path of all vortex messages represented in the eyewall phase diagram for the Caribbean, 1989-2005 (full version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
Figure 5.9 ...... 94 Mean path of all vortex messages represented in the eyewall phase diagram for the Caribbean, 1989-2005 (zoomed-in version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
Figure 5.10 ...... 95 Mean path of all vortex messages represented in the eyewall phase diagram for the Gulf of Mexico, 1989-2005 (full version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
Figure 5.11 ...... 96 Mean path of all vortex messages represented in the eyewall phase diagram for the Gulf of Mexico, 1989-2005 (zoomed-in version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
Figure 5.12 ...... 97 Best track positions for Hurricane Rita, 18-26 September 2005, courtesy of the National Hurricane Center (http://www.nhc.noaa.gov/2005atlan.shtml).
xii Figure 5.13 ...... 98 Eyewall phase diagram for Hurricane Rita (2005) using inner eye diameter for concentric eyewalls.
Figure 5.14 ...... 99 Eyewall phase diagram for Hurricane Rita (2005) using outer eye diameter for concentric eyewalls.
Figure 5.15 ...... 100 Eyewall phase diagram for Hurricane Rita (2005) using average of inner and outer eye diameters for concentric eyewalls.
Figure 5.16 ...... 101 Comparison of MSLP and eye size for Hurricane Rita of 2005.
Figure 5.17 ...... 101 Comparison of MSLP and temperature inside the eye for Hurricane Rita.
Figure 5.18 ...... 102 Comparison of the temperature inside the eye, dewpoint temperature inside the eye, and eye size of Hurricane Rita.
Figure 5.19 ...... 103 Best track positions for Hurricane Charley, 9-14 August 2004, courtesy of the National Hurricane Center (http://www.nhc.noaa.gov/2004atlan.shtml).
Figure 5.20 ...... 104 Eyewall phase diagram for Hurricane Charley (2004) using inner eye diameter for concentric eyewalls.
Figure 5.21 ...... 105 Comparison of MSLP and eye size for Hurricane Charley.
Figure 5.22 ...... 105 Comparison of temperature inside the eye and eye size for Hurricane Charley.
Figure 5.23 ...... 106 NHC best track for Hurricane Wilma, 15-25 October 2005, courtesy of National Hurricane Center (http://www.nhc.noaa.gov/2005atlan.shtml).
Figure 5.24 ...... 107 Eyewall phase diagram for Hurricane Wilma (2005) using inner eye diameter for concentric eyewalls.
xiii Figure 5.25 ...... 108 Eyewall phase diagram for Hurricane Wilma (2005) using average of inner eye and outer eye diameters for concentric eyewalls.
Figure 5.26 ...... 109 MSLP and eye diameter for Hurricane Wilma.
Figure 6.1 ...... 113 Graphical representation of time period between vortex messages.
Figure 6.2 ...... 113 Correlation between temperature inside the eye and MSLP for 700 hPa vortex message reports.
Figure 6.3 ...... 113 Future intensity change (change from point A to point B) compared to point A. Negative is future intensification.
Figure 6.4 ...... 114 Future eye size change (change from point A to point B) compared to point A. Negative is future eye contraction.
Figure 6.5 ...... 114 Example of pattern matching eyewall phase diagram trajectory segments to predict future trajectory in the eyewall phase diagram.
xiv ABSTRACT
There has been great energy focused on tropical cyclone intensity forecasting over the past thirty years. Toward the goal of providing more accurate intensity forecasts, the role of the environment of a tropical storm has been studied at great length over the past few years while the storm itself has not. There remains considerable work left toward understanding how the tropical cyclone structure itself can be used to aid intensity forecasting. One step toward this goal for the Atlantic is by dissecting a climatology of reconnaissance vortex message reports from the Atlantic basin between 1989 and 2005. Such an analysis will permit the comparison of tropical cyclone core structure measurements to know future intensity change. This vortex message data, which is collected from dropsondes and radar during flights into tropical disturbances, includes eye size, pressure, eye temperature, eye dewpoint, maximum flight level winds and other pertinent information. The number of occurrences for each vortex message characteristic as well as frequency plots of eye type, Julian day, latitude, longitude, temperature, dewpoint, and intensity change as a function of mean sea level pressure (MSLP) and eye size were created. The composite mean eyewall cycle was analyzed, along with the cycles of concentric eyewalls and elliptical eyewalls. Based on this vortex message climatology and analysis, an eyewall phase diagram was developed that graphically shows the evolution of a storm. These eyewall phase diagrams show how eyewall cycles evolve in time using mean MSLP, mean eye size, concentric eyewall frequency, and elliptical eyewall frequency data. Case studies include analysis of a storm undergoing an eyewall replacement cycle (Rita 2005), a rapidly weakening storm (Charley 2004), and a rapidly intensifying storm (Wilma 2005). It was discovered in this study that core storm data collected from vortex data messages could be used to confirm theories on tropical cyclone intensity. Preliminary attempts at simple forecasts comparing eye characteristics and future intensity change were done. Indeed, short-term forecasts of intensity change should utilize storm-specific structure, beginning with an analysis of that structure in intensification versus weakening events. Further work involving pattern matching trajectories and trajectory segments to
xv forecast future storm trajectory in the eyewall phase diagram may lead to helpful analog tropical cyclone intensity forecast guidance.
xvi CHAPTER 1 INTRODUCTION
Tropical cyclones have had a major impact on the United States, both in terms of financial damage and human casualties. As more people have moved to the Eastern and Gulf coasts of the U.S., the threat of these storms has increased dramatically. The hurricane seasons of 2004 and 2005 demonstrated just how much devastation an active hurricane season can cause. By providing more accurate track and intensity forecasts, the lives lost and injuries caused by tropical cyclones may be decreased. Different kinds of intensity forecast tools and models have been developed over the past few decades, and these techniques have both advantages and disadvantages. An improved understanding of the current theories of eyewall contraction, eyewall replacement cycles, and hurricane intensity change are key for finding new ways to improve intensity forecasts. This research begins with a review of the various datasets that have been used for intensity forecasting. A review of the different models that have been developed to forecast intensity change is also described in this chapter. Through an examination of the prior research, it is anticipated that the clear role of this proof-of-concept research can be illustrated.
a. Datasets
The National Hurricane Center (hereafter NHC) has analyzed and archived tropical cyclone data for the Atlantic basin for many decades. One of the first compilations of data was the tropical cyclone data tape for the years 1886-1977 by Jarvinen and Caso (1978, hereafter JC78). This analysis, which contained the dates, tracks, maximum wind speeds, and minimum central pressures over a 92-year period, was later referred to as the HURicane DATa set, or the HURDAT dataset (JC78). For the earliest years of the dataset, ship and land reports were the source for tropical cyclone data. Beginning in 1944, aircraft reconnaissance recorded storm information, and beginning in the mid 1950’s coastal radar provided more information (JC78). The
1 greatest contribution to the tropical cyclone detection dataset was the implementation of visible and infrared satellites as well as the placement of numerous buoys throughout the Atlantic. Using this additional information, this dataset was updated by Jarvinen et al. (1984). One of the most complete databases of tropical cyclone activity is the NHC best track dataset, which was originally compiled by Neumann et al. (1993). This database provides statistical summaries and storm track data every six hours for North Atlantic storms between 1871 and 1992. For the years 1871-1963, most storm track and intensity data came from the U.S. Weather Bureau technical paper number 55, with various additions and changes by various authors during the following decades. The main data source for the years 1964-1992 was the annual summary reports of tropical activity in the Atlantic basin written by the NHC and the Tropical Prediction Center. Data from 1993 onward were added online to the best track at http://www.nhc.noaa.gov. Pennington et al. (2000) developed the NHC extended best track dataset (EBT), which took the data compiled by Neumann et al. (1993) and added storm size parameters that were collected from ship reports, surface reports, aircraft reconnaissance, and satellites. The list of BT parameters is listed in Table 1.3. Numerous models utilized the NHC HURDAT and BT datasets to improve intensity forecasts. The next few sections will discuss these models.
b. Climatology and Persistence Models
The CLIPER (CLImatology and PERsistence) model developed by Neumann (1972) uses climatology and persistence predictors to statistically predict tropical cyclone motion. The CLIPER model has provided a benchmark for the development of more sophisticated models, and it has proven to be quite reliable for the forecast of storm motion in the easterlies (Jarvinen and Neumann 1979, hereafter JN79). A model known as SHIFOR (Statistical Hurricane Intensity Forecast) was developed for intensity forecasting using statistical regression techniques (JN79). The predictors used in SHIFOR were derived from the CLIPER model (Neumann 1972), and the data used in SHIFOR were obtained from the NHC’s HURDAT dataset (JC78). Prediction equations were developed using multivariate regression analysis with the
2 predictands being the 12 h through 72 h maximum wind speed change at 12 h intervals (JN79). The predictors were initial latitude, initial longitude, average zonal and meridional wind speed over the past 12 h, maximum wind speed, and previous 12 h change in maximum wind speed (JN79). This model was developed with the goal of achieving the same success at forecasting intensity that the CLIPER model has achieved for forecasting storm track (JN79). This technique provides skill compared to CLIPER for 12-24 intensity forecasts, but beyond 24 h the accuracy of the intensity forecasts drop quickly when compared to track forecasts (DeMaria and Kaplan 1999). JN79 concluded that climatology and persistence can only reasonably predict hurricane intensity up to 12 h. Beyond that, another method must be used, such as considering environmental factors and the synoptic data that describe them, as discussed next.
c. Statistical-Synoptic Models
Merrill (1987) was one of the first researchers to incorporate a wide range of synoptic data into a statistical prediction model of intensity. The data used included the NHC HURDAT dataset (Jarvinen et al. 1984), monthly means of sea surface temperatures (Reynolds 1982), and 200 hPa tangential flow. The effects of coastlines and elevated terrain on intensity change also were taken into effect by the Merrill model. This model did not provide any significant improvements over climatology and persistence. According to Merrill (1987), one way to improve intensity forecasts would be to identify the roles that environmental factors and internal convection have on intensity changes. The DK94 Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic basin uses climatology, persistence, synoptic predictors, and sea surface temperatures in a multiple regression scheme to generate forecasts. This is different from the SHIFOR model, which only uses climatology and persistence to generate forecasts. The database includes all 38 storms between 1989 and 1992 with 11 cases from 1982 to 1988. The synoptic predictors in DK94 include flux convergence of eddy angular momentum at 200 hPa, persistence, vertical shear of the horizontal wind, and the difference between current storm intensity and an estimate of Maximum Potential
3 Intensity (MPI) (Emanuel 1988). The synoptic data come from the initial analysis of the National Centers for Environmental Prediction (NCEP) medium range forecast model (also known then as the Aviation model). Sea surface temperature fields were obtained from monthly climatological means. The SHIPS model used a jackknife procedure, which involves removing each storm from the data sample and then deriving regression coefficients (DK 94). The SHIPS model showed a 10%-15% improvement over the SHIFOR model, but there still was a great deal of variability in intensity changes that could not be explained (DK94). This variability may be due to processes at the storm scale, such as eyewall replacement cycles (DK94), which are not resolved by the gridded data, but can be resolved by reconnaissance data. In 1996 the SHIPS model was implemented into the NHC operational forecast environment for both the Atlantic and Eastern Pacific basins (DeMaria and Kaplan 1999). There were slight improvements over SHIFOR in the Atlantic basin, but no skill compared to climatology and persistence was seen in the Eastern Pacific basin. The next step for the SHIPS model was to transform it from a statistical-synoptic model to a statistical-dynamical model, as discussed next.
d. Statistical-Dynamical Models
DeMaria and Kaplan (1999, hereafter DK99) upgraded the SHIPS model even further. Improvements included using synoptic predictors from initial and forecast fields instead of just from initial fields (from the Aviation Model), and using a larger sample (DK99). The new version of SHIPS also removed the Aviation model based storm circulation from the initial analyses using a Laplacian filter (DK99). This was done since the model’s representation of the storm location was not always accurate (DK99) and could contaminate the estimate of the storm environment. Adding the predictors from forecast fields made SHIPS more of a statistical-dynamical rather than a statistical- synoptic model. This new version of SHIPS was first implemented in 1997 for both the Atlantic and eastern Pacific basins. Results showed that the forecasts had statistically significant skill when compared to climatology and persistence up to 72 h, with a larger forecast improvement for the Atlantic than the Eastern Pacific (DK99). The table of
4 predictors used in the SHIPS model from DK99 is given in Table 1.1. The limitations of this technique are that SHIPS only considers surrounding environment influences on intensity changes. Environmental impacts on a storm include sea surface temperatures and shear. If measurements of the storm itself, such as eye characteristics, were implemented into the SHIPS model, it might provide an improved intensity forecast. Following the success of KD99, Kaplan and DeMaria (2003, hereafter KD03) tackled the challenge of forecasting rapid intensification. The authors acknowledged that the inability to forecast rapid intensification was related to the limited understanding of intensity change (KD03). For many years, researchers have focused on how the ocean, inner-core processes, and environmental interactions can affect intensity changes in a tropical cyclone (KD03). KD03 focused on the extremes of intensity change rather than attempting to forecast all intensity changes. The database for KD03 consisted of climatological data from the NHC HURDAT file (Jarvinen et al. 1984), which has 6- hourly estimates of position, mean sea level pressure and maximum winds for all Atlantic tropical cyclones from 1851 onward (KD03). The KD03 database also contained variables from the SHIPS database, which had 12-hourly synoptic information for Atlantic based tropical cyclones from 1989 to 2002. Each of the variables in Table 1.2 was analyzed at the beginning of each 24 h period that the storm was over water and was still tropical (KD03). KD03 defined rapid intensification for the Atlantic basin as the 95th percentile of all 24 h intensity changes over water of tropical cyclones between 1989 and 2000. This equates to a 15.4 ms-1 (30 kt) increase in maximum sustained surface winds over a 24 h period (KD03). A technique for estimating the probability of rapid intensification was developed using five predictors: the previous 12 h intensity change, sea surface temperature at each initial tropical cyclone location, 850-700 hPa relative humidity, 850- 200 hPa vertical shear, and the difference between current intensity and MPI (Emanuel 1988) (KD03). This technique was run operationally in real time throughout the 2001 Atlantic hurricane season, and the results were compared to a dependent sample of RI storms from 1989-2000 (KD03). This rapid intensification forecasting tool showed that the RI probabilities computed during the 2001 season were similar to the dependent sample of RI storms from 1989-2000 (KD03). The results from the KD03 technique
5 were encouraging, suggesting that it could eventually lead to a tool for hurricane forecasters. KD03 suggested that implementing satellite imagery and ocean heat content data to the RI forecasting tool might improve it even further. If details from the core of the storm that are provided by the vortex message dataset were implemented as well, it might improve the KD03 technique even further. We need to be able to identify rapid intensification, and the associated eyewall contraction and eyewall replacement cycles, which are discussed next. To understand eyewall contraction and concentric eyewalls, the Eliassen (1951) model of secondary circulations should be reviewed. Starting with the primary circulation, which is the cyclonic/anticyclonic circulation in the x-y plane, a radial heating profile is produced by ascent and latent heat release (Figure 1.1). This radial heating distorts the gradient wind/thermal wind balance. In order to restore gradient wind/thermal wind balance, a secondary circulation forms in the r-z plane. This secondary circulation goes toward the center of the primary circulation, upwards along the top of the center of the primary circulation, and then outwards at the top. Depending on the magnitudes of static, inertial, and barotropic stability, the secondary circulation can be compressed vertically and/or horizontally (Figure 1.2). Shapiro and Willoughby (1982, hereafter SW 82) developed a statistical- dynamical model that describes forced secondary circulations in tropical cyclones. This model was based upon the Eliassen (1951) model. First, a source of heat or momentum near a tangential wind maximum will cause isobaric heights to fall quickly just inside the radius of maximum winds, and heights to fall more slowly outside the maximum winds (SW82). This leads to an intensification in the height and pressure gradient. These height falls cause a peak in the tangential wind tendency, and since this occurs inside the wind maximum, the radius of maximum winds is brought inward as a response to heating (SW82). This contraction of maximum winds also is associated with contraction of the storm’s eyewall for an intensifying hurricane (SW82). The warm air at the base of the eyewall ascends moist adiabatically, with nearly constant angular momentum, to the top of the eyewall. At the top of the eyewall, this air either is evacuated radially outward or subsides into the eye (Figure 1.3). As the air descends in the eye, it warms dry adiabatically and strengthens the strong temperature
6 gradient between the inside and outside of the eye. As surface pressure continues to fall from this warming, the secondary circulation intensifies, the gradient wind/thermal wind balance is further disturbed, and the eyewall diameter becomes even smaller, increasing the subsident warming. Since the temperature inside the eye is increasing, and the temperature outside the eye remains mostly unchanged, an even stronger temperature gradient forms. The secondary circulation continues to strengthen in an unsuccessful attempt to restore gradient wind/thermal wind balance. In short, eyewall contraction occurs from an unsuccessful attempt to restore gradient wind/thermal wind balance with constantly changing heating. Therefore, examining the temperature difference between inside the eye and outside the eye may provide insight into future changes in the primary circulation and thus how much a storm may strengthen. SW82, while a breakthrough, only addressed single eyewall contraction. The next study that will be discussed analyzes how concentric eyewalls evolve and how concentric eyewalls are related to storm intensity. Willoughby, Clos and Shoreibah (1982, hereafter WCS82) used radar, wind and eyewall data from reconnaissance flights to analyze the evolution of eyewalls and secondary wind maxima in terms of a convective ring model (SW82). As a storm intensifies and grows in size, the advection of angular momentum toward the eye is enhanced (Figure 1.4). If a local tangential wind maximum occurs outside the original eyewall (caused by as yet an incompletely understood process), a convective ring may form as a result. This secondary ring begins to develop into a secondary eyewall, which interferes with the radial advection of angular momentum that drives the primary eyewall. This secondary eyewall causes two primary and two secondary circulations. As air begins to descend in between the two eyewalls, it causes warming and pressures to fall. Therefore, the temperature and pressure gradient between the center of the storm and the second eyewall decreases, but these gradients increase between the second eyewall and the edge of the storm. This causes the secondary eyewall to contract and the original eyewall to remain mostly stable. Eventually, the secondary eyewall develops at the expense of the original eyewall. Once the original eyewall vanishes, the eye heating spreads out to a larger area, causing the minimum MSLP to increase hydrostatically. The mechanism of how rings of convection form is poorly understood, but theories include
7 the role of precipitation-induced downdrafts (Zipser 1977), or temporary periods of symmetric instability (Bennetts and Hoskins 1979). The process of secondary eyewall formation explains why after an eyewall replacement cycle an increase in pressure and eye size usually is seen. The theories put forth by SW82 and WCS82 can be confirmed by the core storm data provided by vortex data messages. Therefore, many of the approaches taken in this research will utilize the results and understanding provided by SW82 and WCS82 WCS82 also attempted to track the evolution of maximum winds of Hurricanes Anita (1977) and David (1979) by fitting trend lines to plots of maximum winds and radii of those maximum winds. The authors then switched focus to Pacific typhoons where they attempted to independently relate a concentric eye wall cycle to intensity change, and tried to determine how often concentric eye walls occurred (WCS82). WCS82 concluded that concentric eyes become more common as intensity increases and that a concentric eye indicates the conclusion of a deepening phase, but not necessarily a weakening phase. The authors noted that using their conclusions in forecasting should be done cautiously. This caution is due to their using only three years worth of data, less impressive eyewall replacement cycles could have been overlooked, and some of the cycles may have been present for some time before they were reported (WCS82). They acknowledged the importance of understanding the concentric eye cycle, and how it may lead to improved forecasts. Therefore, understanding concentric eyewall cycles is the motivation for the research presented here. Since the current study involves a climatology of eyewall characteristics, a previous compilation of certain eyewall size parameters is presented in the following section.
e. Climatology of Atlantic Basin Storm Data
Kimball and Mulekar (2004, hereafter KM04) developed a 15-year climatology of North Atlantic tropical cyclones’ size parameters based on the NHC extended best track dataset (Pennington et al. 2000) for 1988-2002. They examined eyewall radius, maximum winds, gale-force winds, damaging-force winds, hurricane-force winds, and the outermost closed isobar. The distributions of these six parameters, both spatially and
8 seasonally, were compiled, and their behavior were analyzed in various states, sizes, and categories (KM04). KM04 found that storms with smaller eyewalls (less than 15 km) accounted for 26.6% of all tropical cyclones, while storms with larger eyewalls (greater than 31 km) accounted for 14.9% of all tropical cyclones. Further analysis of the eyewall radius data showed that there could be errors and missing eye size data. The reasons for these errors include instrumental errors, eye obscuration by cloud cover, or a poorly defined eye after or during an eyewall replacement cycle (KM04). KM04 did not develop a new technique for intensity forecasting; rather, they provided a detailed climatology of TC size parameters over a 15-year period. They used structure data that were from reliable sources (reconnaissance) as well as less reliable sources (climatological extrapolation and satellite), thus decreasing the homogeneity and utility for forecasting. By providing a more robust climatology of eyewall size parameters, the current study may help researchers even more. Since the vortex messages can provide a great deal of information on storm structure, they can be used to analyze eyewall characteristics and eyewall cycles. A better understanding of eyewall characteristics, the timing of eyewall contractions, and the timing of eyewall replacement cycles, hopefully will produce more accurate forecasts. A study of annular hurricanes (Knaff et al. 2003) strived to understand the structural characteristics and formation of this peculiar type of storm. Since annular hurricanes are very symmetric with a large eye and constant intensity, they are a challenge to forecast (Knaff et al. 2003). Most hurricanes either have a steady, or rapid increase in intensity as they move across the Atlantic basin. The NHC extended best track (Pennington et al. 2000) dataset was used for eye size and wind information. Knaff et al. (2003) discovered two ways to identify these storms: a) digital brightness temperatures from infrared imagery, and b) examining the tropical cyclone’s environmental conditions to determine where and when an annular hurricane may exist (Knaff et al. 2003). As with previous forecasting studies, Knaff et al. (2003) focused on environmental conditions rather than focusing on the cyclone’s core.
9 f. Satellite Based study
A recent presentation by Kossin et al. (2006) discussed the development of a new method to forecast eyewall replacement cycles in order to improve tropical cyclone intensity forecasts. Kossin et al. (2006) reiterated the problems with trying to identify and forecast storms undergoing eyewall replacement and storms with annular characteristics. On average, the intensity of annular storms is under-forecast and the intensity of eye wall replacement cycle storms is over-forecast (Kossin et al. 2006). Kossin et al. (2006) used infrared and visible satellite data, sea surface temperatures, and reanalysis fields such as shear and moisture to develop a tool to diagnose the formation of secondary eyewalls. Their methodology created composite satellite and sea surface temperature images of an area around a storm when a secondary eyewall forms and when it does not form (Knaff et al. 2006). The next step was to identify features in the composites, and then apply these features to various classifications algorithms (Knaff et al. 2006). The results may give a forecaster the probability of secondary eyewall formation using real-time measurements from both satellites and analyses (Knaff et al. 2006). Results describing the accuracy of this technique have not yet been released since it only was implemented for the 2006 hurricane season.
g. Reconnaissance Based Study
A study involving the use of vortex message data to predict hurricane intensity change was presented by Samsury and Rappaport (1991, hereafter SR91). They explored the possibility that the structure and intensity of a tropical cyclone at a certain time could be related to the intensity and structure of the same storm at a future time. SR91 used aircraft wind data from on-board radars, the NHC best track dataset for maximum winds, storm track at six-hour intervals, and sea surface temperatures in the environment of the storm. Five different mean wind profiles were established based on the most prominent characteristic of the maximum wind speed (SR91). These five profiles are described in Table 1.4. An initial time was assigned to each profile (T), which was defined as (I+O)/2, where I was the time at the beginning of the inbound leg and O was the time at
10 the end of the outbound leg (SR91). An intensity also was assigned based on the NHC best track data, and this intensity then was linearly interpolated to time T. Results showed that after 12 h, the distant, broad, and broad/dual categories had an average increase of six to eight kt in maximum winds. The dual profile did not show much change, and the narrow profile showed a weakening of seven kt. These trends extended to 24 h but then disappeared (SR91). The research presented in the current study makes an attempt at hurricane intensity forecasting using various size and temperature parameters instead of wind information.
h. Objectives
Over the past 35 years, various models have been developed to forecast hurricane intensity using climatology, persistence, synoptic data, satellite data, and reconnaissance data. Each model has had small successes, but for the most part, forecasting hurricane intensity has lagged behind hurricane track forecasting (KD03). Although a climatology of North Atlantic tropical cyclone size parameters has been developed for 1989-2004 based on the NHC extended best track dataset, the study to be presented here is the first to develop and utilize an extended climatology of vortex data messages. The current study is different from previous studies in that the current study focuses on details of the storm core, which are provided by reconnaissance vortex messages, as opposed to the environment surrounding the storm. A downside of the vortex message dataset is that it does not contain as many storm track points as the NHC best track dataset. This is due to the fact that only a small percentage of all storm intensities are examined by reconnaissance. Despite the smaller amount of data, it is easier to see eyewall replacement cycles and track a storm’s size, structure and development, since a vortex message typically is completed every 3.4 h, as calculated from the vortex message dataset. These storm characteristics can be difficult to see in satellite images due to limits in resolution (Dorst 2007), thus limiting the utility of the prior studies. The overall goal of the current study is to produce a climatology of Atlantic basin vortex message reports and then use this climatology to aid in forecasting
11 short-term changes in hurricane intensity by developing preliminary graphical, visual, and statistical tools. The study will be divided into five main sections. The data and methodology section (Chapter two) will describe the data being used, how it was collected, and the approaches used to analyze the data. Chapter three will describe the climatology of the vortex message dataset including occurrence plots and the distribution of vortex messages across the Atlantic basin. Chapter four examines frequency plots of various eye parameters for all vortex message reports, and for reports split up by flight level. Chapter five proposes an experimental eyewall phase diagram and examines composites and various case studies within it. Chapter six examines preliminary forecasting results. The final chapter presents conclusions and ideas for future studies.
12
Figure 1.1 – Schematic of primary/secondary circulation (Salby 1996).
Figure 1.2 – Cross-section through the core of a tropical cyclone, which shows the secondary circulation (Palmen and Newton 1969). Pressure (solid) and temperature (dashed) are on the left side and temperature deviations from a standard atmosphere are on the right side (dashed-dotted).
13
Figure 1.3 – Secondary circulation induced in a balanced vortex by a source of heat. Distortions induced by I2 (inertial stability) (Elsberry 1995).
Figure 1.4 – Graphical representation of the secondary circulation and distribution of precipitation for a tropical cyclone (Willoughby 1988).
14 Table 1.1 – SHIPS predictors from Kaplan and DeMaria (1999) Predictors Description Source POT Maximum possible intensity-initial Aviation Model intensity (AVN) SHR Magnitude of 850-200 hPa vertical AVN shear DVMX Intensity change during previous 12 h NHC Best Track REFC 200 hPa relative eddy angular AVN momentum flux convergence PEFC (removed 200 hPa planetary eddy angular AVN 1995) momentum flux convergence JDAY Julian day NHC Best Track LONG (removed Initial storm longitude NHC Best Track 1994) DTL (removed 1994) Distance to nearest major landmass SIZE (removed 1997) 850 hPa relative angular momentum AVN DSHR (removed Time tendency of vertical shear AVN 1996) magnitude POT2 POT squared AVN T200 (added 1995) Avg. 200 hPa temperature within 1000 AVN km of storm center U200 (added 1995) Avg. 200 hPa zonal wind within 1000 AVN km of storm center Z850 (added 1997) Avg. 850 hPa vorticity within 1000 km AVN of storm center LSHR (added 1997) SHR times the sine of the initial storm AVN latitude D200 (added 1998) Avg. 200 hPa divergence within 1000 AVN km of storm center SPDX (added 1998) Zonal component of initial storm AVN motion vector VMX (added 1998) Initial storm maximum wind NHC Best Track
Table 1.2 – Variables from SHIPS (DK99) and NHC HURDAT database used in Kaplan and DeMaria (2003). Variable Description Units Source VMX Maximum sustained surface wind speed ms-1 HURDAT LAT Latitude °N HURDAT LON Longitude °W HURDAT SPD Storm speed of motion ms-1 HURDAT DVMX Intensity change during the previous 12 h ms-1 HURDAT USTM U component of storm motion ms-1 HURDAT JDAY Absolute value of Julian date --- HURDAT SST Sea surface temperature °C SHIPS POT Maximum potential intensity (MPI) – VMX ms-1 SHIPS
15 SHR 850-200 hPa vertical shear averaged from r = ms-1 SHIPS 200-800 km U200 200 hPa u component of wind averaged from r = ms-1 SHIPS 200-800 km T200 200 hPa temperature averaged from r = 200-800 °C SHIPS km RHLO 850-700 hPa relative humidity averaged from r = % SHIPS 200-800 km Z850 850 hPa relative vorticity averaged for r ≤ 1000 10-7s-1 SHIPS km REFC 200 hPa relative eddy angular momentum flux ms-1day-1 SHIPS convergence averaged from r = 100-600 km SLYR Pressure of the center of mass of layer for which hPa SHIPS the environmental winds best match the current storm motion averaged from r = 200-800 km
Table 1.3 – Variables and descriptions for the National Hurricane Center’s Extended Best Track Dataset, from Pennington et al. (2000) and utilized by Kimball and Mulekar (2004). Variable Description Units Name Storm Name --- Year Storm Year --- Month Month of storm --- Day Day of storm --- Time Hour of observation, every six hours --- Psmin/mslp Minimum sea level pressure hPa VMAX Maximum winds ms-1 Lat Latitude °N Lon Longitude °W REYE Radius of eye km RMW Radius of maximum winds km R33 Radius of 32.9 m/s winds km R26 Radius of 25.7 m/s winds km R17 Radius of 17.5 m/s winds km ROCI Radius of outermost closed isobar km POCI Pressure of outermost closed isobar hPa
16 Table 1.4 – Wind profile types established by Samsury and Rappaport (1991) Profile Type Description Distant Radius of maximum winds of at least 60 km on one or both sides of the storm center. Broad Radius of maximum winds of less than 60 km on both sides of the storm center and wind speed decreases outward from RMW by less than 50% over 50 km. Narrow Radius of maximum winds of less than 60 km and wind speed decreases outward from RMW by at least 50% over 50 km on at least one side of storm center. Dual Two distinct wind maxima on both sides of the storm center. Broad/Dual Meet the requirements of Broad on one side of the storm center and Dual on the other side of the storm center.
17 CHAPTER 2 DATA AND METHODOLOGY
a. Data
The first step of this study involved manually compiling National Hurricane Center aircraft vortex data messages between 1989 and 2005 into a single database. Manual compilation was necessary because the messages have not had a consistent format since their inception in 1989. Vortex data messages provide information about a tropical system’s core such as latitude and longitude, various temperatures, winds, and other characteristics. Most of the data in these messages are collected from dropsondes released from airplanes flown by the Air Force Reserve 53rd Weather Reconnaissance Squadron, better known as the “Hurricane Hunters” (Dorst 2007). The airplanes are dispatched to tropical waves and depressions to find the center of the storm, any signs of a closed circulation, or signs of strengthening (Dorst 2007). Once a flight collects the data, they are quality controlled by the crew of the flight and the Air Force unit known as CARCAH, or Chief Aerial Reconnaissance Coordination, All Hurricanes, which is collocated with the National Hurricane Center (NHC 2005). After quality control is completed, the CARCAH disseminates the data to forecasters and then the public (NHC 2005). When needed and available, supplemental data from on-board radars are used to determine the character and diameter of an eyewall (OFCM 2007). A large number of dropsondes over a designated area is released, and the data from each dropsonde, combined with flight level data and radar data, compose the vortex data message. An example message from Hurricane Ivan in 2004 is given below:
URNT12 KNHC 120005 VORTEX DATA MESSAGE A. 12/0005Z B. 18 DEG 09 MIN N 79 DEG 35 MIN W C. 700 MB 2295 M D. 50 KT E. 314 DEG 102 NM F. 022 DEG 146 KT G. 288 DEG 010 NM H. 910 MB
18 I. 12 C/ 3123 M J. 22 C/ 3122 M K. 13 C/ NA L. CLOSED M. CO15-17 N. 12345/7 O. 0.1/ 1 NM P. AF966 2009A IVAN OB 29 MAX FL WIND 150 KT SE QUAD 2042Z.
The data that were used for this study include sections A, B, C, H, I, J, K, L, M, and P. Data from section D were missing from most vortex data messages; therefore, it was not used. Data from sections E, F, G involve wind reports, which were not the main focus of this study. Sections N and O describe flight fix position and fix accuracy, which also were not pertinent for this study. A breakdown of the vortex message is as follows: • A. Date and time (UTC) of the fix of the center of the storm (The 12th at 0005 UTC, for the above example). • B. Latitude and Longitude of the center in degrees and minutes (18 deg. 9 min. north, 79 deg. 35 min. west). • C. Flight level in millibars and meters (700 mb/hPa, 2295 m). • H. Minimum sea level pressure (in mb/hPa) which is measured either directly by a dropsonde or extrapolated from instruments on the aircraft (910 mb/hPa). • I. Maximum flight level temperature (°C) and pressure altitude (m) outside the eye (12°C, 3123 m). • J. Maximum flight level temperature (°C) and pressure altitude (m) inside the eye (22°C, 3122 m). • K. Dewpoint (°C) of the storm center (13°C). • L. The character of the eye which is either “CLOSED WALL” or “OPEN ‘direction’” when there is a break in the wall. If there is less than 50% of a wall then “N/A” is recorded (Closed eyewall). • M. Shape and diameter of the eye in nautical miles. The three shapes are circular (C), concentric (CO), and elliptical (E) (Concentric, Inner eye of 15 nmi and outer eye of 17 nmi). • P. The remarks section, which includes a great deal of information, but the most important includes the maximum winds (along with the time and quadrant), and
19 the highest recorded temperature at that particular flight level (Maximum flight level winds of 150 kt in the southeastern quadrant of the storm at 2042 UTC).
As mentioned earlier, since the vortex data messages are not standardized for every year and every storm, all data needed to be entered manually. Not every vortex message was compiled and analyzed. Messages from recon missions that go through a tropical wave or disturbance that ends up not developing into a significant tropical cyclone were not considered useful. Since this study focuses on eye characteristics, a minimum criterion for a storm to be included in the database was required: at least one vortex message with a recorded eye diameter. Also, in order to avoid minor tropical depressions or very weak tropical storms with questionable evidence of an eyewall, a maximum wind of at least 45 kt was required. Table 2.1 shows a list of all storms incorporated into the vortex message database, along with the time period each storm was flown, and the total number of vortex messages for the storm. The Atlantic basin was the main focus of this study since it is flown much more often than the Eastern Pacific, thus providing a more robust and representative dataset. The first approach to analyzing the vortex message dataset was to decide which storm characteristics to study. Certain measurements provided a better representation of a storm, and at greater detail than others. Mean sea level pressure (MSLP) was used since it, along with maximum winds, was the best way to describe the evolution of storm intensity. The maximum flight level temperature inside (MFLT in) and outside (MFLT out) of the eye were used since they may provide precursor information about how a storm may be strengthening or weakening from Sawyer-Eliassen nonlinear balance (see Introduction, Section d). The maximum flight level pressure altitude inside and outside of the eye were recorded since they quantify the strength of the flight level vortex through the isobaric height gradient. The eye’s dewpoint was used to calculate the dewpoint depression, which could reveal trends of drying or moistening, and changes in the strength of the secondary circulation (see Introduction, Section d). Eye characteristic (closed or open), shape (circular, concentric, elliptical). and size were used to document the evolution of eyewall replacement cycles. The various steps taken to analyze the vortex message data and the new graphical tools developed are discussed next.
20 b. Visualization i. Distribution and Frequency The locations of all vortex message points were plotted to see how they were distributed throughout the Atlantic basin. All points were plotted by Saffir-Simpson category, eye size, month, rapid intensification/weakening, and eye type. A FORTRAN program was written to group the MSLP, eye size, and other variables for all storms into 17 bins (Table 2.2). Each storm was assigned to a bin based on its eye size; so, only the vortex messages with an eyewall necessarily were used. The bins were not split up linearly since the distribution of vortex messages was not uniform for all eye sizes (Figure 2.1). In order to capture the smallest eye diameters, the bins also needed to be smaller at these sizes. Once the data were categorized into the different bins, they were converted to binary format and plotted. The data were divided into the three most common flight levels: 1500 ft, 850 hPa, and 700 hPa. This was necessary since temperatures and dewpoints varied a great deal between each level. To see the distributions of variables having stable statistical representations, plots were made only for bins having at least three values. Graphs showing the number of occurrences for each vortex message parameter also were completed. This was done for MSLP, eye temperature, flight level pressure altitude, eye dewpoint, and maximum wind speed at flight level. The vortex messages also were categorized by eyewall shape and eyewall type. Vortex message climatology was compared to best track climatology to see whether the vortex message database uniformly sampled the full basin in time and space. Plots of the mean values of MSLP and eyewall diameter as a function of the time since eyewall formation are presented in Chapter three. These plots use four-hour bins and a 1-2-1 smoother using GrADS. The mean plots also show the percent frequency of concentric and elliptical eyewalls as a function of time since eyewall formation. These analyses were done to quantify and describe the composite mean eyewall lifecycle over a 17-year period, and to compare against the Sawyer-Eliassen theory.
21 ii. Eyewall Phase Diagram A major component of this study was the development and use of the eyewall phase diagram as a visualization and potential forecast tool. An example of the eyewall phase diagram is seen in Figure 2.2. This diagram plots the lifecycle of a storm as a function of intensity, eye size, and eye type with the analysis and timing of eyewall replacement cycles. The eyewall phase diagram is similar to the frequency plot diagram, with MSLP on the x-axis and the log of eye size on the y-axis. A listing of a particular storm’s vortex messages by day and time are displayed along the right hand side as each vortex message point is displayed in the phase space. Colored lines represent the six- hour intensity change between times, and each point has a shape based on the eye wall type. As a first attempt, only the inner eyewall of concentric eyes were plotted. This diagram is an analog to the cyclone phase space diagram developed by Hart (2003), although with decidedly different goals. The eyewall phase diagram can provide information for a single storm, or it can show the mean evolution of numerous storms’ intensity compared to eyewall diameter. The mean value of storm intensity and eye size are shown in Chapter five for the entire Atlantic basin, and the basin divided into three sections, the Caribbean, the Gulf of Mexico, and the eastern Atlantic. In pursuit of relationships between the vortex message parameters and future intensity change, scatterplots of a vortex parameter at a certain time were compared with the storm’s future minimum surface pressure. This was done for eye temperature, eye size, eye dewpoint, and pressure for the three flight levels. The goal was to find correlations between a parameter and future intensity change, leading to forecast tools and methods. Future change in intensity and eye size was also done as a function of MSLP and eye size, which provided much more interesting results.
22 Frequency of Eye Diameter-All Vortex Messages 300 250 200 150
Count 100 50 0
2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 0 3 8 5 5 5 5 1 1 1 1 1 2 2 2 2 2 3 3 3 3 4 4 4 5 6 7 8 Eye Diameter (nmi)
Figure 2.1 – Eye diameter frequency in the Atlantic basin from all vortex messages.
23
Figure 2.2 – Example of the eyewall phase diagram (Hurricane Rita, 2005).
24 Table 2.1 -- Storms included in the vortex message dataset Type/Peak # of Vortex VortexMessages/ Intensity Storm Beg. Date End Date Messages day Hurricane-1 Chantal 7/31/1989 8/1/1989 12 12.1 Hurricane-2 Dean 8/2/1989 8/6/1989 16 4.5 Hurricane-4 Gabrielle 9/4/1989 9/7/1989 28 7.9 Hurricane-5 Hugo 9/15/1989 9/22/1989 43 7.0 Tropical Storm Iris 9/19/1989 9/19/1989 7 9.5 Hurricane-1 Jerry 10/13/1989 10/15/1989 17 8.4 Tropical Storm Karen 11/30/1989 12/2/1989 6 2.6 Tropical Storm Arthur 7/24/1990 7/26/1990 10 5.0 Hurricane-1 Bertha 7/28/1990 8/2/1990 23 4.7 Hurricane-2 Diana 8/6/1990 8/7/1990 7 6.7 Hurricane-3 Gustav 8/28/1990 8/31/1989 14 4.2 Hurricane-1 Klaus 10/3/1990 10/9/1990 29 5.5 Hurricane-1 Lili 10/11/1990 10/13/1990 7 4.1 Hurricane-1 Nana 10/16/1990 10/20/1990 23 6.3 Hurricane-3 Bob 8/17/1991 8/19/1991 34 14.2 Hurricane-4 Claudette 9/6/1991 9/9/1991 18 7.4 Hurricane-2 Grace 10/27/1991 10/29/1991 16 9.1 Hurricane-5 Andrew 8/19/1992 8/26/1992 64 9.1 Tropical Storm Danielle 9/22/1992 9/25/1992 22 7.1 Tropical Storm Earl 9/30/1992 10/2/1992 14 7.0 Hurricane-3 Emily 8/26/1993 9/1/1993 52 7.9 Hurricane-2 Gert 9/19/1993 9/20/1993 15 11.5 Tropical Storm Alberto 7/2/1994 7/3/1994 13 11.4 Hurricane-2 Florence 11/6/1994 11/6/1994 2 28.2 Tropical Storm Barry 7/7/1995 7/8/1995 4 5.1 Tropical Storm Chantal 7/13/1995 7/18/1995 28 6.0 Hurricane-1 Erin 7/31/1995 8/3/1995 40 11.5 Hurricane-4 Felix 8/11/1995 8/21/1995 69 6.8 Hurricane-2 Iris 8/23/1995 9/2/1995 37 3.7 Hurricane-4 Luis 9/3/1995 9/10/1995 47 7.2 Hurricane-3 Marilyn 9/13/1995 9/19/1995 56 9.1 Hurricane-4 Opal 10/1/1995 10/4/1995 38 10.5 Hurricane-3 Roxanne 10/9/1995 10/17/1995 57 7.1 Hurricane-3 Bertha 7/7/1996 7/12/1996 57 10.9 Hurricane-1 Cesar 7/26/1996 7/28/1996 13 6.1 Hurricane-1 Dolly 8/19/1996 8/23/1996 15 4.2 Hurricane-4 Edouard 8/26/1996 9/2/1996 67 9.8 Hurricane-3 Fran 8/29/1996 9/6/1996 69 9.4 Hurricane-4 Hortense 9/7/1996 9/9/1996 12 5.5 Tropical Storm Josephine 10/6/1996 10/8/1996 11 7.9 Hurricane-3 Lili 10/16/1996 10/20/1996 27 6.8 Hurricane-1 Marco 11/19/1996 11/24/1996 18 3.5 Tropical Storm Claudette 7/13/1997 7/15/1997 6 3.4 Hurricane-1 Danny 7/17/1997 7/25/1997 38 4.8 Hurricane-3 Erika 9/5/1997 9/9/1997 33 7.7
25 Hurricane-3 Bonnie 8/20/1998 8/28/1998 69 8.5 Hurricane-2 Danielle 8/27/1998 9/1/1998 41 8.1 Hurricane-4 Georges 9/19/1998 9/28/1998 74 8.2 Hurricane-5 Mitch 10/22/1998 11/5/1998 47 3.5 Hurricane-4 Bret 8/19/1999 8/23/1999 35 10.1 Hurricane-2 Dennis 8/24/1999 9/4/1999 76 6.8 Hurricane-4 Floyd 9/9/1999 9/16/1999 66 8.8 Hurricane-4 Gert 9/16/1999 9/21/1999 13 2.5 Hurricane-2 Irene 10/13/1999 10/18/1999 44 9.7 Hurricane-2 Jose 10/18/1999 10/23/1999 26 5.9 Hurricane-4 Lenny 11/14/1999 11/20/1999 41 7.4 Hurricane-1 Debby 8/21/2000 8/23/2000 22 10.3 Hurricane-1 Florence 9/11/2000 9/16/2000 20 4.4 Hurricane-1 Gordon 9/16/2000 9/18/2000 24 11.8 Hurricane-4 Keith 9/29/2000 10/5/2000 33 5.8 Hurricane-2 Michael 10/17/2000 10/18/2000 6 5.6 Hurricane-3 Erin 9/7/2001 9/9/2001 13 6.1 Hurricane-2 Humberto 9/22/2001 9/24/2001 8 5.1 Hurricane-4 Iris 10/5/2001 10/8/2001 28 8.8 Hurricane-4 Michelle 11/1/2001 11/6/2001 39 7.0 Hurricane-2 Gustav 9/9/2002 9/11/2002 18 8.2 Hurricane-3 Isodore 9/18/2002 9/26/2002 61 7.7 Hurricane-4 Lili 9/23/2002 10/3/2002 65 6.5 Hurricane-1 Claudette 7/8/2003 7/15/2003 50 7.5 Hurricane-1 Erika 8/15/2003 8/16/2003 13 11.8 Hurricane-4 Fabian 9/1/2003 9/6/2003 27 5.4 Hurricane-5 Isabel 9/12/2003 9/18/2003 35 5.8 Hurricane-3 Alex 8/1/2004 8/4/2004 20 9.0 Tropical Storm Bonnie 8/9/2004 8/12/2004 18 6.6 Hurricane-4 Charley 8/11/2004 8/14/2004 36 12.1 Hurricane-4 Frances 8/29/2004 9/6/2004 70 8.9 Tropical Storm Gaston 8/28/2004 8/29/2004 4 18.5 Hurricane-5 Ivan 9/6/2004 9/23/2004 92 5.4 Hurricane-3 Jeanne 9/14/2004 9/26/2004 38 3.3 Tropical Storm Bret 6/28/2005 6/28/2005 3 27.5 Hurricane-4 Dennis 7/6/2005 7/10/2005 51 12.2 Hurricane-5 Emily 7/13/2005 7/20/2005 66 9.7 Tropical Storm Franklin 7/22/2005 7/25/2005 15 4.8 Hurricane-2 Irene 8/12/2005 8/15/2005 19 8.5 Hurricane-5 Katrina 8/24/2005 8/29/2005 55 11.4 Tropical Storm Nate 9/8/2005 9/8/2005 2 26.4 Hurricane-1 Ophelia 9/7/2005 9/17/2005 95 10.0 Hurricane-1 Philippe 9/19/2005 9/19/2005 4 19.6 Hurricane-5 Rita 9/19/2005 9/24/2005 68 13.7 Hurricane-1 Stan 10/4/2005 10/4/2005 6 25.5 Hurricane-5 Wilma 10/17/2005 10/24/2005 59 8.3 Hurricane-3 Beta 10/28/2005 10/29/2005 4 3.8
26 Table 2.2 – Listing of bins used in the development of frequency diagrams. For the eye diameter column, the values in parenthesis indicate the range of diameters assigned to each bin. Bin Number Eye Diameter (nmi) Vortex Msgs. per bin 1 2 (2-3) 9 2 4 (4-5) 23 3 6 (6-7) 20 4 8 (8-9) 78 5 10 (10-11) 141 6 12 (12-13) 94 7 15 (14-17) 242 8 20 (18-22) 445 9 25 (23-27) 202 10 30 (28-32) 262 11 35 (33-36) 108 12 40 (37-48) 163 13 50 (49-55) 47 14 60 (56-65) 25 15 70 (66-75) 6 16 80 (76-85) 4 17 90 (>85) 1
27 CHAPTER 3 VORTEX MESSAGE CLIMATOLOGY
A vortex message climatology was needed before any attempts at forecasting could be made. The first part of this climatology examines the distribution of vortex message reports throughout the Atlantic basin. The vortex messages are categorized by eye size, category, month, rapid intensification, eye type, and eyewall replacement. Plots of the number of occurrences of various eyewall parameters are presented in the second section. The third section of this chapter examines how the mean values of MSLP and eyewall diameter vary with time after the formation of an eyewall. Mean value plots show the frequency of concentric and elliptical eyewalls as time progresses from eyewall formation.
a. Atlantic basin distribution
i. All Data Points Figure 3.1 shows the locations of all vortex messages throughout the Atlantic basin between 1989 and 2005. The edge of where reconnaissance flights are flown can be seen between 50°W and 55°W, consistent with KM04. The range of vortex message points is between 11°N and 44°N latitude, and 52°W and 97°W longitude, with the greatest concentrations near the eastern coast of the United States. Comparing Figure 3.1 with the NHC best track (BT) points between 1989 and 2005 (Figure 3.2), it is clear that the BT database is larger. This is expected since the BT tracks all tropical depressions, tropical storms, and hurricanes, whether or not they are sampled by reconnaissance flights, for the entire Atlantic basin (Neumann et al. 1993). The BT dataset also collects information from a variety of sources such as satellites, reconnaissance flights, ship observations, and land observations (Neumann et al. 1993), compared to the vortex message database which only uses reconnaissance flight data.
28 ii. Category Figure 3.3 shows vortex message points by category: tropical storm through category five hurricane. The vortex message does not record flight level winds everywhere, only the maximum flight level winds. Therefore, MSLP was used to divide the vortex message dataset according to intensity category. Tropical storm and category one points are represented by the same color since there is no clear MSLP distinction between them. Tropical storm and category one points are distributed throughout the entire basin, but there appears to be a greater number north of 20°N. Category two points are located predominately off the United States east coast and north of the Bahamas, with fewer south of 20°N. For category three points, most are north of 20°N, but for category four, there is a cutoff near 30°N. This is most likely due to decreasing sea surface temperatures and an increased shear environment (KM04). The same reason can be applied to why category five points are only seen in the Gulf of Mexico and between Cuba and Central America. The BT plotted by category (Figure 3.4) also shows that a majority of the points correspond to either a tropical depression, tropical storm, or category one hurricane. Since the BT dataset designates a category for each point based on wind speed, these data can be divided into tropical depressions and tropical storms. By removing the tropical depressions and tropical storms in Figure 3.5, it is easier to compare the BT dataset with the vortex message dataset by hurricane category. By examining only the hurricane strength points, the two datasets look more comparable. The BT points are at six hour intervals, and, as mentioned earlier, the average time between vortex messages is 3.4 h. This time difference between the two datasets can be seen by comparing Figures 3.3 and 3.5, where the vortex message points typically are much closer together than the BT points. Figure 3.6 compares the number of vortex message points and the BT points by category. Category one is most common for both data sets, but for the BT, 53% are category one, while for the vortex messages only 39% are category one. The results for category two storms are similar, 20% for BT and 18% for the vortex messages. There is a greater difference between the two datasets for category three storms. The vortex message climatology now has a higher percentage (21%) compared to the BT (12%).
29 The category four and five counts are similar for both sets with both showing 14% of values being category four and 3% category five for the vortex messages, and 2% for the BT. When compared to the BT, the vortex message dataset under-represents category one storms and over-represents category three storms. Between 1989 and 2005, 73 tropical cyclones hit the United States, with the most common being tropical storms (32 occurrences) and category one (13 occurrences). Comparing the number of storms that hit the United States with the total number of storms in the Atlantic basin, 40% of category three hurricanes hit the United States (Table 3.1). Considering storm category as it encountered the United States, category three is the most common with 10 instances, where six of these ten occurred in 2004 and 2005. Since a large percentage of category three storms make a U.S. landfall, this may explain why the vortex message dataset has a larger percentage of storms being category three than the BT. This confirms the argument that a threat to land may be one of the main determining factors when deciding which storms to fly into. iii. Month Plots of vortex message points by month from June until December are shown in Figure 3.7. Both June and December have a minimal vortex message count, with June’s messages occurring in the Bay of Campeche and December’s occurring east of Belize. The message points for July are rather evenly spaced across the Gulf of Mexico, Caribbean Sea, and Western Atlantic. August shows greater activity north of 20°N but not much south of there. The most active month, September, has numerous points in the Western Atlantic and more activity in the Gulf of Mexico and south of 20°N, as also seen for the BT (Figure 3.8). The sea surface temperatures still are quite warm in the Gulf of Mexico and off the East coast in July, August, and September, which explains the large amount of activity in the higher latitudes (Elsberry 1995). By October, a major equatorward shift in storm location can be seen, with a clustering in the Bay of Campeche and east of the Yucatan Peninsula. November’s vortex messages also are closer to the equator than previous months, with most being south of 20°N. This equator-ward shift most likely is due to cooling sea surface temperatures in the mid latitudes (Elsberry 1995). Figure 3.8 represents the distribution of BT points divided by month. There are a
30 greater number of points during June, November and December compared to the vortex message plot (Figure 3.7). This suggests that most storms during these months are either tropical depressions, very weak tropical storms that were not flown into, or storms not included in the vortex message database. iv. Eye Size Figure 3.9 divides the vortex messages by eye size, with a different color for every 10 nmi. The messages appear to be evenly distributed across the domain for the smallest eyes (less that 10 nmi), and the next largest eyes (11 nmi to 20 nmi). Small eye sizes are associated with developing or weakening tropical storms such as TS Bret (2005), or very strong storms such as Hurricane Wilma (2005) (KM04). The 11 to 20 nmi bin has the greatest number of vortex message points since the most common eye diameter in the reports is 20 nmi. The number of points between 21 and 30 nmi south of 20°N decreases as well as the number of points in the Gulf of Mexico. The number of vortex message points continues to decrease in the Gulf of Mexico and south of 20°N as the eye diameter increases. Poleward of 20°N there is more land, which may prevent an eye from contracting or remaining stable (Elsberry 1995).
v. Eye Type Figure 3.10 displays where circular, elliptical, and concentric eyewalls occur across the Atlantic basin. A satellite image of each eye type is shown in Figures 3.11-13. Circular eyewalls occur in all areas, perhaps due to circular being the most common eye type. Elliptical is the second most common type, and it appears most often off the east coast of the United States and farther out into the basin. A spatial distribution of storms by month and category in the Atlantic basin was performed by KM04, but not by eye type. Further, as mentioned earlier, KM04 examined a highly heterogeneous dataset that included estimates of eye size and intensity without direct observation. Therefore, the results from the spatial distribution of elliptical and concentric eyewalls are a new finding. The concentric eyewall storms shown in red in Figure 3.10 and Figure 3.14 represent the storms that have at least one instance of a concentric eyewall. The red dots represent storm locations for all instances of concentric eyewalls. They are shown one
31 message before the concentric eyewall begins, and one message after it returns to circular or elliptical. This was done to capture the entire eyewall replacement cycle. A clustering of points appears south/southwest of Cuba, northwest of Cuba, east of Bermuda, and a few east of Puerto Rico. There are few concentric eyewalls in the Gulf of Mexico, and the farthest north location is 34°N. It appears that an eye must exist for a long time period for an eyewall replacement cycle to occur. The average amount of time it takes from eye formation to a concentric eyewall is 23.5 h. Referring back to Chapter two, secondary eyewalls form as a result of a convective ring of heating which intercepts the incoming flux of angular momentum (SW82). Thus, Figure 3.14 suggests that a significant travel time across the ocean is needed for a storm to develop a convective ring, which leads to a secondary eyewall. vi. Rapid Intensifiers/Rapid Weakeners The distribution of rapidly intensifying storms and rapidly weakening storms is widespread across most of the western Atlantic, Caribbean, and Gulf of Mexico. The criterion for rapidly intensifying storms, as defined by KD03, is a 15.4 ms-1 (30 kt) wind speed increase over a 24 h period. Conversely, a 15.4 ms-1 (30 kt) decrease in wind speed over 24 h defines rapidly weakening storms. Whether a storm underwent rapid intensification or weakening was determined from the NHC best track wind data (D. Manning, personal communication, 2007). Figure 3.15 shows the entire storm track of every BT storm that has at least one occurrence of rapid intensification, using a different color for each month. Figure 3.16 shows the same information, but for rapidly weakening storms. Since many rapidly intensifying storms also are rapidly weakening storms, the two figures are quite similar. There is a clustering of rapidly weakening points north of the Bay of Campeche, most likely due to interaction with the Yucatan Peninsula. September is the most active month for rapid intensification and rapid weakening, most likely because there are more storms in September; therefore, RI/RW is more likely (Table 3.2). As the season progresses, the storm tracks gradually move equatorward, especially during October and November, which is also noted in Figure 3.15. Now that a spatial analysis of the vortex message database across the Atlantic basin
32 has been completed, various histograms of the vortex message parameters will be presented.
b. Trend and Number of Occurrences
The first plot of this section (Figure 3.17) examines the trend in the number of reconnaissance flights and the actual number of vortex message reports between 1989 and 2005. Looking first at the trend line for the plot of vortex messages compared to all Atlantic basin storms between 1989 and 2005, there does not appear to be a significant increase in the number of vortex message reports per storm. The trend line for the number of vortex messages compared to storms that were flown into has increased during the 17-year period, but only about one message per year during the 17-year period. This does not necessarily mean that more storms are occurring, but does mean that more storms generally are being flown. The next eleven plots examine the number of occurrences for various vortex message parameters. For temperature inside the eye (Figure 3.18) and temperature outside the eye (Figure 3.19), three distinct maxima can be seen. Each maximum represents the three major flight levels, 1500 ft, 850 hPa, and 700 hPa. As one goes higher in the free troposphere, the temperature usually decreases. Therefore, the first maximum is associated with the 700 hPa readings (highest flight level), the second maximum the 850 hPa readings (middle flight level), and the third maximum the 1500 ft readings (lowest flight level, near surface) (Aguado 2001). The 700 hPa flight level is most common in the vortex message reports, explaining why its temperatures are most common. The 850 hPa flight level is the second most common, and the 1500 ft level is least common. This is the reason for the decrease in the size of the maxima from left to right. A similar trend can be seen in the plot of dewpoint (Figure 3.20) inside the eye. The histogram of eye dewpoint depression (Figure 3.21) has a completely different trend than eye temperature or eye dewpoint. Dewpoint depression is defined as the difference between air temperature and dewpoint temperature (Huschke 1959). For the study presented here, dewpoint depression of the eye is the difference between the temperature of the eye and the dewpoint of the eye. According to SW82, the greater the
33 intensity of a storm, the more subsidence and drying that occur in the eye. Since strong storms are less common than weak storms (Figures 3.3 and 3.4), the extreme dewpoint depressions that are associated with strong subsidence in the eye usually occur only for very intense storms. Weak storms, such as tropical storms and low category hurricanes, have weaker subsidence, and thus smaller dewpoint depressions. The maximum flight level pressure altitude inside and outside the eye are represented in Figures 3.22 and 3.23. Since the plot ranges from low to high altitude from left to right, the maxima are in the opposite order from the maxima in the eye temperature and dewpoint plots (Figures 3.18-20). As mentioned in Chapter one, these data are important in diagnosing the vortex size and strength at flight level. Figure 3.24 represents the occurrences of mean sea level pressure values for all vortex message reports. The lowest value, from hurricane Wilma in 2005 (category five), is 884 hPa, and the highest value is 1014 hPa from Hurricane Andrew (1992) when it was tropical depression strength. The reports gradually increase in frequency as pressure increases until 993 hPa with a maximum of 66 occurrences. Past this point, the values diminish until the largest value of 1014 hPa. Flight level maximum winds (Figure 3.25) range between 27 kt and 170 kt. The most common values are 58 kt and 59 kt (54 occurrences), which correspond to tropical storm strength at flight level. A large jump occurs at 45 kt, followed by a gradual increase to 58 kt. The wind values then are somewhat steady until about 82 kt, which is the transition value between category one and two storms. This distribution of maximum flight level winds, along with the distribution of different categories of storms across the basin (Figure 3.3), shows that category one is the most common category in the Atlantic. The comment section of the vortex message also indicates the storm quadrant where these winds are located. Figure 3.26 shows that the northeast quadrant has the largest occurrences of maximum flight level winds (28% of total), while the total for the northeast, north and eastern quadrants accounts for 58% of total. This is expected since winds in a tropical storm or hurricane are the strongest in the forward right quadrant, which most often is the northeast quadrant (Aguado 2001). Looking at all 2944 vortex message reports (Figure 3.27), 1070 (36.3%) do not have any measurable eyewall, 1512 (51.4%) are circular, 274 (9.3%) are elliptical, and 88
34 (3%) are concentric. Out of the 1874 vortex message points that have a measurable eyewall, 80% are circular, 15% are elliptical, and 5% are concentric (Figure 3.28). The histograms just presented provide information on the number of occurrences of various storm parameters. In order to analyze the relationships between changes in MSLP and eye size, and how these parameters affect, or are affected by eye shape, mean plots were created. These mean plots, presented next, show the mean MSLP and eye size as well as the frequency of elliptical and concentric eyewalls from first eyewall formation until 144 hours after eyewall formation.
c. Mean MSLP, Eye Size, and Frequency of Concentric/Elliptical Eyewalls
i. Entire Atlantic Basin The first panel for the entire Atlantic basin in Figure 3.29 shows how MSLP evolves over time, where the time of initialization is at the first formation of an eye. The second panel shows how the mean eyewall diameter evolves with time, where the inner eyewall is used for concentric eyewalls. As soon as an eyewall forms, the mean MSLP steadily decreases during the first 60 h after formation while the eyewall size remains steady. The width of the standard deviation for the MSLP also grows slightly during the first 60 h while the standard deviation for eye size remains steady. On average, when a storm develops an eye, the storm intensifies, but it does not necessarily have an eyewall contraction. During an eyewall replacement cycle, the inner eye does not become much smaller since the energy source for eyewall generation supplied by the advection of angular momentum toward the eye is cutoff by the outer eyewall (SW82). After the replacement process has ended, the outer eye takes over and the inner eye disappears; therefore, there is an almost instant increase in eye size. According to the vortex message database, elliptical eyewalls typically are the largest, with a mean diameter of 26.28 nmi (Table 3.3). Since a symmetric ring of convective heating is required for the formation of a concentric eyewall and elliptical eyewalls are not symmetric, this could explain why elliptical eyewalls are not associated with eyewall replacement cycles. The third and fourth panels of Figure 3.29 show a cycle of enhanced elliptical and concentric probabilities. This cycle quantifies the most likely time scale of eyewall
35 replacement cycles. After the first 60 h, the MSLP stabilizes and then begins to increase at 96 h, then decreases at 132 h. The eye diameter remains steady until 96 h, then increases as MSLP increases to 120 h. The diameter decreases as MSLP increases slightly to 132 h, then increases as MSLP decreases for the final 12 h. As the MSLP and eye size become steady, the occurrence of concentric eyewalls increases to fifteen percent while elliptical eyewalls decrease. This shows that the intensity of storms tends to level out or weaken during an eyewall replacement cycle (SW82). As the MSLP increases and the eye size increases, the occurrence of elliptical eyewalls also increases and once again, the pressure drops as a concentric eyewall phase begins. The bottom two panels of Figure 3.29 indicate that cycles of enhanced occurrence of concentric and elliptical eyewalls are opposite each other. Future work should determine what causes this cycle and why elliptical eyewalls lead to concentric eyewalls. ii. Atlantic Basin Minus Caribbean and Gulf of Mexico Figure 3.30 examines the mean MSLP and eyewall diameter and frequency of elliptical and concentric eyewalls for the Atlantic basin excluding the Caribbean and Gulf of Mexico. An initial drop in pressure is noticeable during the first 60 h, as was seen in Figure 3.30 for the entire Atlantic basin, but in Figure 3.31 there is a slow, gradual pressure drop until hour 120. The eye size increases slightly during the first 36 h, becomes steady until hour 60, and then increases until just after hour 120 when the eye size decreases. The alternating pattern of concentric and elliptical eyewalls is seen in the bottom two panels. Elliptical eyewalls appear more frequent than concentric eyewalls in this area. As the frequency of concentric eyewalls increases, the frequency of elliptical eyewalls decreases, but the occurrence of ellipticals still is higher than concentric. The eye size and MSLP decrease leading up to the concentric maximum, but then levels out during the maximum. When the elliptical shape takes over just after hour 24, the eye size and MSLP increase, leading up to another active concentric phase. The eye size and MSLP decrease slightly then become steady. This cycle repeats until hour 144. Since elliptical eyewalls are associated with the largest eye diameters and the highest MSLP (Table 3.3), the average MSLP and eye size should increase when the elliptical shape becomes dominant.
36 iii. Caribbean Plots for storms in the Caribbean Sea (Figure 3.31) only extend to 96 h since afterwards the storms either exit the Caribbean region and enter the Gulf of Mexico region (Section iv.), or they make landfall and reconnaissance flights stop until the storms move back over water. The MSLP drops initially until a large frequency of elliptical eyewalls occur at hour 24 when the MSLP and the eye size stabilize. As the frequency of concentric eyewalls reaches a maximum at hour 60, the MSLP reaches a minimum, then levels out until after hour 72. The eye diameter also begins to decrease at hour 48 at the concentric maximum, and then begins to increase as the concentric cycle ends. In this part of the basin, it appears that contraction and intensification lead up to an increase in concentric activity and then stabilize during the maximum in activity. iv. Gulf of Mexico The Gulf of Mexico region provides the most intriguing results of all the areas that were studied (Figure 3.32). The first 24 h show a steady drop in pressure and an increase in eye size prior to an enhanced period of elliptical eyewalls. Exiting the elliptical maximum and entering the concentric maximum at 24 h, the pressure stabilizes, but the mean eye diameter decreases until the concentric maximum. Thereafter, the MSLP continues to decrease, but the standard deviation becomes much wider. There is a steady increase in eye size and a decrease in pressure prior to the second period of enhanced elliptical eyewalls just before hour 48. Right after this maximum, there is a period of enhanced concentric eyewalls, and this maximum is greater than the elliptical maximum right before it. Since MSLP usually decreases prior to an eyewall contraction and increases after a contraction (WCS82), and the concentric frequency is greater than the elliptical, this may explain the drop in pressure and then increase in pressure after the concentric maximum. After this maximum, the MSLP decreases and then stabilizes, while the eye size also remains stable until hour 96. Soon after hour 96, a large elliptical eyewall occurrence appears prior to hour 120. During this time period, there is a large increase in MSLP and a large increase in eye size. Between hour 96 and hour 120, the standard deviation of MSLP and eye size increase, which may mean that enhanced concentric activity leads to a better forecast than enhanced elliptical activity, at least for
37 the Gulf of Mexico. Another enhanced concentric period occurs after the final enhanced elliptical period, where a dip in MSLP and eye size occur in between the decline in elliptical shape and the increase in concentric activity. After the maximum of the final period of enhanced concentric activity, the eye diameter increases, most likely due to the end of eyewall replacement cycles (WCS82).
d. Representativeness of Results
A question that may be raised throughout this chapter is whether the vortex message dataset is a representative sample of the entire Atlantic basin. At this point, it is unknown what the distribution of messages by eye type or whether the eyewall contraction and eyewall occurrence cycles would appear east of 55°W. Based upon the differences seen in the distribution of points and the cycles seen in the mean diagrams for different areas, it can be hypothesized that the rest of the Atlantic might have different results. The biased representation of the major hurricanes in comparison to the lesser ones further suggests that the eyewall climatology shown here is biased toward the more intense storms, or the more intense portion of storms. Indeed, the distribution of storms east of 55°W is less likely to have major hurricane status than those west of 55°W. Therefore, if data from vortex messages east of 55W existed and were added to the database, it may affect the results of the mean plot for the entire Atlantic and perhaps some of the histogram plots. Further, it goes without saying that no statement can be made about how well this climatology represents the climatology of other basins of the world.
e. Climatology Summary
This chapter examined the spatial distribution of vortex messages as well as histograms and mean plots of vortex message parameters. The distribution of vortex messages by category and month had similarities to the NHC BT, but the plot of messages by eye type showed some unexpected results. Histograms were also completed for various vortex message parameters. Since the current study provides the first
38 compilation of all vortex message parameters, it was the first time that a climatology of many storm parameters had been performed. Therefore, the distribution of eye temperature, dewpoint, dewpoint depression, and maximum flight level pressure altitude provide new information on the tropical cyclone core. The mean plots in Section c provided a new way to examine the relationships between MSLP, eye size, and frequency of eye type. These plots revealed a cycle in heightened occurrences between concentric and elliptical eyewalls. The panel of mean eye size for each section of the Atlantic revealed eyewall contraction cycles, which were smoothed out for the analysis of the entire basin. A decrease in the standard deviation surrounding the mean MSLP and mean eye size was seen following enhanced elliptical and concentric activity for the Gulf of Mexico region. This may lead to the development of probability forecasts of eyewall cycles and intensity for the Gulf of Mexico region, and perhaps the entire Atlantic basin. The next chapter will examine in more detail the thermodynamic characteristics and climatology of the eye structure, in preparation for analyzing the prior research on eyewall contraction and replacement and, ultimately, forecast tool generation.
39
Figure 3.1 – Location of all vortex message reports between 1989 and 2005.
Figure 3.2 – Location of all NHC best track data points between 1989-2005.
40
Figure 3.3 – Vortex message points by category, 1989-2005.
Figure 3.4 – NHC best track points by category, 1989-2005.
41
Figure 3.5 – NHC best track points, category 1-5, 1989-2005.
Distribution of Points by Category 89-05
60% Vortex Message 53% Best Track 50%
39% 40%
30%
20% 21% 20% 18% 12% 13% 13% 10% 3% 2% 0% Cat 1Cat 2Cat 3Cat 4Cat 5
Figure 3.6 – Comparison of vortex message database and NHC BT by category, 89-05.
42
Figure 3.7 – Vortex message points by month, 1989-2005.
Figure 3.8 – NHC BT points by month, 1989-2005.
43
Figure 3.9 – Vortex message points by eye diameter, 1989-2005.
Figure 3.10 – Vortex message points by eyewall type, 1989-2005.
44
Figure 3.11 – Satellite image of a circular eyewall (Hurricane Rita, 2005), courtesy of the Space Science and Engineering Center, University of Wisconsin-Madison (http://www.ssec.wisc.edu/~gumley/modis_gallery/).
Figure 3.12 – Satellite image of a concentric eyewall (Typhoon Amber, 1997), courtesy of Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin- Madison, (http://cimss.ssec.wisc.edu/tropic/archive/1997/storms/amber/).
45
Figure 3.13 – Satellite image of an elliptical eyewall (Super Typhoon Ma-On, 2004), courtesy of the MODIS Rapid Response System, (http://rapidfire.sci.gsfc.nasa.gov/ gallery/?2004282-1008/Ma-On.A2004282.0405.2km).
46
Figure 3.14 – Storms with eyewall replacement, 1989-2005.
Figure 3.15 – Rapid intensifiers by month, 1989-2005.
47
Figure 3.16 – Rapid weakeners by month, 1989-2005. Vortex Message Frequency
60
50 Vortex Msg. per obs storm Vortex Msg. per storm
40
30 Storm Count Storm 20 Vortex Message Count
10
0 1989 1994 1999 2004 Year Figure 3.17 – Number of vortex messages compared to both total number of storms and number of storms involved with this study. Black line is the trend line for each plot.
48 Temperature Inside Eye
300
250
200
150 Count 100
50
0 9 10111213141516171819202122232425262728293031 Temperature (oC)
Figure 3.18 – Frequency of occurrence of temperature inside the eye.
Temperature Outside Eye
350
300
250
200
Count 150
100
50
0 3456789101112131415161718192021222324252627 Temperature (oC) Figure 3.19 – Frequency of occurrence of temperature outside the eye.
49 Eye Dewpoint
300
250
200
150 Count 100
50
0 -3-101234567891011121314151617181920212223242526 Temperature (oC) Figure 3.20 – Frequency of occurrence of dewpoint inside the eye.
Dewpoint Depression
500 450 400 350 300 250
Count 200 150 100 50 0 0 2 4 6 8 10 12 14 16 18 20 22 25 31 Depression (oC)
Figure 3.21 – Frequency of occurrence of eye dewpoint depression (temperature inside eye-dewpoint inside eye) for all flight levels.
50 Maximum Flight Level Pressure Altitude Inside of Eye 55 50 45 40 35 30
Count 25 20 15 10 5 0 181 238 273 307 335 465 501 554 814 1461 1483 1503 1523 1543 1563 1589 1644 1750 2436 2970 3012 3033 3053 3073 3093 3113 3144 3651 Meters above Sea Level
Figure 3.22 – Frequency of occurrence of maximum flight level pressure altitude inside the eye.
Maximum Flight Level Pressure Altitude Outside of Eye 55 50 45 40 35 30
Count 25 20 15 10 5 0 123 249 360 388 421 451 491 1026 1474 1500 1526 1552 1581 1674 2745 3012 3038 3064 3090 3116 3168 Meters above Sea Level Figure 3.23 – Frequency of occurrence of maximum flight level pressure altitude outside the eye.
51 MSLP Occurences 1989-2005
80 70 60 50 40 Count 30 20 10 0 884 901 905 910 914 918 922 926 930 934 938 942 946 950 954 958 962 966 970 974 978 982 986 990 994 998 1002 1006 1010 MSLP (hPa)
Figure 3.24 – Frequency of occurrence of mean sea level pressure.
Maximum Flight Level Wind Speed
60 50 40 30 Count 20 10 0 27 39 45 51 57 63 69 75 81 87 93 99 105 111 117 123 129 135 141 147 153 160 170 Wind Speed (kt)
Figure 3.25 – Frequency of occurrence of maximum winds at flight level.
52 Maximum Wind Quadrant
900 821 800 700 600 483 500 419 400 329 316 Count 300 175 192 194 200 100 0 N NE E SE S SW W NW
Quadrant
Figure 3.26 – Frequency of occurrence of location of maximum flight level winds by storm quadrant. Distribution of Eye Type for Vortex Message Dataset 1600 1512 1400 1200 1070 1000 800
Count 600 400 274 200 88 0 No Eyewall Circular Elliptical Concentric
Figure 3.27 – Distribution of eye type.
Percentage of Eye Types
5% 15%
Circular Elliptical Concentric
80%
Figure 3.28 – Percentage of eye type.
53
Figure 3.29 – Mean MSLP, mean eye diameter, frequency of elliptical eyewalls and frequency of concentric eyewalls for the entire Atlantic basin, 1989-2005.
54
Figure 3.30 – Mean MSLP, mean eye diameter, frequency of elliptical eyewalls and frequency of concentric eyewalls for the entire Atlantic basin minus Caribbean and Gulf of Mexico, 1989-2005.
55
Figure 3.31 – Mean MSLP, mean eye diameter, frequency of elliptical eyewalls and frequency of concentric eyewalls for the Caribbean Sea, 1989-2005.
56
Figure 3.32 – Mean MSLP, mean eye diameter, frequency of elliptical eyewalls and frequency of concentric eyewalls for the Gulf of Mexico, 1989-2005.
57 Table 3.1 – Number of Atlantic storms 1989-2005 and number that hit United States. Year TS Cat 1 Cat 2 Cat 3 Cat 4 Cat 5 Total 1989 4 3 2 0 1 1 11 1990 6 5 2 1 0 0 14 1991 4 1 1 1 1 0 8 1992 3 1 2 0 0 1 7 1993 4 2 1 1 0 0 8 1994 4 2 1 0 0 0 7 1995 8 4 2 2 3 0 19 1996 4 3 0 4 2 0 13 1997 5 2 0 1 0 0 8 1998 4 3 4 1 1 1 14 1999 4 0 3 0 5 0 12 2000 7 4 1 1 2 0 15 2001 6 4 1 2 2 0 15 2002 8 1 1 1 1 0 12 2003 9 3 1 1 1 1 16 2004 6 2 1 2 3 1 15 2005 13 7 1 2 1 4 28 Total 99 47 24 20 23 9 222 Total hit US 32 13 4 8 8 8 73 % Hit US 32.32 27.66 16.67 40.00 34.78 88.89 32.88
Table 3.2 – Percentage of all storms, rapid weakeners, and rapid intensifiers by month according to the vortex message database. All Storms Rapid Intensifiers Rapid Weakeners June 0.1 0 0 July 12.8 10.7 14.5 August 28.0 27.8 29.4 September 38.6 41.2 36.9 October 16.7 15.8 15.2 November 3.7 4.6 4.1 December 0.1 0 0 Total 2945 2265 2102
Table 3.3 – Average eye size and MSLP for each eye type. Eye Type Average Eye Size Average MSLP Circular 23.56 nmi 962.1 hPa Elliptical 26.28 nmi 968.9 hPa Concentric 14.7 nmi (Inner Eye) 941.9 hPa
58 CHAPTER 4 EYE CHARACTERISTIC CLIMATOLOGY
This chapter examines the frequency of various parameters in the vortex message report such as eye type, date, latitude, longitude, and time of day. Plots of the frequency of eye temperature, eye dewpoint, eye dewpoint depression, and eye size change at 700 hPa and 850 hPa also are presented in this chapter. These frequency diagrams provide a visual way to examine how various vortex message parameters vary with respect to eye size and intensity. The plots of eye temperature, eye dewpoint, and eye dewpoint depression help explain how these eye parameters affect eyewall contraction (SW82). The axes are chosen based on the relationship between eye size and MSLP according to Sawyer-Eliassen theory (Chapter one, Section c). The goal of this chapter is to illuminate potential relationships among the characteristics that may lead to improved eyewall cycle understanding and predictive skill.
a. All flight levels
The first frequency plot (Figure 4.1) contains all vortex message reports with circular, inner concentric, and elliptical eyewalls. The area of maximum occurrences (50-60 vortex messages) is between 955-985 hPa and is centered on 20 nmi. The lower left corner represents extreme values from the category five storm Wilma 2005 which set the record for the lowest pressure ever recorded in the Atlantic basin (Pasch et al. 2006). Extreme values in the lower right corner represent intense storms with small eye diameters (Emily 1993 category three, Iris 2001 category four, Katrina 2005 category five), moderate to weak storms (Gert 1993 category two, Irene 1999 category two, Florence 2000 category one), and a tropical storm (Bret 2005). If a tropical storm actually forms an eye early in its life, it tends to be small since there is not a strong secondary circulation to advect angular momentum inward and upward at the eyewall, which is the case for tropical storm Bret in 2005 (KM04). For Irene and Gert, the small eyes occur near the end of the storms’ life where Irene is a category two and Gert is a
59 tropical storm. For these two storms, the eyes form later in development (KM04). A high level of organization, or storm symmetry, is needed for a storm to develop an eye (KM04). If a high level of organization is not achieved until the end of the storms’ lifecycle, the eye will not have sufficient time to grow and develop. Florence shows a concentric eye at one point with a three nmi and five nmi eye that occurs when the system was transitioning from a category one hurricane to a tropical storm, which is very uncommon (KM04). The small eyes in the stronger category four and five storms are due to eyewall contraction (KM04). A plot similar to Figure 4.1 is Figure 4.2, a frequency plot that only includes bins of at least three values. Values from hurricane Wilma (2005) in the lower left corner, and the small eye sizes in the lower right corner, as well as some very large eye sizes along the top, do not appear. The area of maximum occurrences still is centered on 20 nmi. The frequency plot of vortex messages with a circular eye wall has a similar shape to that of all vortex message reports since it is the most common eye shape (Figure 4.3). The most common values still are centered on 20 nmi and are between 955 hPa and 985 hPa. As with the earlier plot of all vortex messages, this figure shows all cells regardless of the population. All elliptical vortex message reports, the second most common type of eye behind circular eyewalls, are in Figure 4.4. A similar maximum is between 970-990 hPa but is centered on 25 nmi. This is expected since the average size of elliptical eyewalls is 26.2 nmi, and the average size for circular eyewalls is 23.5 nmi, based on the vortex message database (Figure 3.2). The elliptical vortex messages do not exhibit as large a range as the circular eyes, but the elliptical range is larger than for concentric vortex message reports (Figure 3.10). Both the inner and outer diameters of concentric eyewalls are seen in Figures 4.5 and 4.6, respectively. As expected from Sawyer-Eliassen theory (Chapter two, Section c), the inner concentric eyewalls are associated with low pressures and small diameters. The outer concentric eyewalls have the same pressure range but with larger eye sizes. Once an outer eyewall forms, it begins to cutoff the energy source for the original eyewall; therefore, the outer eyewall grows non-linearly at the expense of the original eyewall (SW82). Even though a hurricane may go through an eyewall contraction before
60 it forms a secondary eyewall, the original eyewall usually does not contract since it has lost its source of angular momentum to the secondary eyewall. There is a fascinating pattern between the inner and outer concentric plots (Figures 4.5 and 4.6). The vortex messages appear to fall into three different areas: 905 hPa-925 hPa, 935 hPa-945 hPa, and 955 hPa-975 hPa. These three areas, which are seemingly a new finding, could be regimes for concentric occurrence. Future studies should analyze this pattern in more depth to determine whether it can be explained by the theories set forth by Eliassen (1951) and SW82, and how it may be related to the eyewall cycles seen in Figures 3.29- 3.32. The distribution of vortex messages by Julian day shows how different eye sizes typically occur throughout the year (Figure 4.7). As the year progresses, the eye sizes appear to increase, while the average MSLP decreases. KM04 found that the largest eyes on average occur during September, the month having the most intense storms (Table 3.2). KM04 explained that more Cape Verde storms form during August and September, and these systems become more intense storms with larger eyes. Annular hurricanes, which are highly symmetric storms with circular eyes, tend to have larger eye sizes than other tropical systems (Knaff et al. 2003). These storms are uncommon, and they are difficult to identify, therefore a complete listing of annular hurricanes between 1989 and 2005 does not yet exist. Future research should identify the annular storms in the vortex message database and examine where and how they fit into the composite mean lifecycles shown here. The plot of latitude frequency shows a gradual increase in eye size and an increase in pressure from the lower left to the upper right corner (Figure 4.8). The most intense storm in the dataset (Wilma 2005) accounts for the lowest latitude values, while the larger eye, less intense storms seemingly are a result of extratropical transition (KM04). The explanation for the expansion of these storms during extratropical transition can be found in Evans (2006). The longitude plot (Figure 4.9) does not show as smooth a transition as the Julian day or latitude plots. This is expected since eye sizes can change from large to small to large again during contraction or replacement cycles as a storm moves across the basin (Figure 3.9). The most intense storms tend to occur
61 further west than the weaker storms. This may be due to the long distance that these storms travel across the warm waters of the Caribbean, or through the Gulf of Mexico. The distribution of the time of day that each vortex message was reported appears in Figure 4.10. The most common time to report vortex messages is between 1000 UTC and 1600 UTC (5AM to 11AM EST). Even though the diurnal cycle of deep convection in the tropical oceans is not very strong, it is noticeable, with the most intense convection in the early morning hours (Hendon 1993). Since early morning is the most active time for convection, reconnaissance flights may be dispatched to closely monitor any changes in the tropics that may affect active storms or the forecast of storms by the NHC. The temperature inside the eye and the temperature outside the eye, as well as eye dewpoint, can be used to verify theories on hurricane intensity change. Since an increase in eye temperature corresponds to increase in intensity according to SW82, this theory can be quantified by information from the vortex message dataset (Figure 4.11). As also noted by SW82, the amount of drying that occurs in the eye due to subsidence can also be an indicator that surface intensity change has happened, or is about to happen. Accordingly, eye dewpoint and eye dewpoint depression will also be analyzed. This analysis will be done for both 700 hPa and 850 hPa values in the following sections, as only those two flight levels are above the boundary layer, and they well-sample the warm-core.
b. 700 hPa Maps
The plots in this section are divided into the two most commonly used flight levels, 700 hPa and 850 hPa. The 1500 ft level is not used since it is a small dataset, without noticeable trends. Plots of temperature, dewpoint, and dewpoint depression are analyzed to characterize the distribution of eye characteristics as a function of MSLP and eye size. Values of 700 hPa temperature inside the eye exhibit a general increase from right to left (Figure 4.12). At first, one may think that this warming is due to the fact that as the intensity of a storm increases the heights fall. Therefore, the 700 hPa isobaric height becomes closer to the surface, so warming must occur since temperature tends to increase
62 from higher altitudes to lower altitudes, all else equal. In order to quantify the theories of Eliassen (1951) and SW82 as explained in Chapter two, the temperature inside the eye can be compared with the temperature outside the eye (Figure 4.13). Since the temperature inside the eye varies a great deal and the temperature outside of the eye does not, we can take the temperature difference of Figures 4.12 and 4.13 to get Figure 4.14 (temperature inside eye-temperature outside eye). It is seen that by taking the temperature difference, there is still a substantial increase in temperatures from upper right to lower left. The temperature inside the eye increases due in part to subsidence in the eye, and this temperature increase causes pressure falls hydrostatically. The pressure falls enhance the secondary circulation, which causes eyewall contraction and leads to more subsidence in the eye. This same reasoning can be applied to the results of the dewpoint and dewpoint depression plots at 700 hPa, and in fact are even more evident. As just mentioned, isobaric heights become closer to the surface as a storm intensifies. Dewpoint, as well as temperature, tends to increase as altitude decreases, all- else-equal. As shown in Figure 4.15, the dewpoint generally increases from right to left, but there is a slightly greater lower right to upper left trend of increasing dewpoints. In order to further confirm and possibly quantify Eliassen (1951)/SW82 theory, the dewpoint depression was calculated, and the results are examined next. Dewpoint depression is a useful way to examine the amount of drying occurring in the eye since larger values represent drier conditions (Figure 4.16). Temperature inside the eye was used in the calculations since dewpoint is measured inside the eye. Figure 4.16 shows a gradual increase in dewpoint depression values as storms become more intense. Therefore, this confirms that as storms intensify, drying in the eye increases due to enhanced subsidence. The next section examines temperature and dewpoint changes at 850 hPa.
c. 850 hPa Maps
The 850 hPa plots represent data collected closer to the surface than data collected at 700 hPa. Warmest temperatures inside the eye tend to be associated with lower pressures and smaller eyes (Figure 4.17). Temperatures decrease even more once the eye
63 becomes very large since the amount of subsidence decreases as the eye increases (SW82). The lowest intensity readings from reconnaissance flights occur at 700 hPa. Therefore, the readings at 850 hPa mostly are storms that either do not develop or storms in the early stages of development. As with the 700 hPa dewpoint plot, the 850 hPa plot exhibits an increase in dewpoint temperature as MSLP decreases (Figure 4.18). Its reason is similar to that for the increase in dewpoint with increasing pressure at 700 hPa. It appears that dewpoint depressions at 850 hPa (Figure 4.19) are less than those at 700 hPa (Figure 4.15). Even though drying in the eye continues as air descends through the entire depth of the eye, dewpoint depressions will be less at 850 hPa than 700 hPa due to other sources of moisture. As air approaches the sea surface, the effects of eyewall mixing can exceed the effects of drying caused by subsidence. The frequency plots presented in this chapter show the most common combinations of MSLP and eye size for all reports, and also for all vortex message reports divided by eye type. An average eye diameter of 20 nmi for all messages agrees with the previous findings of KM04. The regimes that occur in the inner and outer concentric plots were not expected, and further research should include finding a reason for these regimes, whether existing theory can predict these regimes, and whether the regimes can be used to anticipate intensity change. The frequency plots of various eye temperatures and dewpoints were useful in quantifying previous intensity change theories. The results presented thus far illustrate that a great many aspects of storm structure vary during the lifecycle of a hurricane. One thing that is needed is a diagram that simplifies the lifecycle evolution so that the relationship among the variables is more clearly illuminated. A proposed diagram is presented and discussed next.
64
Figure 4.1 – Frequency distribution of all vortex message reports. C = circular, IC = inner concentric, E = elliptical.
Figure 4.2 – Frequency distribution of all vortex message reports (greater than 3 per bin).
65
Figure 4.3 – Frequency distribution of circular eyewalls.
Figure 4.4 – Frequency distribution of elliptical eyewalls.
66
Figure 4.5 – Frequency distribution of concentric eyewalls (inner diameter).
Figure 4.6 – Frequency distribution of concentric eyewalls (outer diameter).
67
Figure 4.7 -- Frequency distribution of Julian day.
Figure 4.8 – Frequency distribution of latitude.
68
Figure 4.9 – Frequency distribution of longitude.
Figure 4.10 – Frequency distribution of time of day.
69 Dewpoint Inside Eye
Temp Inside Eye . Temp Outside Eye . .
MSLP
.
Figure 4.11 – Vortex message parameters used to quantify the hurricane intensity theories of Eliassen (1951) and SW82.
Figure 4.12 – Frequency distribution of 700 hPa temperature inside the eye.
70
Figure 4.13 – Frequency distribution of 700 hPa temperature outside the eye.
Figure 4.14 – Frequency distribution of 700 hPa temperature difference from inside eye and outside eye.
71
Figure 4.15 – Frequency distribution of dewpoint temp. inside the eye at 700 hPa.
Figure 4.16 – Frequency distribution of eye dewpoint depression (temp. inside eye- dewpoint temp. inside eye) at 700 hPa.
72
Figure 4.17 – Frequency distribution of temperature inside the eye at 850 hPa.
Figure 4.18 – Frequency distribution of dewpoint temp. inside the eye at 850 hPa.
73
Figure 4.19 – Frequency distribution of eye dewpoint depression (temp. inside eye- dewpoint temp. inside eye) at 850 hPa.
74 Table 4.1 – Julian day/Calendar day equivalent Julian Day Calendar Day (non-leap years) 200-210 19 July-29 July 211-220 30 July-8 Aug 221-230 9 Aug-18 Aug 231-240 19 Aug-28 Aug 241-250 29 Aug-7 Sept 251-260 8 Sept-17 Sept 261-270 18 Sept-27 Sept 271-280 28 Sept-7 Oct 281-290 8 Oct-17 Oct 291-300 18 Oct-27 Oct 301-310 28 Oct-6 Nov
75 CHAPTER 5 EYEWALL PHASE DIAGRAM
Given the large amount of variability in eye structure, a method is needed to concisely describe the lifecycle of individual storms. The eyewall phase diagram provides a visual way to examine the evolution of a storm’s lifecycle and how this evolution is related to changes in pressure and eye size. This visualization technique was chosen to provide a way to analyze the timing of eyewall replacement cycles and eyewall contraction cycles. The eyewall phase diagram may be utilized in the future to analyze and forecast replacement and contraction cycles in real-time, which may lead to more accurate intensity forecasts. This diagram is useful in analyzing individual storms, but it also can be used for examining composite means. The first part of this chapter will examine plots of vortex messages divided into rapid intensifiers, rapid weakeners, and non-rapid weakeners. The second section examines mean values of all storms in the eyewall phase diagram. Diagrams for the entire Atlantic basin, the Caribbean, the Gulf of Mexico, and the eastern Atlantic are analyzed with the goals of identifying unique lifecycle characteristics for each region. Three case studies of individual storms that go through rapid intensification, rapid weakening and eyewall replacement also will be presented. Hurricane Rita from 2005 was chosen because it provides a good example of an eyewall replacement cycle in the eyewall phase diagram using the inner concentric diameter, outer concentric diameter, and an average of the two diameters. Hurricane Charley of 2004 was chosen because of its dramatic weakening and change in eye diameter, which is well represented in the eyewall phase diagram. The final case study examines Hurricane Wilma of 2005, which exhibited dramatic intensification and became the most intense tropical storm in the Atlantic basin (Pasch et al. 2006). This rapid intensification is the main reason for choosing Wilma as a case study.
76 a. Rapid Intensifiers, Rapid Weakeners, Non-Rapid Weakeners
A plot of all vortex message reports for storms that undergo rapid intensification is seen in Figure 5.1, and storms that go through rapid weakening is seen in Figure 5.2. A separate symbol and color are assigned based on whether a vortex message has a circular, elliptical, or concentric eye. Based on the vortex message dataset, 68% of storms go through rapid intensification, and 60% of storms go through rapid weakening. This is comparable with KD03 who found that 60% of all hurricanes undergo rapid intensification. For rapidly intensifying and weakening storms, all five hurricane categories and tropical storms are represented, as are all three eye types. All storms that do not experience rapid intensification either are tropical storms, category one, category two, and category three hurricanes. In the eyewall phase diagram of non-RI storms, none have a concentric eyewall, all points are above 955 hPa, and most points are between 20 nmi and 40 nmi (Figure 5.3).
b. Mean Plots i. Entire Atlantic Basin Considering the entire Atlantic basin (Figures 5.4 and 5.5), a steady decrease in pressure occurs from the initial time to hour 48, and the eye size during this time changes little. Beginning with hour 48, mean MSLP still decreases, and mean eye size also decreases until around hour 60 when both MSLP and eye size increase. Between hours 72 and 96, there are numerous changes back and forth between increasing and decreasing eye sizes and pressures. Next, there is a gradual decrease in pressure and eye size from hours 96 until 120. From hour 120 until the end, eye size decreases with increasing pressure, then eye size increases with decreasing pressure. The first period of enhanced elliptical probability occurs between hours 24 and 48 (Figure 5.5). During this period, the eyewall size remains steady as the MSLP decreases, as was seen in Figure 3.29. Throughout the entire plot, the periods of enhanced probability for each eyewall type remain partially separated. In other words, there is not a single occurrence of circular, then elliptical, then concentric shape; each enhanced
77 period remains sustained for at least a 24 h period. Around hour 96, there is a lull in elliptical probability, a great deal of circular probability, and an increase in MSLP and eye size. Hour 96 is the end of the Caribbean data, as seen in Figure 3.31, because at this point storms move from the Caribbean to the Gulf of Mexico. This small area between Cuba and the Yucatan Peninsula is where storms tend to interact with land (Figure 3.1); therefore, land interaction may be correlated with an increase in eye size (Figure 3.9). In Figure 3.29 the occurrence of concentric eyewalls remains below 15% until the storms’ end, therefore a heightened occurrence of concentric eyewalls does not appear in Figure 5.5 until after hour 120. Referring back to Figure 3.29, there is an enhanced occurrence of elliptical then concentric eyewalls, which corresponds to an increase, then decrease in eye size and MSLP. Therefore, the elliptical and concentric occurrences seen after hour 108 (Figure 5.5) most likely correspond to the elliptical and concentric events in the Gulf of Mexico. ii. Atlantic Basin Minus Caribbean and Gulf of Mexico The mean plot for the eastern Atlantic with the Caribbean and Gulf of Mexico points removed shows a more complicated pattern (Figures 5.6 and 5.7). As seen in Figure 3.10, this area displays a large occurrence of elliptical eyewalls and a low occurrence of concentric eyewalls. No time periods in the eastern Atlantic have a greater than 15% chance of concentric eyewall occurrence. This could be due to the fact that a heat source is required to form a ring of convection, and as a storm moves higher in latitude, it encounters cooler sea surface temperatures that may interfere with the formation of a convective ring (KM04). Because the higher latitudes are included in Figure 5.7, there is less occurrence of concentric eyewalls. In Figure 5.7, a pattern of circular, then elliptical eyewalls is noticeable for the first 72 h, but the pattern is not as clear later on. As seen in the plot for the entire Atlantic (Figure 5.5), eye size tends to increase until hour 120, then decrease. After hour 120, the MSLP remains steady as eye size decreases. As storms begin to weaken, the eyewall can dissipate and even shrink in size (KM04). A future study should separate closed eyewalls from broken eyewalls and analyze how each type affects the overall vortex message climatology.
78 iii. Caribbean The plot of the Caribbean area (Figure 5.8 and 5.9) is shorter because after 96 h, storms exit the Caribbean region and enter the Gulf of Mexico. There appears to be a pattern of eyewalls expanding and then contracting, which appears to occur as pressure decreases. During the last 24 h, eye size increases as MSLP increases. Referring back to Figure 3.10, there is an increased probability of land interaction in between western Cuba and the Yucatan Peninsula. This interaction with land may be the cause of increasing eye size and MSLP during the last 24 h in the Caribbean. Referring again to Figure 3.10, there is little land interaction over the Caribbean Sea itself. Also, the Caribbean tends to have warmer sea surface temperatures, which are more favorable for eyewall development (KM04). Therefore, the Caribbean is an area that can encourage eyewall contraction cycles. iv. Gulf of Mexico As observed with the plot of mean values of MSLP and eye size in Figure 3.32, the mean eyewall phase diagram plot for the Gulf of Mexico is the most interesting (Figure 5.10 and 5.11). The Gulf of Mexico has the highest occurrence of concentric eyewalls. Figure 5.11 shows that the most numerous occurrences of greater than a 15% chance of concentric eyewall development occur in the Gulf of Mexico. Also, before each enhanced occurrence of concentric eyewalls, there is a heightened occurrence of elliptical eyewalls. This agrees with the cyclical pattern seen in Figure 3.32 between maxima in concentric occurrence and elliptical occurrence. Leading up to each period of enhanced elliptical occurrence, the eye size increases. Then, as the elliptical phase ends and a concentric phase begins, the eyewall contracts. After the end of an enhanced concentric eyewall occurrence, the eye size tends to increase. Referring back to SW82, when a secondary eyewall forms, it begins to cutoff the heat source to the original eyewall. When this secondary eyewall takes over the original eyewall, the storm’s eye size increases, since the inner eyewall of concentric vortex messages are being used. Therefore, there usually is a large jump to a larger eye size at the end of a concentric eyewall cycle. This jump to a larger eye size is evident immediately following the enhanced concentric occurrence after hour 48, and it is delayed for a small time after hour
79 24. There is a longer period of enhanced concentric activity after hour 48, which may explain why the jump to a larger eyewall and the sudden increase in MSLP are much stronger than after hour 24. From hours 96 to 108, there is a steady increase in pressure and eye size until another elliptical period. The mean pressure increases when the enhanced elliptical eyewall period starts, but then decreases after hour 120 as another concentric eyewall period begins. At the end of the last concentric period the data end; so it is not known whether the eye size or MSLP become larger when exiting the concentric phase. Based on the previous two occurrences after hours 24 and 48, the eye size and MSLP most likely increase. These mean lifecycles offer hope for improved forecasting of eyewall cycles once an eye has formed, as will be discussed in Chapter six.
c. Case Study 1: Eyewall Replacement Cycle (Rita, 2005)
Hurricane Rita was a category five storm that formed on 18 September 2005 and lasted until 26 September 2005. This storm’s lowest pressure reached 895 hPa, making it the fourth lowest pressure recorded in the Atlantic basin. Rita hit land as a category three storm, but it did not cause as much damage as Katrina (2005). Nonetheless, there were still problems with flooding, wind, high storm surge, and tornadoes for the east Texas coast and the west Louisiana coast (Knabb 2005). The storm track can be seen in Figure 5.12. Rita was chosen as a case study to examine how pressure, eye temperature, eye size, and dewpoint evolve before, during, and after an eyewall replacement cycle. Various forms of the eyewall phase diagram of Rita will be analyzed using both the inner and outer concentric eye diameters and an average of the two. Figure 5.13 shows the eyewall phase diagram from vortex message reports between 20 and 24 September 2005, which plots the concentric eyewall points using the inner eyewall. By the time reconnaissance flights begin on 19 September, Rita is still tropical storm strength with a MSLP of 998 hPa. By 20 September, the pressure has decreased to 985 hPa, and it continues to decrease along with the eye size until 22 September when it reaches its lowest pressure. Starting on 22 September at around 1400 UTC, Rita begins its first eyewall replacement cycle. This cycle lasts around five hours, and the inner eyewall varies only between 15 and 18 nmi in diameter. The MSLP also is
80 steady, varying between 913 and 915 hPa. The second eyewall replacement cycle begins soon after the first one ends, about an hour and a half later. Similar to the first cycle, the inner eye varies between 14 and 18 nmi, and the MSLP varies between 911 and 917 hPa. Since both cycles are in sync, and the MSLP and eye size do not change much, the concentric points are confined to a small area. The second eyewall phase diagram shows the same vortex messages except that the outer eyewall of the concentric points is used (Figure 5.14). The size of the outer eyewall varies between 32 and 55 nmi; so the points are not as close together as those of the inner eyewall. The third eyewall phase diagram is an average of the inner and outer eyewall diameters (Figure 5.15). These eyewall sizes are between 25 and 36 nmi. After the end of an eyewall replacement cycle, a storm often will weaken as indicated by an increase in pressure (or an end of pressure fall), or an increase in eye diameter (SW82). The diagram that best shows this is Figure 5.13, which uses the inner eyewall diameter. The next few points after the concentric eyewall cycle has ended reveal an increase in pressure as well as an increase in eyewall diameter. To see changes in eye size along with changes in MSLP and eye temperature, various plots were created using vortex message data from Hurricane Rita. These plots use the inner eyewall diameter when concentric points are plotted. The first plot shows how MSLP and eye size change between 20 and 24 September (Figure 5.16). The pressure decreases from 980 hPa down to around 900 hPa, and then increases slightly when the second eyewall begins to form. The eye diameter shrinks as the pressure decreases. As Rita goes through its two eyewall cycles, the pressure and eye diameter are steady as seen in the eyewall phase diagram (Figure 5.13). Once the first eyewall completely disappears, and the replacement cycle has ended at around 0300 UTC, the pressure and eye diameter begin to increase. The time before the concentric eyewall begins marks Rita’s strongest intensity. After the collapse of the original eyewall the storm eventually dies out. Since the distribution of heat changes during an eyewall replacement cycle, the evolution of the temperature inside the eye is plotted with MSLP (Figure 5.17). In addition, the evolution of temperature and dewpoint inside the eye are plotted along with eye size (Figure 5.18). The change in temperature is not as striking as the change in
81 pressure. The temperature increases as the storm intensifies on 21 September (Figures 5.17) The dewpoint of the eye decreases dramatically as Rita becomes more intense (Figure 5.18). The change in eye temperature and dewpoint appears to coincide with the change in eye size (Figure 5.18). This is due to increased subsidence in the eye as Rita intensifies, causing an increase in temperature and a drying of the eye (SW82). As the storm approaches its eyewall replacement cycle, the storm weakens, and the temperature begins to decrease. Once the second eyewall forms, the temperature remains steady until the storm ends.
d. Case Study 2: Rapid Weakening (Charley, 2004)
Hurricane Charley began as a tropical wave off the west coast of Africa in early August 2004, became a tropical storm on the 10 August and lasted until 14 August (Pasch et al. 2005). Charley reached category four strength on 13 August just before it hit the southwestern coast of Florida. Charley encountered a mid-tropospheric trough in the Gulf of Mexico after it hit Cuba, causing it to rapidly intensify and curve towards Florida (Pasch et al. 2005). The NHC best track can be seen in Figure 5.19. Hurricane Charley had episodes of both rapid intensification and rapid weakening as defined by KD03. The sudden rapid weakening is the most fascinating, especially when plotted on the eyewall phase diagram (Figure 5.20). Looking at the NHC best track wind data, Charley had a 30 kt increase in winds between 0600 UTC 12 August and 0600 UTC 13 August. There also was a 35 kt increase between 1800 UTC 12 August and 1800 UTC 13 August (D. Manning, personal communication, 2007). On the eyewall phase diagram, this corresponds to points seven through 16 for the first 24 h increase and points 12 through 25 for the second 24 h increase. In the cases of rapid weakening, the first instance of a 30 kt decrease in winds over 24 h period occurs between 0600 UTC 13 August and 0600 UTC 14 August (D. Manning, personal communication, 2007), corresponding with points 17 through 29 on the eyewall phase diagram. According to the NHC wind data, Charley continued to rapidly weaken until the final report on 1200 UTC 15 August.
82 Between vortex message points 28 and 29 on the eyewall phase diagram (Figure 5.20), Charley’s pressure increases an astonishing 52 hPa in only 11 h as it encounters the Florida peninsula. During this time the eyewall increases from a closed circular five nmi diameter to a slightly opened elliptical 35 nmi diameter. This large change in intensity and eye diameter also can be seen in Figure 5.21. The plot shows a steady pressure drop between 11 and 13 August until the large pressure increase on 14 August when Charley hits the Florida peninsula. A vortex message reported a concentric eyewall at 1413 UTC 11 August, and there is a gradual increase and then leveling of the eye diameter until 0700 UTC 13 August when Charley begins to rapidly intensify. From 0700 UTC 13 August until 2000 UTC 13 August, the pressure drops 31 hPa and the eye diameter decreases 13 nmi in 13 h. Once Charley hits land between 13 and 14 August, the pressure increases to 993 hPa, and the eye diameter increases to 35 nmi. The third plot for Charley involves eye diameter and temperature inside the eye at 700 hPa (Figure 5.22). In this case, the eye size and temperature are steady until a 2°C drop in temperature right before Charley enters rapid intensification. The temperature continues to drop as the eye contracts to four nmi and then there is an increase of 5°C over four hours. When the eye begins to contract again at 1522 UTC 13 August, the temperature rises again. This occurs because as the eye contracts, subsidence in the eye increases (SW82), and the temperature warms dry adiabatically. The increased subsidence accelerates air through the eye, causing the increase in temperature. Once Charley hit land, the storm rapidly weakened, the eye diameter and MSLP increased dramatically, and the storm lost its energy source from the warm water of the Gulf of Mexico. Since the eye diameter increased, the secondary circulation weakened, causing subsidence to decrease and the eye temperature to decrease.
e. Case Study 3: Rapid Intensification/Most Intense Storm (Wilma, 2005)
Hurricane Wilma was the third category five storm of the 2005 season. This storm formed on 15 October as a tropical depression and became a tropical storm on 17 October (Pasch et al. 2006). Wilma gradually gained strength and became a hurricane on 18 October, but it is the explosive intensification on 19 October that makes this storm so
83 fascinating. In less than 24 h Wilma goes from a category one hurricane to a category five hurricane, and became the most intense tropical cyclone in recorded history for the Atlantic basin (Pasch et al. 2006). The increase in wind speed over 24 h is from 60 kt to150 kt, which exceeds by a factor of three the criteria for rapid intensification (30 kt in 24 h) set by KD03. Wilma also achieved the smallest known eye ever seen by the NHC (2 nmi), and the lowest Atlantic pressure ever recorded occurred about an hour and a half later at 882 hPa (Pasch et al. 2006). Afterwards, Wilma formed a double eyewall as it moved toward the Yucatan Peninsula (Pasch et al. 2006). After completing this eyewall replacement cycle, the eye increased as it moved over the Yucatan Peninsula (Pasch et al. 2006). Wilma underwent a second eyewall replacement cycle as it moved off the Yucatan and into the open waters of the Caribbean and past Cuba. Once this cycle ended, Wilma maintained a large eye of 40 to 60 nmi as it passed over southern Florida and back into the Atlantic (Pasch et al. 2006). Figure 5.23 shows the NHC best track positions of Hurricane Wilma. Wilma’s eyewall phase diagram does an excellent job of showing her dramatic deepening and subsequent weakening (Figure 5.24). Points four, five, and six are the first to examine in the diagram. Between four and five the pressure drops 53 hPa, from 954 to 901 hPa in about 5.5 h. During this time, the eye decreases from eight to four nmi. Between points five and six, the pressure drops another nine hPa, and the eye decreases to two nmi. Interestingly, the eye increases to four nmi before it reaches the incredibly low pressure of 882 hPa. Overall between points four and seven, the pressure drops 70 hPa, and the eye decreases from eight to two nmi and then back up to four nmi in about nine hours. This translates to about -7.7 hPahr-1. As Wilma begins an eyewall replacement cycle between points 11 and 12, the pressure increases and continues to increase even after the inner eye shrinks to three nmi. Once the cycle ends, the eye increases to about 35 nmi as to be expected since the outer eye has taken over the inner eye (SW82). The same process occurs when Wilma enters a second eyewall replacement cycle on 23 October at points 33 and 34. At this point, Wilma has just moved into open waters after traveling over the Yucatan Peninsula. This cycle lasts only a couple of hours, and Wilma then returns to about a 60 nmi eye. Wilma undergoes two more small replacement cycles at 1500 UTC 23 October and 0100 UTC
84 24 October. The storm hits the southwestern coast of Florida just before 1200 UTC 24 October. During these replacement cycles between the Yucatan Peninsula and the coast of Florida, Wilma does not gain much strength, mostly remaining between 950 and 963 hPa, i.e., a category two or three storm. Figure 5.25 is a plot of Wilma in the eyewall phase diagram using an average diameter of the inner and outer eyewalls for concentric points. The large differences between the inner and outer eyewalls can be seen when both eyewall phase diagrams are compared. Even in the average of the two, the end of the first major eyewall replacement cycle can be seen at points 14 and 15, although the other replacement cycles at points 33 and 34 and points 38 and 39 are harder to pick out. Figure 5.26 is a plot of Wilma’s pressure and eye size, with arrows indicating the beginning and end of an eyewall replacement cycle, and the points when Wilma encounters land. As Wilma completes the first full eyewall replacement cycle, the eye diameter increases, and the pressure continues an upward trend after reaching record low values. The pressure and eye size increase again when Wilma encounters Cozumel, Mexico in the middle of the plot, corresponding to point 26 on eyewall phase diagram. The fluctuations in eye diameter are obvious as Wilma goes from circular to concentric and back again. The last large jump in pressure and eye size can be seen at the end as Wilma encounters southwest Florida, corresponding to point 51 in the eyewall phase diagram. The case studies presented here are of three hurricanes that represent extreme cases in the Atlantic basin. Even though these storms are quite different, by comparing the eyewall phase diagrams of each storm, some similarities can be seen. For Rita (Figure 5.13), Charley (Figure 5.20), and Wilma (Figure 5.24), an increase in eye size occurs after almost every occurrence of a concentric eyewall. This is to be expected since the inner eyewall dissipates at the end of a concentric eyewall period. An eyewall contraction also can be seen before most instances of concentric eyewalls for all three storms. Some differences can be noted between the three case studies as well. All three eyewall phase diagrams show the most intense period as well as the time of landfall. Even though Rita, Charley, and Wilma show weakening at landfall, Charley’s increase in intensity and eye size at landfall is much greater than for Rita and Wilma. Differences
85 can also be noted for occurrences of concentric eyewalls. Charley only has one concentric vortex message (point 2), Rita has 11 concentric vortex messages that occur in two periods (points 21-24, 27-33), and Wilma has nine concentric vortex messages that occur in five periods (points 9, 12-14, 33-34, 38-39, 43). The ability to identify similarities and differences between various storms, whether they are extreme cases, or average cases, demonstrates that the eyewall phase diagram can be a useful tool. The eyewall phase diagram is a graphical tool that condenses the data presented in the mean diagrams seen earlier into a single plot. This diagram presents the mean analysis of a composite of storms for a certain area in a more conducive way to develop a forecasting tool. This may be developed into a forecasting guide for eyewall formation and storm development, depending on the location of a storm in the Atlantic basin. The eyewall phase diagram can also be used to analyze the evolution of individual storms. This can provide more insight into the timing of eyewall contraction and replacement cycles. Methods that move beyond graphical analysis of lifecycles, and pursue forecasting intensity change using the tools developed thus far, are discussed in the next chapter.
86
Figure 5.1 – All rapid intensifiers represented in the eyewall phase space.
Figure 5.2 – All rapid weakeners represented in the eyewall phase space.
87
Figure 5.3 – All non-rapid intensifiers represented in the eyewall phase space.
88 Figure 5.4 – Mean path of all vortex messages represented in the eyewall phase diagram for entire Atlantic basin, 1989-2005 (full version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development. A time designation for every 24 h from an initial time of zero up to 120 h is noted on the diagram.
89
Figure 5.5 – Mean path of all vortex messages represented in the eyewall phase diagram for entire Atlantic basin, 1989-2005 (zoomed-in version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
90 Figure 5.6 – Mean path of all vortex messages represented in the eyewall phase diagram for Atlantic basin minus Caribbean and Gulf of Mexico, 1989-2005 (full version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
91
Figure 5.7 – Mean path of all vortex messages represented in the eyewall phase diagram for Atlantic basin minus Caribbean and Gulf of Mexico, 1989-2005 (zoomed-in version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
92 Figure 5.8 – Mean path of all vortex messages represented in the eyewall phase diagram for the Caribbean, 1989-2005 (full version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
93
Figure 5.9 – Mean path of all vortex messages represented in the eyewall phase diagram for the Caribbean, 1989-2005 (zoomed-in version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
94 Figure 5.10 – Mean path of all vortex messages represented in the eyewall phase diagram for the Gulf of Mexico, 1989-2005 (full version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
95
Figure 5.11 – Mean path of all vortex messages represented in the eyewall phase diagram for the Gulf of Mexico, 1989-2005 (zoomed-in version). Single circles represent a 70% chance of circular eyewall development, diamonds represent over a 15% chance of elliptical eyewall development, and a double circle represents over a 15% chance of concentric eyewall development.
96 45
Hurricane Rita 18-26 September 2005 40 Hurricane Tropical Storm 26 Tropical Dep. Extratropical Subtr. Storm 35 Subtr. Dep. Low / Wave 00 UTC Pos/Date 25 12 UTC Position PPP Min. press (mb)
30 24
937 mb 895 mb 23 25 22 21 20 19 18
20 -100 -95 -90 -85 -80 -75 -70 -65 Figure 5.12 – Best track positions for Hurricane Rita, 18-26 September 2005, courtesy of the National Hurricane Center (http://www.nhc.noaa.gov/2005atlan.shtml).
97 Figure 5.13 – Eyewall phase diagram for Hurricane Rita (2005) using inner eye diameter for concentric eyewalls.
98 Figure 5.14 – Eyewall phase diagram for Hurricane Rita (2005) using outer eye diameter for concentric eyewalls.
99 Figure 5.15 – Eyewall phase diagram for Hurricane Rita (2005) using average of inner and outer eye diameters for concentric eyewalls.
100 Rita MSLP and Eye Diameter (700 hPa and 850 hPa)
1000 55 MSLP Eye Diameter 50 980 45
960 40
35 940 30 MSLP (hPa) MSLP 920 25 (nmi) Size Eye
20 900 End of Second 15 Start of First Concentric Eyewall Concentric Eyewall 880 10 1517Z 1730Z 2224Z 1517Z 2116Z 0714Z 1449Z 1913Z 2043Z 2328Z 0144Z 0557Z 1143Z 1746Z 2303Z 0453Z 0608Z 0725Z 20 Sept 21 Sept 22 Sept 23 Sept 24 Sept
Figure 5.16 – Comparison of MSLP and eye size for Hurricane Rita of 2005.
Rita MSLP and Eye Temp (Inside) (700 hPa)
1000 35 MSLP Temp 980 30
960 25
940
20 C) o 920 15 Temp ( MSLP (hPa) MSLP 900 End of Start of Second 10 880 First Concentric Concentric Eyewall Eyewall 860 5
840 0 1517Z 1730Z 2224Z 1517Z 2116Z 0714Z 1449Z 1913Z 2043Z 2328Z 0144Z 0557Z 1143Z 1746Z 2303Z 0453Z 0608Z 0725Z 20 Sept 21 Sept 22 Sept 23 Sept 24 Sept
Figure 5.17 – Comparison of MSLP and temperature inside the eye for Hurricane Rita.
101 Rita Eye Temp/Dewpoint (Inside Eye) and Eye Size (700 hPa) 35 60
Start of Temp (C) First 30 Eye Dewpoint Concentric 50 Eyewall End of Eye Size (nmi) 25 Second Concentric 40 Eyewall 20 C) o 15 30 Temp (
10 (nmi) Size Eye 20
5
10 0
-5 0 1517Z 1601Z 2007Z 2224Z 0204Z 1753Z 2116Z 0538Z 0912Z 1449Z 1745Z 1922Z 2043Z 2211Z 0010Z 0144Z 0507Z 0831Z 1143Z 1700Z 1911Z 2303Z 0431Z 0531Z 0608Z 0703Z 0755Z 20 Sept 21 Sept 22 Sept 23 Sept 24 Sept Figure 5.18 – Comparison of the temperature inside the eye, dewpoint temperature inside the eye, and eye size of Hurricane Rita.
102 Figure 5.19 – Best track positions for Hurricane Charley, 9-14 August 2004, courtesy of the National Hurricane Center (http://www.nhc.noaa.gov/2004atlan.shtml).
103 Figure 5.20 – Eyewall phase diagram for Hurricane Charley (2004) using inner eye diameter for concentric eyewalls.
104 Charley MSLP and Eye Diameter
1010 45
1000 MSLP 40 Eye Diameter 990 35 980 30 970 25 960 20 950 MSLP (hPa) MSLP 15 940 (nmi) Diameter Eye Rapid 930 Intensification 10 Begins 920 5 Rapid Weakening Begins/Hits Land 910 0 1223Z 1710Z 0212Z 0554Z 1333Z 1704Z 2059Z 2307Z 0231Z 0656Z 1000Z 1219Z 1522Z 1833Z 1930Z 0700Z 1000Z 11 Aug 12 Aug 13 Aug 14 Aug
Figure 5.21 – Comparison of MSLP and eye size for Hurricane Charley.
Charley Eye Temperature (Inside) and Eye Diameter (700 hPa)
25 45 Eye Temp Eye Diameter 40
20 35 C) o 30 15 25
20 10 15 Eye Diameter (nmi) Diameter Eye Eye Temperature ( Eye
5 10 5
0 0
1154Z 1333Z 1516Z 12 1704Z Aug1757Z 2059Z 2148Z 2307Z 0055Z 0231Z 0540Z 0656Z 0832Z 1000Z 1124Z 131219Z Aug1403Z 1522Z 1701Z 1833Z 1904Z 1930Z 1957Z 140700Z Aug0808Z 1000Z
Figure 5.22 – Comparison of temperature inside the eye and eye size for Hurricane Charley.
105 45
26
40
35
25 30 Hurricane Wilma 15-25 October 2005
Hurricane 25 24 Tropical Storm Tropical Dep. Extratropical 23 Subtr. Storm 22 20 Subtr. Dep. 882 mb Low / Wave 21 17 00 UTC Pos/Date 20 16 12 UTC Position 19 18 PPP Min. press (mb) 15
-85 -80 -75 -70 -65 -60 -55 -50 -45 Figure 5.23 – NHC best track for Hurricane Wilma, 15-25 October 2005, courtesy of National Hurricane Center (http://www.nhc.noaa.gov/2005atlan.shtml).
106 Figure 5.24 – Eyewall phase diagram for Hurricane Wilma (2005) using inner eye diameter for concentric eyewalls.
107 Figure 5.25 – Eyewall phase diagram for Hurricane Wilma (2005) using average of inner eye and outer eye diameters for concentric eyewalls.
108 Wilma MSLP and Eye Diameter 980 90
start of eyewall replacement cycle 80 960 end of eyewall replacement cycle encounters land 70 940 MSLP 60 Eye Diameter 920 50
900 40 MSLP (hPa) MSLP 30 880 (nmi) Diameter Eye 20 860 10
840 0 1954Z 0432Z 1806Z 0513Z 1020Z 2139Z 0644Z 1744Z 2142Z 1920Z 2302Z 0805Z 1458Z 1905Z 0112Z 0554Z 0839Z 1700Z 2217Z 18 Oct 19 Oct 20 Oct 21 Oct 22 Oct 23 Oct 24 Oct
Figure 5.26 – MSLP and eye diameter for Hurricane Wilma.
109 CHAPTER 6 FORECASTING
The results just presented provide a framework for attempting to forecast eyewall size, storm intensity, and eyewall contraction cycles. Although some of these attempts represent preliminary proof-of-concept methods at this point, further research should aggressively attempt to solidify the preliminary findings. These attempts involve comparing current and past eyewall structures to short-term future intensity changes, as well as pattern matching of historical eyewall phase diagram trajectories to current and forecast storms. Time derivatives of various vortex message vortex parameters were calculated and compared to the future change in intensity with time. The period of time is the time between each vortex message, which corresponds to the time between point A (vortex message one) and B (vortex message two) in Figure 6.1. The vortex parameters used are pressure, eye size, eye temperature (inside and out), and eye dewpoint. This was done for all three major flight levels but only 700 hPa is discussed here since it provides the greatest amount of data. This process was done in order to seek a way to forecast future intensity change by just using eye characteristics provided by the vortex message dataset. To find a correlation using the vortex message data, plots were made between the MSLP and the temperature inside the eye. The goal was to show that data from the vortex message database could be used to provide information with statistical significance. The only plot that results in a significant R2-value is at 700 hPa (Figure 6.2), with a value of 0.36, which is much higher than the other flight levels (Table 6.1). This value is considered statistically significant at the 95% confidence level based upon the student’s T-test. This is expected since the higher one goes in the atmosphere, the more that temperature changes influence surface pressure changes (Hirschberg and Fritsch 1993). When the change in intensity is plotted against the change in 700 hPa eye dewpoint, eye temperature, and eye diameter, a great deal of scatter is apparent. Relating this to Figure 6.1, a vortex parameter value at VM#1 (point A) is compared to the
110 intensity change that occurs between VM #2 (point B) and VM #3 (point C). Linear regression is performed on each plot to determine if there is any relationship. No statistically significant R2-values were found for any eye characteristic compared to intensity. Each of the three vortex parameters are then plotted against the future change for that particular parameter (i.e., change in eye size versus future change in eye size). In other words, a vortex parameter value at point A, such as eye dewpoint, in Figure 6.1 is compared with the eye dewpoint change that occurs between VM #2 (point B) and VM #3 (point C). Once again the R2-values are statistically insignificant (Table 6.2). After this was completed, future change in intensity and eye size was plotted as a function of MSLP and eye size. Figure 6.3 shows the future change in intensity at 700 hPa between VM#1 (point A) and VM#2 (point B) compared to the MSLP and eye size from VM#1 (point A). This plot reveals four different regimes separated by areas of intensity stability, which are denoted as gray. The two regimes on the right hand side of the plot (cold colors) represent intensification while the other areas (warm colors) represent weakening. The reason for these different regimes is unknown. It is also unknown why the stable areas separate the regimes the way they do, rather separating the regimes in a straight line across lengthwise or height wise. As the vortex message database grows, and more analysis can be done on core storm data, the answer to these questions may be answered. The change in eye size frequency plot shows large increases in intensity for smaller eyes, and large decreases in intensity for larger eyes (Figure 6.4). As a storm undergoes an eyewall contraction process, its eye becomes small, and the storm can become more intense. As mentioned in chapter two, during an eyewall replacement cycle, an outer eyewall forms and cuts off the inflow of angular momentum to the original eyewall. As the outer wall contracts, it tightens and eventually takes over the original inner eyewall. The point at which the original eyewall disappears marks the end of the decrease in MSLP (WCS82). Very small or no change appear between 20 nmi and 30 nmi which is the most common range of eye diameters (Figure 2.1). Steady state storms tend to have middle to large eye sizes (Knaff et al. 2003), consistent with the lack of change for the middle to large eye sizes in Figure 6.4. Various regimes can be seen in Figure 6.4, where each color block represents a different regime. The blocks denoting
111 large increases in eye size (orange and yellow) may represent the end of eyewall replacement cycles. Conversely, the green and blue blocks associated with large decreases in eye size may correspond to eyewall contractions. An interesting result from this plot is the decrease in the stable range of values (gray area) from less intense to more intense conditions. The reason for this decrease in the magnitude of this “metastability” is unknown and further research is needed to answer these provocative results. Finally, future work should involve pattern matching trajectories and trajectory segments within the eyewall phase diagram. The goal is to produce a forecast for the future trajectory of a given storm. As seen in the example in Figure 6.5, various analogs are plotted for a certain amount of time from a designated point in a storm, or a composite of storms, in the eyewall phase diagram. The spread of the analog sample also is used to provide a measure of the predictability of the tropical cyclone’s structure. The size of the full vortex message database (92 storms over 17 years) limits the confidence in such a product, given there are relatively few cases of the more intense storms. However, as the database grows in size, it is envisioned that the stability and forecast skill of such a product can be quantified and may become useful in real-time. This product is provided on a web page as a further analog to the cyclone phase diagrams at http://moe.met.fsu.edu/cyclonephase.
112 VM #1 VM #2 VM #3 ↓ ↓ ↓ ______A______B______C______Time Æ Figure 6.1 – Graphical representation of time period between vortex messages.
700 hPa 35
30
C) 25 o
20
15 MFLT In (
10 R2 = 0.355 5 860 880 900 920 940 960 980 1000 1020 MSLP (hPa) Figure 6.2 – Correlation between temperature inside the eye and MSLP for 700 hPa vortex message reports.
Figure 6.3 – Future intensity change (change from point A to point B) compared to point A. Negative is future intensification.
113
Figure 6.4 – Future eye size change (change from point A to point B) compared to point A. Negative is future eye contraction.
Figure 6.5 – Example of pattern matching eyewall phase diagram trajectory segments to predict future trajectory in the eyewall phase diagram.
114 Table 6.1 – Correlation between MSLP and temperature inside eye at the three most common flight levels. Flight Level Number of Vortex Correlation between MSLP and Eye Messages Temperature 1500 ft 404 R2 = 0.02 850 hPa 944 R2 = 0.04 700 hPa 1590 R2 = 0.36
Table 6.2 – Correlation between vortex parameter (at point A) and future change of vortex parameter (between points B and C) at 700 hPa. Flight Level Vortex Parameter Correlation between parameter and future change of parameter 700 hPa Eye size R2 = 0.047 700 hPa Eye temperature R2 = 0.067 700 hPa Eye Dewpoint R2 = 0.07
115 CHAPTER 7 CONCLUSION
Forecasting hurricane intensity remains an enormous problem for meteorologists despite the great advances made in track forecasting. While some progress has been made in producing forecast tools for tropical cyclone intensity changes, these tools still lag considerably in performance compared to other forecast products. One explanation for this hurdle may be that these prior forecast tools almost exclusively examine the storm’s larger scale: satellite representations, the environment of the storm, the ocean temperature, and the often poor model gridded analysis of the storm itself. Only in the past few years have details of the core of the storm itself been utilized for producing forecast products of intensity change. The study performed here extends this new development by examining in detail the rapidly growing archive of vortex reconnaissance messages in the Atlantic basin. It has demonstrated proof-of-concept tools (graphical and statistical) for characterizing eyewall cycles and intensity change using this dataset. Vortex messages report critical information about a tropical cyclone's core gathered from a variety of sources: on-board radar, visual inspect of the sea-surface, flight level observations, and numerous dropsondes that reach the surface. Over this 15- year period, 2,953 vortex messages were examined across 92 storms. Vortex messages include information on maximum winds, eye size, MSLP, eye temperature, eye type, and other variables. The number of occurrences of each of these vortex message parameters was calculated as were frequency plots of these parameters as functions of intensity and eye size. The frequency plots, especially for eye temperature, dewpoint, and dewpoint depression, were used to confirm theories on storm intensity and eyewall contraction. Plots of various eye types showed the most frequent combinations of MSLP and eye size, and also reveal fascinating regimes for the existence of concentric eyewalls. A composite mean eyewall cycle diagram was produced using MSLP, eye diameter, and eye type data provided by the vortex message database. These diagrams show the occurrence of concentric and elliptical eyewalls for the entire Atlantic basin,
116 and the Atlantic basin divided into three regions: the Caribbean, the Gulf of Mexico, and the eastern Atlantic. By separating the basin into three areas, differences in contraction cycles and enhanced occurrences of elliptical and concentric eyewalls could be seen. A cycle of enhanced elliptical and concentric eyewall probabilities, as well as correlations between enhanced activity of elliptical and concentric eyewalls and changes in MSLP and eye size, emerged from these plots. It is still left to be discovered what determines when these cycles occur and when they do not. Such questions require increased dataset size and further attention to be answered. The eyewall phase diagram presented here provides a way to observe the relationship between intensity change, eye type, and change in eye size. This diagram shows the evolution of eyewall replacement cycles in a new way, which can be used to aid understanding of how these cycles develop and evolve. The eyewall contraction process also can be observed with the eyewall phase diagram as well as sudden changes in eye size, possibly caused by rapid intensification or rapid weakening. The mean path of the evolution of eyewalls in time as a function of MSLP and eye size provides a new visual perspective on how a subset of Atlantic tropical cyclones behaves. An attempt at forecasting eyewall size and storm intensity was presented in this study. These attempts at forecasting involved comparing current and past eyewall structure to short term intensity change. Unexpected regimes of future intensity change and future eye size changed emerged from Figures 6.3 and 6.4. One of the future steps from this study is to find the reason for these regimes as well as the location of stable areas in these figures. Future work will attempt to pattern match trajectories and trajectory segments to forecast future storm trajectory in the eyewall phase diagram. The size of the vortex message database causes limited confidence of such a forecast product due to the relatively small sample of more intense storms. As the vortex message database grows in size, the forecast skill of such a product may develop into a tool that can be useful in real-time. The development of a climatology of vortex data messages may provide new insight into how a tropical cyclone’s core evolves and how this evolution may be used to forecast intensity. By creating new visual tools that utilize this database, and statistical
117 tools that interrogate the database in real-time, products may be developed in the near future that provides a more accurate and reliable hurricane intensity forecast.
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122 BIOGRAPHICAL SKETCH
David Piech was born on June 1st, 1981 in the beautiful southwestern city of Albuquerque, NM to Gerald and Paula Piech. David has two younger brothers, Andrew, who is currently a car mechanic and Philip who is a sophomore at the University of New Mexico. Growing up, David experienced many interesting weather events, which included severe thunderstorms, hail, major snowstorms, and even an F-0 tornado. After graduating from Eldorado High School in 1999, David began his studies at the University of New Mexico where he graduated in 2004 with a BS in Geography. David began his graduate studies in the Fall of 2005 at Florida State University and after graduation, he hopes to find employment in operational forecasting for either the government or the private sector. Finally, David would like to dedicate this project in the memory of his friend Emily Sandall who made an amazing and beautiful contribution to the world, which will be felt for many years to come.
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