Improving Hurricane Intensity Forecasts in a Mesoscale Model Via Microphysical Parameterization Methods Cerese Marie Albers

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2007 Improving Hurricane Intensity Forecasts in a Mesoscale Model via Microphysical Parameterization Methods Cerese Marie Albers

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THE COLLEGE OF ARTS AND SCIENCES

IMPROVING HURRICANE INTENSITY FORECASTS IN A MESOSCALE MODEL

VIA MICROPHYSICAL PARAMETERIZATION METHODS

BY CERESE MARIE ALBERS

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 Cerese Marie Albers defended on October 26, 2007.

______Tiruvalam N. Krishnamurti Professor Directing Thesis

______Guosheng Liu Committee Member

______Paul Ruscher Committee Member

______Robbie Hood Committee Member

The Office of Graduate Studies has verified and approved the above named committee members.

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This thesis is dedicated to all of the tenacious professionals involved in the intricate and complex workings of meteorological field experiments. Few people work as tirelessly and efficiently as those who dedicate themselves and their best efforts to orchestrate the brave ventures into the eyes of intense storms in an effort to better understand how to protect the lives and property of United States citizens.

ACKNOWLEDGMENTS

The author would like to acknowledge the generous support of this research by the National Science Foundation Grant: NSF ATM-0636157 Data sets were provided by the Hurricane Research Division (HRD), the National Aeronautics and Space Administration (NASA) and by the National Centers for Environmental Prediction (NCEP) Computational Support was provided by the National Center for Atmospheric Research (NCAR) Computational and Information Systems Laboratory (CISL) and by The Florida State University Computational Sciences and Information Technology (CSIT) IBM Supercomputer Facility (Eclipse, Terragold)

The author also wishes to acknowledge Dr. Tiruvalam N. Krishnamurti without whose help I would not have achieved so many academic accomplishments during these last few years. The opportunities he provided were once-in-a-lifetime and vastly influenced the author’s study and understanding of meteorology. Additionally, the author acknowledges Dr. Sandeep Pattnaik for all of his help with the mesoscale modeling process and for graciously and generously imparting his knowledge, without which this thesis would not have been possible. Special acknowledgement also goes to Robbie Hood of the National Aeronautics and Space Administration Marshall Space Flight Center (NASA MSFC) for invaluable guidance and for introducing the author to the magnificent world of field research and forecasting. Many of the studies in this thesis are attributable to those experiences and the data that was obtained under her guidance. Last, but not least, thanks and praise to God, and thank you to my family: My fiancé Stephen Inglish and my parents Lawrence and Silvia Albers, whose love, patience and fortitude know no weaknesses or boundaries.

TABLE OF CONTENTS List of Tables viii List of Figures ix Abstract xiv 1. INTRODUCTION 1 1.1 Introduction to the Problem and Microphysics as Part of the Solution 1 1.2 A Background on Cloud Microphysics 3 1.3 Brief discussion of the CAMEX-4 and TCSP Field Experiments 5 1.4 Synoptic Histories of Hurricanes Erin (2001) and Dennis (2005) 6 1.4.1 Synoptic Overview of Hurricane Erin (2001) 6 1.4.2 Synoptic History of Hurricane Dennis (2005) 7 2. LITERATURE REVIEW AND RESEARCH OBJECTIVES 11 2.1 Literature Review of Prior Studies Necessitating this Study 11 2.2 Research Objectives 13 3. DATA, DESCRIPTION OF THE MODEL, AND INITIALIZATION 14 3.1 Data and Observations Utilized 14 3.2 Description of the WRF Model 16 3.3 Initializing the Model 18 4. EXPERIMENT DESIGN AND IMPLEMENTATION 23 4.1 Alterations to Microphysical Parameterization in the Model and Justification of Methodology 23 4.2 Running, Post-processing and Evaluating 24 4.3 Final Microphysical Parameterization Coefficients Selected for OPTMP Run 26 5. RESULTS AND ANALYSIS OF RESULTS 32 5.1 Presentation of Results 32 5.2 Results 33 5.2.1 Intensity, Track and Skill Score Computations 33 5.2.2 Simulated Hurricane Structure and Composition Comparisons 36 5.2.3 Simulations versus Observations 41

vi 6. SUMMARY AND CONCLUSIONS 68 FUTURE WORK 70 REFERENCES 72 BIOGRAPHICAL SKETCH 75

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LIST OF TABLES

Table 4.1: Percentage of Variable Reduction per Experiment Series...... 28

Table 4.3 RMSE computation example for Experiment Series 2 ...... 30

Table 4.4 Anomaly Correlation computation example for Experiment Series 2 ...... 30

Table 4.6: Coefficients for the OPTMP run parameterizations selected. After careful calculations and evaluations of each experiment were performed, the OPTMP best combination was chosen for each storm...... 31

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LIST OF FIGURES Fig. 1.1 Energy conversion processes related to microphysics in convective storm environments. The heat that is taken from the sea surface mainly by evaporation (A) is released when the vapor condenses into cloud drops (B). Some of this released heat is reclaimed if the drops re-evaporate (C) and return to vapor. The heat remains in the air if the drops precipitate as rain (D). Drops that ascend to the sub-zero parts of the cloud freeze there and release additional latent heat of freezing (E), which along with the freezing of ascending vapor warm the upper levels of the cloud (G). Some of the heat is lost when ice evaporates aloft (I). The rest of the heat remains in the cloud when the ice hydrometeors precipitate and melt while cooling the air below (H). 9

Fig. 1.2 Erin’s eye as seen by MODIS during its mature stages, capturing small mesovorticies present inside the normally clear-air region of the eye. These mesovorticies can be indicative of intense core dynamic processes altering the structure and characteristics of the eye and eyewall 10

Fig. 1.3 Hurricane Erin (2001) on September 10, 2001 viewed with GOES-8 satellite multi-spectral color, courtesy of NASA GSFC. 10

Fig. 3.1 Flight track with times (UTC) of the NASA ER-2 aircraft into Hurricane Erin (2001) during the CAMEX-4 field campaign, overlaid with visible satellite imagery. 21

Fig. 3.2 Flight track with times (UTC) of the NASA ER-2 aircraft into Hurricane Erin (2001) during the CAMEX-4 field campaign, overlaid with enhanced Infrared satellite imagery. 21

Fig. 3.3 Flight track with times (UTC) of the NASA ER-2 aircraft into Hurricane Dennis (2005) during the TCSP mission. 22

Fig. 3.4 The Krishnamurti et al, 2007 technique of Rain Rate Initialization procedure for atmospheric mesoscale models. 22

Fig. 4.2 A, B, & C Hurricane Dennis Experiment Series 2 example of selecting best options for OPTMP runs, for reducing the melting of graupel by 75, 50 and 25 percent, respectively. This pressure versus time plot for Hurricane Dennis (2005) compares real storm observation from the NHC (blue) with the CTRL run (green) and the microphysics example parameterization to select which parameterization has the best intensity improvement of the experiments in that series. Based on how the experiment performs during the maturation and intensification stages of the hurricane it is decided if it is a potential candidate for the OPTMP run. 29

ix Fig. 4.5 The RMSE and ANOMCOR error bar graphs for Experiment Series 2 for minimum central pressure and maximum winds at 10m. This is the main skill score computation method by which the optimal microphysical combination choices were made. 31

Fig. 5.1 Hurricane Erin (2001) Minimum Central Pressure versus Time chart for the WRF model Only (purple), NHC Storm data (blue), the CTRL run (green), and the OPTMP run (red). 45

Fig. 5.2 Hurricane Erin (2001) Maximum Winds at 10m versus Time chart for the WRF model Only (purple), NHC Storm data (blue), the CTRL run (green), and the OPTMP run (red). 45

Fig. 5.3 Hurricane Erin (2001) Minimum Central Pressure versus Time chart for the WRF model Only (purple), NHC Storm data (blue), the CTRL run (green), and the OPTMP run (red). 46

Fig. 5.4 Hurricane Erin (2001) Maximum Winds at 10m versus Time chart for the WRF model Only (purple), NHC Storm data (blue), the CTRL run (green), and the OPTMP run (red). 46

Fig. 5.5 Hurricane Erin (2001) Storm Track for NHC Storm data (blue), the CTRL run (green), and the OPTMP run (red). 47

Fig. 5.6 Hurricane Dennis (2005) Storm Track for NHC (OFCI) Storm data (red), the CTRL run (purple), and the OPTMP (RINIT) run (green). 47

Fig. 5.7 Hurricane Erin (2001) total 48-hr forecast period Root Mean Square Error (RMSE) for Mean Minimum Sea Level Pressure (hPa) for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. 48

Fig. 5.8 Hurricane Erin (2001) total 48-hr forecast period Anomaly Correlation (ANOMCOR) for Mean Minimum Sea Level Pressure (hPa), shown as a correlation value, for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. 48

Fig. 5.9 Hurricane Erin (2001) total 48-hr forecast period Root Mean Square Error (RMSE) for Maximum Winds at 10 m (m/s) for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. 49

Fig. 5.10 Hurricane Erin (2001) total 48-hr forecast period Anomaly Correlation (ANOMCOR) for Maximum Winds at 10 m (m/s), shown as a correlation value, for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. 49

Fig. 5.11 Hurricane Dennis (2005) total 48-hr forecast period Root Mean Square Error

x (RMSE) for Mean Minimum Sea Level Pressure (hPa) for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. 50

Fig. 5.12 Hurricane Dennis (2005) total 48-hr forecast period Anomaly Correlation (ANOMCOR) for Mean Minimum Sea Level Pressure (hPa), shown as a correlation value, for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. 50

Fig. 5.13 Hurricane Dennis (2005) total 48-hr forecast period Root Mean Square Error (RMSE) for Maximum Winds at 10 m (m/s) for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. 51

Fig. 5.14 Hurricane Dennis (2005) total 48-hr forecast period Anomaly Correlation (ANOMCOR) for Maximum Winds at 10 m (m/s), shown as a correlation value, for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. 51

Fig. 5.15 A and B Equitable Threat Scores (ETS) versus Rainfall Threshold (mm) for the Day 2 (second 24 hours in the forecast period for each storm) forecasts of hurricanes Erin (A) and Dennis (B), comparing the CTRL run (green) and the OPTMP run (red) against the NCEP fnl analysis for each storm, respectively. Threat scores range between 0.0 and 1.0. The higher the ETS at a given threshold, the more overlap there was between the forecasted rainfall and the observed rainfall over that 24 hour period. 52

Fig. 5.16 Hurricane Erin (2001) 24-hr averaged vertical cross section of horizontal winds (m/s) for CTRL (A) and OPTMP (B) runs. 52

Fig. 5.17 Hurricane Dennis (2005) 24-hr averaged vertical cross section of horizontal winds (m/s) for CTRL (A) and OPTMP (B) runs. 53

Fig. 5.18 Hurricane Erin (2001) 24-hr averaged vertical cross section of Composite Simulated Reflectivity (dBz) for CTRL (A) and OPTMP (B) runs. 53

Fig. 5.19 Hurricane Dennis (2005) 24-hr averaged vertical cross section of Composite Simulated Reflectivity (dBz) for CTRL (A) and OPTMP (B) runs. 53

Fig. 5.20 Hurricane Erin (2001) 24-hr averaged vertical cross section of Equivalent Potential Temperature Deviation (K) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured. 54

Fig. 5.21 Hurricane Dennis (2005) 24-hr averaged vertical cross section of Equivalent Potential Temperature Deviation (K) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured. 54

Fig. 5.22 Hurricane Erin (2001) 24-hr averaged vertical cross section of Temperature

xi Deviation (K) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured. 55

Fig. 5.23 Hurricane Dennis (2005) 24-hr averaged vertical cross section of Temperature Deviation (K) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured. 55

Fig. 5.24 Hurricane Erin (2001) 24-hr averaged vertical cross section of Vertical Velocity (m/s) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured. 56

Fig. 5.25 Hurricane Dennis (2005) 24-hr averaged vertical cross section of Vertical Velocity (m/s) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured. 56

Fig. 5.26 Hurricane Erin (2001) 24-hr averaged vertical cross section of Mixing Ratios (g/kg) for CTRL (A) and OPTMP (B) runs. Graupel Mixing Ratios are shaded, Snow Mixing Ratios are dotted, and Rain Water Mixing Ratios are solid. 57

Fig. 5.27 Hurricane Dennis (2005) 24-hr averaged vertical cross section of Mixing Ratios (g/kg) for CTRL (A) and OPTMP (B) runs. Graupel Mixing Ratios are shaded, Snow Mixing Ratios are dotted, and Rain Water Mixing Ratios are solid. 57

Fig. 5.28 Hurricane Erin (2001) 24-hr averaged vertical cross section of Mixing Ratios (g/kg) for CTRL (A) and OPTMP (B) runs. Cloud Ice Mixing Ratios are shaded and Cloud Liquid Water Mixing Ratios are contoured. 58

Fig. 5.29 Hurricane Erin (2001) 24-hr averaged vertical cross section of Mixing Ratios (g/kg) for CTRL (A) and OPTMP (B) runs. Cloud Ice Mixing Ratios are shaded and Cloud Liquid Water Mixing Ratios are contoured. 58

Fig. 5.30 A, B, and C Hurricane Erin (2001) HRD HWIND spatial analysis at 1930 UTC Sept 09 (A) compared to simulated spatial wind analyses from the CTRL run (B) and OPTMP run (C) at 1800 UTC Sept 09. This HWIND diagram derives from a human analysis of US AF recon 700 hPa winds adjusted to the surface, ship reports, and a position extrapolation from a wind center fix. 59

Fig. 5.31 A, B, and C Hurricane Erin (2001) HRD HWIND spatial analysis at 1819 UTC Sept 10 (A) compared to simulated spatial wind analyses from the CTRL run (B) and OPTMP run (C) at 1800 UTC Sept 10. This HWIND diagram derives from a human analysis of NOAA P3 surface winds, GOES visible cloud drift winds adjacent to the surface, ship reports, 7 GPS Sonde surface winds computed from MBL and a NOAA wind center fix. 60

Fig. 5.32 A, B, and C Hurricane Dennis (2005) HRD HWIND spatial analysis at 2230

xii UTC July 10 (A) compared to simulated spatial wind analyses from the CTRL run (B) and OPTMP run (C) at 0000 UTC July 10. This HWIND diagram derives from a human analysis of a variety of buoys, GPS Sondes, satellite derived wind fields and imagery, and SHIP guidance. 61

Fig. 5.33 A and B Hurricane Erin (2001) Simulated Spatial Composite Reflectivity (dBz) at 0000 UTC Sept 10 for the CTRL run (A) versus the OPTMP run (B). 62

Fig. 5.34 A, B and C Hurricane Dennis (2005) Simulated Spatial Composite Reflectivity (dBz) at 1600 UTC July 09 for the Key West radar reflectivity (dBz) versus 1800 UTC July 09 for the CTRL run (B) and the OPTMP run (C). The Key West radar imagery was utilized to compare spatial and structural differences between the CTRL and OPTMP runs to reality. 62

Fig. 5.35 A, B, and C Hurricane Dennis (2005) Simulated Spatial Composite Reflectivity (dBz) at 1900 UTC July 10 for the Pensacola radar reflectivity (dBz) versus 1800 UTC July 10 for the CTRL run (B) and the OPTMP run (C). The Pensacola radar imagery was utilized to compare spatial and structural differences between the CTRL and OPTMP runs to reality. 63

Fig. 5.36 A, B, and C Hurricane Erin (2001) Accumulated Rainfall (mm) for Day 2 of the forecast (second 24 hours or from 0000 UTC Sept 10 through 0000 UTC Sept 11) shown by Tropical Rainfall Measuring Mission (TRMM) (A) compared to Simulated Accumulated Rainfall (mm) in the CTRL run (B) and the OPTMP run (C). 64

Fig. 5.37 A, B, and C Hurricane Dennis (2005) Accumulated Rainfall (mm) for Day 2 of the forecast (second 24 hours or from 0000 UTC July 10 through 0000 UTC July 11) shown by Tropical Rainfall Measuring Mission (TRMM) (A) compared to Simulated Accumulated Rainfall (mm) in the CTRL run (B) and the OPTMP run (C). 65

Fig. 5.38 A, B, and C Erin Simulated Vertical Profile of Reflectivity (dBz) at 35° N at 2000 UTC on Sept 10 for the CTRL run (C) and the OPTMP run (B) versus Observed CAMEX-4 ER-2 Doppler Radar (EDOP) Reflectivity (dBz) (A) at the same latitude and longitudes for 1906-1950 UTC. 66

Fig. 5.39 A, B, and C Dennis Simulated Vertical Profile of Reflectivity (dBz) at 24° N at 1200 UTC on July 09 for the CTRL run (C) and the OPTMP run (B) versus Observed TCSP ER-2 Doppler Radar (EDOP) Reflectivity (dBz) (A) at the same latitude and longitudes for 1420-1440 UTC. 67

xiii ABSTRACT

Accurate hurricane intensity prediction is at the forefront of atmospheric science today, and improvements to mesoscale modeling of these storms continue to be major components of refining the accuracy of intensity forecasting. The primary goal of this study is to improve mesoscale modeling of hurricane intensity via the comparison of field campaign observations of Hurricane Erin 2001 from the Fourth Convection And Moisture Experiment (CAMEX-4) and Hurricane Dennis 2005 from the Tropical Cloud Systems and Processes (TCSP) mission with simulated results of improved microphysical parameterization in a mesoscale model that utilizes the Krishnamurti, et al (1991) technique of rain rate initialization (RRI). Comparison of the simulated results with field observations collocated with satellite observations provides a way to validate many different aspects of the simulated hurricane’s structure and intensity. The mesoscale model used in this research is the Weather Research & Forecasting (WRF) model version 2.1 (ARW). Much of the existing microphysical parameterization of this model is built from results of mid-latitude observations. Substantial improvement to the model’s intensity forecasting in the tropics can be made via proper parameterization of the model microphysics for hurricanes. With a foundation of results from other hurricane mesoscale modeling initial/boundary conditions, dynamics and physics studies, basic options for modeling hurricanes Erin (2001) and Dennis (2005) are chosen and held constant during a series of microphysical sensitivity experiments for each storm. These are specifically designed to isolate the individual effects of altering one microphysical parameter at a time on the hurricane’s intensity forecast and are carried out in a doubly or triply nested way. The initial and boundary conditions used in the innermost grid with finer resolution are obtained from the respective outermost grids where rain rate initialization is invoked. All of the results are illustrated for the highest- resolution innermost domain, which is integrated using an explicit microphysics scheme. Each of these experiments are integrated for a forty-eight hour forecast period, adequately capturing the mature and intensification stages of the two hurricanes. Skill scores are obtained from the results of the two sets of experiments. Root Mean Square Errors (RMSE) and Anomaly Correlations (AC) are computed by

xiv comparing the model output of each experiment to NCEP’s final analysis (fnl) available at one-degree horizontal resolution and six-hour temporal resolution interpolated to the respective model grid. Taking into account the way that each experiment performs in terms of simulated storm intensity as well as optimized RMSE and AC, the optimal combination of microphysical processes (i.e. melting, evaporation, fall speed of hydrometeors) for each storm is determined. Then a final forty-eight hour forecast of each hurricane is made utilizing this optimal microphysical parameterization combination. The results from each final run are compared to observations, skill scores are computed, and the final intensity improvements for both hurricanes Erin and Dennis are shown. The results of this study strengthen the evidence that RRI and proper microphysical parameterization in mesoscale hurricane modeling are both useful and effective techniques, and combine to improve hurricane intensity forecasting in a mesoscale model.

CHAPTER ONE: INTRODUCTION

1.1 Introduction to the Problem and Microphysics as Part of the Solution

Accurate hurricane prediction is at the forefront of atmospheric science today. Devastating Atlantic hurricane seasons such as those occurring in 2004 and 2005 have left many people asking how it is possible to better predict the rapidly changing track and intensity forecasts of these powerful storms. While errors in forecasting the track of a hurricane have decreased by roughly 50 percent since1990, there is still much to be learned about improving hurricane intensity forecasts. This improvement becomes vital when one considers the quick and unforeseen increase in intensity that some storms experience just before landfall, like hurricane Charley in 2004 making landfall near Port Charlotte, FL with devastating results. According to The American Meteorological Society (AMS) “For the 5-yr period 2001–2005, NHC intensity forecast errors averaged 10 knots (5 m s-1) for the 24-hour forecast and 14 knots (7 m s-1) for the 48-hour forecast. In contrast to the improvements …for track, mean intensity errors have not changed significantly during the past 30 years. Furthermore, these average statistics obscure the large errors that typically occur when storms strengthen (or weaken) rapidly. Unexpected rapid increases in strength close to landfall can result in communities being under prepared. …The inability to anticipate these changes is of great concern,” AMS (2007). Also consider the impact that hurricane Dennis had in 2005 when it steamed toward the Gulf coasts of Florida and Alabama, and severe weather warnings were output for a storm that suddenly decreased in intensity just before landfall with mitigated impact to the Gulf coastal communities. In order to improve the confidence that the community can have in hurricane forecasts, it is necessary to better understand how to improve the intensity forecasts the hurricane models produce. The understanding of how a hurricane’s intensity is determined is the key to comprehending how to better forecast a tropical cyclone. Numerous different internal and

1 external thrusts seem to dictate the evolution of the intensity of the hurricane, as shown in Krishnamurti et al (2005). One external thrust considered here is the angular momentum that the inflowing parcels of air carry into the interior sections of the hurricane. The outer angular momentum is generally depleted along with inflowing trajectories of parcel motion, occurring from a number of torques such as surface and internal friction and those from pressure asymmetries in the storm, as noted by Pattnaik and Krishnamurti (2005). The internal thrusts of the storm will dictate the inward advection of angular momentum and can be due to internal flare-ups of organized convection within the heavy precipitation areas that provide a teleconnection between the external angular momentum and the resultant intensity of the hurricane. One of the key components of the hurricane’s internal thrust that is considered here is the contribution made by cloud microphysics to the system. Inner core convective processes are a significant part of the inner thrust of a tropical cyclone that determine the intensity and development of the whole system. The inner thrust communicates with the outer thrust dynamically and thermodynamically, and is a necessary component of the processes that govern a tropical cyclone’s strength and magnitude of variability in intensity. Observations taken from above the convective core during intense stages can provide key insight into contributing factors of intensity jumps such as convective bursts and eyewall replacement cycles. Various microphysical processes (discussed in greater detail in section 1.2) are a significant part of these inner core convective processes, and observations of cloud condensation nuclei, winds, radar reflectivity and brightness temperatures are necessary to determine the microphysical make-up of the core and rain rates. Many in situ and remote sensing observations can be combined at high-resolutions to provide a virtual map of the different hydrometeors present in and around the core of the storm. These various types of hydrometeors such as graupel, cloud ice, cloud liquid water, rain, snow, and hail provide detailed information about the convective nature of the storm, and are important for the cyclone’s intensity. This study addresses the ways in which microphysical parameterization methods can be validated with in situ and remote sensing data obtained from field studies to improve the forecast accuracy of hurricane intensity predictions. At the root of this improvement is the concept of rain rate initialization utilizing actual data. As defined in

2 Krishnamurti, et al. (2001), rain rate initialization over the tropics entails the assimilation of observations in a numerical weather prediction model. During this process the vertical profiles of several variables such as humidity, vertical velocity, horizontal divergence, convective heating and surface pressure are adjusted such that the model’s initialized rainfall will closely match the “observed” rain rates for a particular degree of resolution. This study recognizes the important roles that in situ and remote sensing observations of liquid and ice phase hydrometeors can have on rain rate initialization in numerical weather prediction models for forecasting convective precipitation. If the initial precipitation improves, then as the model runs forward in time the forecasted precipitation will improve. This has a positive impact on the processes in the model that interact with the precipitation, such as are contained within the model cumulus and microphysics schemes, so that they will more accurately interact to produce a better intensity forecast of the simulated hurricane. These final results, at selected time intervals, can be compared to observations obtained in field experimentation to show the value of rain rate initialization and microphysical parameterization in simulating hurricanes. It is important to note that while the issue of proper microphysical parameterization in mesoscale models remains complex, this study is an attempt to elucidate the optimal way in which one state-of-the-art mesoscale model can be parameterized using its existing model schemes to best improve the intensity of the simulated hurricane and compare its simulated results against observations to validate an accurate simulation of the storm environment. The main focus of this study is how improved microphysical parameterization in mesoscale models can positively affect intensity forecasting out to 48 hours while accurately simulating the real storm environment. Recognizing the many facets involved in intensity prediction, what is shown here is a procedure, and by no means a final solution.

1.2 A Background on Cloud Microphysics

The hurricane intensity prediction issue is highly complex and necessitates a deep understanding of the manner in which all of the intensity factors interact to produce a

3 tropical cyclone. An excellent representation of these factors and their interactions is required of a mesoscale model which predicts at the cloud-resolving level. The primary mechanisms, structures and processes which most profoundly affect a hurricane’s intensity occur at several scales. For synoptic scales, the amount of vertical wind shear in the storm environment is crucial to the limit on intensification. At the mesocale level the transport of angular momentum, cloud torques, and convective rainband structure play very important roles. At the microscales, vertical hot towers in the core, eyewall replacement cycles, and microphysical interactions are very important inner thrusts that can directly impact a hurricane’s intensity. As noted previously, one of the key components of a hurricane’s internal thrust is the contribution made by cloud microphysics to the system. It has been shown that the inner thrust communicates with the outer thrust dynamically and thermodynamically, and is a necessary component of the processes that govern a tropical cyclone’s strength and magnitude of variability in intensity. Various microphysical processes of the inner core are responsible for a significant part of the intensity issue. These processes at the cloud resolving scale include condensation/evaporation, freezing/melting, sublimation/deposition and production terms such as riming, accretions, autoconversion, collision and coalescence. These processes interact to alter the various physical phases in which hydrometeors can be found in the overall storm distribution. In a very broad sense, a hurricane may be thought of as a large, organized conglomeration of convective storms that rise from the lower levels to the far upper reaches of the troposphere, and sometimes a little beyond. Of these storms, the collection of convective thunderstorms close to the center of a well-organized tropical cyclone forms the eyewall region, where many of the interactions affecting storm intensity play out. In each of these systems, hydrometeors are constantly changing sizes, phases, and location. A schematic example that illustrates how these processes can fuel storm intensification is given by Rosenfeld et al, 2007 in Fig. 1.1. The process of heating the core of a hurricane is discussed and shows that latent heating of this area is an important factor in hurricane intensification. Latent heat release in microphysical processes is so important for a hurricane’s intensity because it provides extra heat to be used by the system. Pattnaik and

4 Krishnamurti, 2007 found that graupel conversion processes have a large effect on latent heat and storm dynamics. Moisture and heat fluxes from the surface are important for storm genesis and maintenance, but latent heat release aloft provides a substantive energy source for intensification by aiding in the organization of moist convection and increasing the melting of frozen hydrometeors. Suppressed melting processes allow the heating that was used to melt ice hydrometeors to go into heating a column of air in the core of the storm which can lead to intensification. An increase in the production of graupel helps retain large scale condensational heating within the storm, thus contributing to an increase in the magnitude of warming of the inner core and can impact the storm’s intensity.

1.3 Brief discussion of the CAMEX-4 and TCSP Field Experiments

The first of the hurricanes addressed in this study is hurricane Erin (2001) that was observed on September 10, 2001 as part of the National Aeronautics and Space Administration (NASA) Fourth Convection and Moisture Experiment (CAMEX-4). The objectives of the NASA CAMEX-4 mission were to study tropical cyclone development, tracking, intensification, and landfall impacts using aircraft and surface remote sensing instrumentation. The primary aircraft used during CAMEX-4 were the NASA DC-8 and ER-2 research airborne platforms, and the P3 aircraft from the Hurricane Research Division (HRD) of the Atlantic Oceanographic and Meteorological Laboratory (AOML), part of the National Oceanographic and Atmospheric Administration (NOAA). These instrumented aircraft sampled the storm environments of selected hurricanes, even investigating upper altitude regions of Hurricane Erin that would not normally be sampled. Erin would not normally have been sampled by US reconnaissance aircraft due to its distance out at sea and the fact that its projected track did not take it toward landfall in the United States. However, because the special instrumentation aboard the ER-2 requires a completely different flight level to collect that data than a normal aircraft can handle and because of the CAMEX-4 mission, Erin was sampled during its mature stage. Several instruments used aboard these aircraft observed hurricane microphysics. It was one of the most comprehensive collections of microphysical data in recent years.

5 The second storm of this study, hurricane Dennis of 2005, was sampled during the NASA Tropical Cloud Systems and Processes (TCSP) mission. During this July field experiment, the NASA ER-2 flew three flights into hurricane Dennis during its transit through the Caribbean Sea into the Gulf of Mexico. Some of the data obtained during its last mission over Dennis is utilized in this study. The objectives of the TCSP field campaign were focused on the study of the dynamics and thermodynamics of precipitating cloud systems and tropical cyclones using NASA-funded aircraft and surface remote sensing instrumentation used to answer key questions pertaining to the origins and lifecycle of weather disturbances in the tropics, (Halverson et al, 2007). The NOAA P3 aircraft that flew many missions during the field experiment also collected a great deal of data from this storm. The combined data sets for hurricane Dennis from these aircraft provide a great deal of insight into the microphysical make-up of the storm.

1.4 Synoptic Histories of Hurricanes Erin (2001) and Dennis (2005)

This section constitutes a short overview of the synoptic storm histories of the two tropical cyclones utilized in this research. It is important that this background is recognized, as it helps determine the maturation and intensification stages of the hurricanes for the purposes of this study.

1.4.1 Synoptic Overview of Hurricane Erin (2001)

The first of the analyzed hurricanes, hurricane Erin of 2001, originated as an African Easterly wave that exited West Africa around August 30, 2001. Cloud patterns showed better organized convection by September 1 first and by 1800 UTC that day it became a tropical depression close to 600 nautical miles (nm) southwest of the Cape Verde Islands while it headed on a west-northwest track for a few days. Tropical storm Erin formed on September 2 around 0600 UTC and some moderate weakening occurred on September 3 before Erin regained strength on September 4. By September 5, the storm again encountered unfavorable conditions in the form of southwesterly shear and it degenerated a bit before a new low-level circulation emerged again on September 6. A

6 tropical depression again, it began to re-strengthen over the next few days and gradually it became hurricane Erin on September 8. While Erin was still strengthening it moved on a northwest heading and passed near Bermuda where hurricane watches and warnings were posted (see Fig. 1.2 for imagery of Erin’s eye as seen with MODIS imagery during its intensification stages). The storm was still strengthening by September 9 when it reached its peak intensity near 1800 UTC that day at wind speeds close to 105 knots (kts). Erin continued north-northwest and was sampled by the NASA CAMEX-4 ER-2 aircraft on September 10 as Erin began to gradually weaken. On Sept. 10, GOES 8 Multi-spectral imagery showed it still had excellent eye features and a tight, closed center of circulation, as shown in Fig. 1.3. It then proceeded to make a rightward turn as a result of short-wave trough interactions with the weakening Atlantic subtropical ridge on September 11, 2001. As Erin transited northeast over the next few days it weakened below hurricane strength before making its extratropical transition heading toward Greenland on September 16. Erin’s final day occurred when it merged with a high-latitude cyclonic flow over Greenland the following day. Some of this history has been adapted from Pasch and Brown (2002).

1.4.2 Synoptic History of Hurricane Dennis (2005)

Hurricane Dennis is the second storm analyzed in this study. Some of the synoptic history is adapted from Beven (2007). It began as Tropical Depression 4 (TD-04) moving on a west-northwestward heading across the southeastern Caribbean Sea, a climatological graveyard for tropical cyclones that time of year. Nonetheless, the storm structure continued to improve, and satellite signatures observed banding features becoming better defined as time progressed. These improved features formed the basis for upgrading the depression to Tropical Storm Dennis at 1500 UTC on July 5 about 300 nm south of San Juan, Puerto Rico. The TC translated across the central Caribbean, guided by strong subtropical high pressure to its north. Aided by low environmental shear and warm sea surface temperatures in the Gulf of Mexico, Dennis continued to strengthen and was upgraded to

7 the first hurricane in the North Atlantic hurricane season in a special advisory package issued at 2200 UTC on July 06. A reconnaissance plane associated with the TCSP mission had found 79-kt winds at 700-mb with a central pressure of 985 hPa. Dennis continued to intensify on its northwestward track, and became a Category 2 storm on the Saffir/Simpson Scale at 1200 UTC on July 7. Six hours later Dennis passed Jamaica. Dennis became the first July major hurricane (Category 3 or higher) in the Atlantic since Hurricane Bertha in 1996, and was upgraded to a Category 4 storm that evening. Dennis headed northwest toward Cuba and near 1500 UTC on July 8, Dennis reached its peak intensity with 130 kt winds and a minimum central pressure of 937 hPa. Weakening slightly, Hurricane Dennis made an 1800 UTC Cuban landfall, and experienced further weakening due to its interaction with land. After its passage over Cuba, Dennis began to quickly reorganize over the very warm waters of the Gulf of Mexico and continued to steadily strengthen throughout much of July 9. Dennis underwent an explosive intensification period when the minimum central pressure dropped 11 hPa in an hour and a half. Dennis was re-upgraded to Category 3 status in a special advisory issued at 2300 UTC. The center was then located about 245 nm south of Panama City, Florida, and moving northwestward at about 12 kts. Continued intensification led to an upgrade to Category 4 status once more at 0500 UTC on July 10 with a minimum central pressure of 937 hPa. At 1900 UTC on July 10, Dennis was downgraded to a Category 3 hurricane with 105-kt winds, possibly due to its stint over the cooler sea surface temperatures (SST) near the Florida Panhandle region. Hurricane Dennis’ center made landfall in northwestern Florida east of Pensacola at about 2000 UTC with estimated winds of 105 kts and a minimum central pressure of 943 hPa. Dennis continued moving north-northwestward inland and weakened gradually as it tracked across northeast and until it eventually lost tropical characteristics in the lower Ohio River Valley, Beven (2007).

Adapted from Rosenfeld, et al 2007

Fig. 1.1 Energy conversion processes related to microphysics in convective storm environments. The heat that is taken from the sea surface mainly by evaporation (A) is released when the vapor condenses into cloud drops (B). Some of this released heat is reclaimed if the drops re-evaporate (C) and return to vapor. The heat remains in the air if the drops precipitate as rain (D). Drops that ascend to the sub-zero parts of the cloud freeze there and release additional latent heat of freezing (E), which along with the freezing of ascending vapor warm the upper levels of the cloud (G). Some of the heat is lost when ice evaporates aloft (I). The rest of the heat remains in the cloud when the ice hydrometeors precipitate and melt while cooling the air below (H).

Fig. 1.3 Hurricane Erin (2001) on September 10, 2001 viewed with GOES-8 satellite multi-spectral color, courtesy of NASA GSFC.

10 CHAPTER TWO: LITERATURE REVIEW AND RESEARCH OBJECTIVES

2.1 Literature Review of Prior Studies Necessitating this Study

A background of prior research in the areas of microphysics, parameterization of schemes, and the role they play in mesoscale modeling of hurricanes is necessary before attempting a methodology to parameterize the microphysics in the WRF model. Here, research covering the aforementioned areas is shown and its relevancy is discussed. Willoughby (1995) noted that although there are many factors that determine a tropical cyclone’s intensity, it is ultimately dependent on the magnitude and distribution of latent heat release in the storm core. Latent heat and ice phase microphysics are inextricably linked, and so an extensive explanation of this was included in the introduction section. Lord et al (1984) emphasized that inclusion of ice processes in an axisymmetric, non-hydrostatic hurricane model results in robust impacts on the structure and evolution of a simulated hurricane vortex. Processes such as melting and evaporation play a crucial role in the mesoscale organization of moist convection. Four years later, Lord and Lord (1988) placed significance on graupel conversion processes to latent heat release and storm dynamics. McFarquhar and Black (2004) showed that the mass-weighted prescribed coefficients should be allowed to vary with density and the mean hydrometeor size and distribution. They noted that field observations made in tropical cyclones show that ice hydrometeor composition, number and size can vary in different regions of the storm. For the purposes of this study, this prior research obligates the use of a microphysics scheme that well represents ice hydrometeors as individual species and allows them to be parameterized to best reflect variances with density. In their study, McFarquhar and Black also showed that existing microphysical parameterizations are not appropriate for representing the fallout of snow and graupel in tropical cyclones. Thus, the fall speeds of those hydrometeors were altered in this study. Additionally, Lin et al

11 (1983) showed the dramatic improvements made in a cloud-resolving model when six forms of water substances were included (water vapor, cloud water, cloud ice, rain, snow, and graupel). They showed that the addition of a snow field significantly modified the microphysical processes and gave the model a greater degree of realism. This was the original impetus behind the creation of the WSM 6-class graupel scheme that is utilized in the WRF model in this study. After Lin et al there were several revisions to their idea that culminated with the Hong et al (2003) suggestions that led to the creation of the actual scheme. Hong et al found that modifications in the ice microphysical processes result in realistic distributions of clouds by reducing the ice crystals and by increasing the snow at colder temperatures. Based on the aforementioned studies, the WSM 6-class graupel scheme was deemed appropriate for this research. Braun and Tao (2000) showed a hurricane’s horizontal distribution of precipitation is equally as sensitive to Planetary Boundary Layer (PBL) parameterization as well as the cloud microphysical parameterization. In this study, the impacts of altering model microphysics parameterization schemes on intensity is discussed, but this research has given a suggestion for future work as well investigating the PBL. Two years later, Braun (2002) highlighted the importance of hydrometeor water-loading and noted that it significantly contributes to deceleration of air parcels rising in the eyewall. When graupel falls out, water-loading is reduced and weak upward accelerations result. Gilmore et al (2004) showed that variations to parameters describing graupel result in changes to the amount of accumulated precipitation. This tells us that the parameterization of graupel will play an important role in the resulting rainfall production by the model. Pattnaik and Krishnamurti, 2007 observed that the weakest storms were produced when the fall speeds of frozen hydrometeors were doubled. It stands to reason that experiments to reduce the fall speeds of frozen hydrometeors would allow a storm to intensify. The reduction of melting processes should lead to an enhanced and faster production of frozen hydrometeors. They also recognized that while microphysical parameterization may not be the only factor responsible for the overestimation of rainfall in their simulations, more critical evaluations of those schemes is needed. This was, at its most basic level, the impetus for this research.

12 Rogers et al (2006) notes that a method for evaluating tropical cyclone simulations by comparing them to an extensive set of microphysics observations, including hydrometeor concentrations, radar reflectivity, and vertical motion that span a variety of storms is needed. This was one of the main reasons that remote sensing data from TCSP and CAMEX-4 was used in this research.

2.2 Research Objectives

It is important to note that while the issue of proper microphysical parameterization in mesoscale models remains complex, this study is an attempt to elucidate the optimal way in which one state-of-the-art mesoscale model can be parameterized using its existing model schemes to best improve the intensity of the simulated hurricane and compare its simulated results against observations to validate an accurate simulation of the storm environment. The main focus of this study is how improved microphysical parameterization in mesoscale models can positively affect intensity forecasting out to 48 hours while more accurately simulating the real storm environment. Recognizing the many facets involved in intensity prediction, what is shown here is a procedure, and by no means a final solution. One primary objective is to have an improved intensity prediction from a more “realistic-looking storm” than the WRF can produce on its own. A “realistic-looking” storm for the purposes of this research should ideally meet a particular set of criteria at the appropriate times. These include proper thermodynamic structure, close-matching intensity prediction based on sea level pressure and surface winds, a track prediction that is close to the observed track, good rain band structure, a realistic eyewall precipitation structure, a good warm core structure, a well-correlated simulated reflectivity, a realistic microphysical representation, as well as having appropriate values of mixing ratios, horizontal and vertical velocities, and precipitation rates for a hurricane of that intensity. An endeavor to compare the ultimate model simulations by these parameters is performed, and assessed.

13 CHAPTER THREE: DATA, DESCRIPTION OF THE MODEL, AND INITIALIZATION

3.1 Data and Observations Utilized

When evaluating model simulated results, one must carefully select data sets and observations that perform well at the scale of your simulation, and are adequately able to aid in elucidating the overall results of comparing the model versus reality. Also, it is useful and consistent to utilize the same data set for initialization for the skill score computations later. This allows a fair and accurate comparison of the results and permits a deeper understanding of the physical processes involved in producing the simulation. One such data set used to initialize the model that was later used in skill score computation is the National Centers for Environmental Prediction (NCEP) Final Analyses (FNL) Data. The datasets for the initial state and time-varying boundary conditions for the numerical experiments were obtained from the NCEP FNL analyses for the respective model domains for Erin (2001) and Dennis (2005). These datasets are Global Final Analyses available from NCEP-NCAR at one degree grids every 6 hours.

All analyses consist of SLP, T, RH, U, V, Psfc, geopotential height, soil values, ice cover, and a variety of other variables at 26 mandatory pressure levels from 1000 to 10 hPa. These same data sets were used for computing the skill scores of the model experiments and final runs. Another useful data set that is used in this study to compare model simulated wind fields to is the Hurricane Research Division (HRD) HWIND product. The HWIND datasets are useful for comparison of the structural characteristics of the model-produced surface wind speed, and are obtained from the surface wind analyses of the Atlantic Oceanographic and Meteorological Laboratory (AOML). They integrate various available tropical cyclone observation platforms (e.g. ships, buoys, coastal platforms, aviation reports, reconnaissance aircraft data, winds from polar-orbiting SSM/I, ERS,

14 QuikSCAT, TRMM and GOES cloud drift winds) to develop an objective analysis for the distribution of wind speeds in a hurricane. The ER-2 Doppler Radar (EDOP) is another instrument that is highly useful for model simulation comparisons, and it is an X-band (9.6 GHz) fully-coherent pulsed Doppler radar mounted in the nose of the ER-2. It measures the vertical reflectivity and wind structure of mesoscale precipitation systems. EDOP has two antennas pointing at nadir and 33 degrees ahead of nadir. For a flight level of 20km above the surface, the nadir surface footprint is 1km (Heymsfield et al, 1996). Calibration occurs to the instrument and surface measurements from the two beams are compared with published values as well as statistics from TRMM estimated values. EDOP provides excellent radar imagery of a storm along the flight path for comparison with model output. The track along which the ER-2 flew for Hurricane Erin is shown in Fig. 3.1 and 3.2, utilizing both visible and infrared imagery overlays to show the flight track through mature Hurricane Erin. A similar flight track is available for Dennis, but it has no satellite overlay for the hurricane at that time. The flight that the ER-2 took over Dennis from Costa Rica and back is acknowledged by Fig. 3.3. Last, but certainly not least in terms of observations that can be compared with a simulated version of them for a given time, is the Tropical Rainfall Measuring Mission (TRMM) Data Set. The precipitation datasets for comparisons with observations were collected for the forecast periods for both hurricane simulations. These datasets are the NASA TRMM 3B42 GSFC obtained gridded 3-hourly rain-rate estimates at a horizontal resolution of 0.25° X 0.25° in a global latitude expanse from 50.0°N to 50.0°S. The 3B42 is a useful data set because is a blended product, hailing from satellite sensors or missions such as TRMM, SSMI, AMSR and AMSU. Each precipitation field is best interpreted as the precipitation rate effective at the observed time. These datasets were interpolated for every 1-min interval, the time step of the WRF model, for the 24 hours of RRI. Later these data sets were averaged over a 24 hour period and used for observational comparisons to model forecasts.

15 3.2 Description of the WRF Model

This study utilizes the state-of-the-art research mesoscale model known as the Advanced Research WRF (Weather Research and Forecasting model) developed at the National Center for Atmospheric Research (NCAR). The model was developed to be a highly flexible, portable code that can be utilized for both research and also operational purposes. The WRF model ARW system is a primitive equation, non-hydrostatic Eulerian mass coordinate (“em” solver) system with a dynamic core and a dynamics solver with various other components including physics schemes (which were specifically utilized in this study), initialization schemes and a data assimilation package. The version used in this study is the Advanced Research WRF (ARW) version 2.1. Though there are two dynamics solvers in the WRF Software Framework (WRS), the Advanced Research WRF (ARW) solver is utilized in this study because of its scientific and algorithmic approaches to modeling the atmosphere. The ARW solver integrates the compressible, non-hydrostatic Euler equations formulated utilizing a terrain-following mass vertical coordinate denoted as η, which has values that vary from 1.0 at the surface to 0.0 at the upper boundary of the model domain. The coordinate is given as η = (ph – pht) / µ where µ = phs – pht. In these equations ph is the hydrostatic component of the pressure, and phs and pht refer to values along the surface and top boundaries, respectively, Skamarock et al (2005). The Advanced Research WRF has a variety of supported functions and characteristics. First, the model equation set has a run-time hydrostatic option available, and it is conservative for scalar variables. The top of the model is a constant pressure surface and the horizontal grid is the Arakawa-C grid. The time-integration scheme in the model uses the third-order Runge-Kutta scheme, and the spatial discretization makes use of the second to sixth order schemes. The model supports both idealized and real-data applications with various lateral and top boundary condition options, and also supports one-way, two-way and moving nest options. The model runs on most single-processor, shared- and distributed-memory machines so its applications have wide usage among the public mesoscale modeling community, Skamarock et al (2005).

16 The WRF V2.1 ARW model used to carry out the microphysical parameterization experiments in this study has specific configurations based on the results of prior mesoscale modeling studies. The model has some of the best available physics and microphysical parameterization schemes to produce forecasts that closely represent the observed trends of the two tropical cyclones explored in this study. The specific basic run options chosen for Hurricane Erin (2001) include a total forecast time of 48 hours, from 0000 UTC Sept 09 through 0000 UTC Sept 11, 2001. The time step of the model integration is every 60 seconds, and the model utilizes two domains, and outer and an inner. The outer domain has a grid size of 25km and the inner domain has a grid size of 5km. For the specific options and schemes chosen for parameterizing and initializing the model, each were chosen based on the literature review and were chosen to best simulate a tropical cyclone environment in the model. The WSM 6-class graupel scheme was chosen (and later modified) for the microphysical parameterization scheme. The longwave radiation scheme chosen was the Rapid Radiative Transfer Model (RRTM) scheme, and the shortwave radiation scheme chosen was the Dudhia scheme. For the surface-layer representation, the Monin-Obukhov (Janjic) scheme was chosen, and complimented by the boundary-layer physics given in the Mellor-Yamada Janjic TKE scheme. The land-surface thermal diffusion scheme was selected, and cumulus option 0 was chosen for innermost domain because the innermost domain resolution is less than 10 km. Variations in parameterizations to the WSM 6-class graupel microphysics scheme include the reduction of the melting of Snow, reduction of the melting of graupel, reduction of the evaporation of rain water, reduction of the fall speed of snow reduction of the fall speed of graupel, and reduction of the intercept parameter for graupel. The model run configuration for the first storm, Hurricane Erin of 2001, includes a double-nested run. Erin’s outer domain (domain 1) is set at 25km and its inner domain (domain 2) is set at 5km. To make the inner domain, the nest-down option between domains and inclusion of nested boundary conditions was implemented. This inner domain allows the initial conditions for domain 2 to be based on the successful output from domain one. In this way, domain 2 runs in a single-nested way. The finest model

17 grid domain was chosen to better resolve the hurricane’s inner core not only during its intensification stages, but over the course of the entire 48 hour model forecast period. This storm, which was never extremely intense during its lifecycle, has a 5km inner domain designed to be less than the 10km threshold for cloud resolving capabilities and yet not so small that it becomes far too computationally intensive for timing purposes of running 18 experiments for each storm. The model run time captured the hurricane’s transit north of 30°N until off of the mid-Atlantic coast and through its lifecycle while it maintained its tropical characteristics. The model run configuration for the second storm, Hurricane Dennis of 2005, includes a triple-nested run. Dennis’ outer domain (domain 1) is also set at 25km. However its middle domain (domain 2) is set at 8.3km and the innermost domain (domain 3) is set at 2.7km. These inner domains are also created from the nest-down option in WRF which allows the initial conditions for domain 2 to be based on the successful output from domain 1, and the initial conditions for domain 3 to be based on the successful output from domain 2 . In this way, domain 3 runs in a single-nested way. The finest model grid domain was chosen for Dennis for the same reasons as with Erin; to best resolve the hurricane’s inner core not only during its intensification stages, but over the course of the entire 48 hour model forecast period. Hurricane Dennis became a more intense hurricane in its lifecycle than Hurricane Erin, and though it was quite a bit more computationally intensive to run the experiments for Hurricane Dennis at such a fine resolution, it was necessary due to the structure and intensity of Dennis and its core. The specific options and schemes chosen for Dennis were exactly the same as for Erin, but with a different set of domain numbers and sizes, and a different model integration time step. The grid sizes were previously mentioned, and the model integration time step is 12 seconds for the Dennis simulations.

3.3 Initializing the Model

Existing microphysical parameterization of this model is built from results of mid-latitude observations. Substantial improvement to the model’s intensity forecasting in the tropics can be made via proper parameterization of the model microphysics for

18 hurricanes. The initial and boundary conditions used in the innermost grid with finer resolution are obtained from the respective outermost grids where rain rate initialization is invoked. All of the results are illustrated for the highest-resolution innermost domain, which is integrated using an explicit microphysics scheme. Each of the experiments are integrated for a forty-eight hour forecast period, adequately capturing the mature and intensification stages of the two hurricanes. The Krishnamurti et al, 2007 technique of Rain Rate Initialization (henceforth denoted as RRI) for a mesoscale atmospheric model is utilized for initializing the precipitation structure for the model forecast time periods. The RRI takes place in the outermost domain for the period of twenty four hours before the model forecast start time. For Erin, this means that RRI takes place at the 25km outer grid from 0000 UTC September 8 through 0000 UTC September 9, 2001. For Dennis, this means that RRI takes place at the 25km outermost grid from 0000 UTC July 8 through 0000 UTC July 9, 2005. While RRI techniques are not a primary focus of this study and are only utilized for initializing the model, it is clear that he majority of the intensity improvement in the WRF model simulations of hurricanes Erin and Dennis is attributable to the RRI technique’s effect on precipitation structure and, through a variety of related processes, subsequently the intensity enhancement. A background understanding of this technique is therefore necessitated. A schematic diagram of the RRI technique is illustrated in Fig. 3.4. Understanding the Rain Rate Initialization technique comes primarily from the examination of typical model-developed convective and non-convective rain. For Convective Rain, moisture profiles and data such as vertical velocity (omega), temperature field (to get saturation) (T), and specific humidity (incorporated into the moisture part) (q) are put into the model calculations. Totaling these variables gives a model input of Omega, T, and q. What comes out of a non-RRI mesoscale model is Rain- and it’s usually pretty wrong compared to reality. Most of the blame for this can be attributed to the moisture data. If that can be changed to get a rain rate similar to the observed, then a better forecast should emerge. The formal way to do this is to change the moisture sets so that they agree- this is the biggest aspect of RRI.

19 In the case of the WRF model, performing RRI includes the model adding a delta q to the q values which in turn change many profiles at once. The difference between the model rain and the satellite rain (the observations) must be minimized across whatever profiles are analyzed. To accomplish this in the WRF model the Relative Humidity (RH) is manipulated. This can be expressed as RH + RH’ where RH’ is a straight line (a linear function of pressure) and has a max possible value of 20%. This straight line has a slope of alpha. Different values of alpha minimize the difference between the Model Rain and Satellite Rain. The best alpha-value gives you the best rain rate initialization RH value, and optimal rain rate initialization can occur. For Day 0 minus 1 we change the q profile and adjust over several points; many at once across the whole model grid. The results are then compared to the observations after each time step and the “closest” match of the different options is used to initialize the next step. Then, when the first 24 hour forecast period is over, the model is allowed to run and produce a 48 hour (2-day) forecast. RRI was performed in this way for both hurricanes Erin and Dennis.

Fig. 3.1 Flight track with times (UTC) of the NASA ER-2 aircraft into Hurricane Erin (2001) during the CAMEX-4 field campaign, overlaid with visible satellite imagery.

Fig. 3.2 Flight track with times (UTC) of the NASA ER-2 aircraft into Hurricane Erin (2001) during the CAMEX-4 field campaign, overlaid with enhanced Infrared satellite imagery.

Fig. 3.3 Flight track with times (UTC) of the NASA ER-2 aircraft into Hurricane Dennis (2005) during the TCSP mission.

Fig. 3.4 The Krishnamurti et al, 2007 technique of Rain Rate Initialization procedure for atmospheric mesoscale models.

22 CHAPTER FOUR: EXPERIMENT DESIGN AND IMPLEMENTATION

4.1 Alterations to Microphysical Parameterization in the Model and Justification of Methodology

The aforementioned studies found that alterations to particular species of hydrometeors have impacts, large and small, on the intensity, structure of precipitation and vertical make-up of a hurricane. It has been shown that there is a need for improved microphysical parameterization, which is a proven factor of intensity prediction. First Rain Rate Initialization is applied for the 24 hours prior to the model forecast start time. The initialized WRF model is allowed to spin up (on average takes about 12-18 hrs to have a good degree of skill) and produce a simulated storm for Erin or Dennis for 48 hours, with only one microphysical parameterization altered at a time in the WSM 6-class graupel scheme. These alterations take place as described in Table 1. In Table 1, each experiment type is given a series number, for ease of grouping experiments in a logical order and describing the impacts on each later. Experiment Series 1 refers to any reduction of the melting of snow while Experiment Series 2 refers to any reduction of the melting of graupel. Experiment Series 3 refers to any reduction of the evaporation of rain water. Experiment Series 4 refers to any reduction of the fall speed of snow while Experiment Series 5 refers to any reduction of the fall speed of graupel. Lastly, Experiment Series 6 refers to changes made to the intercept parameter of graupel. Across the top of Table 1, the percentage shown indicates the percent reduction of a variable in that column, for its respective row. An example of how to read the table is given in the caption below it. As previously mentioned, these parameters were chosen and altered based on prior literature studies, such as Pattnaik and Krishnamurti (2007) where it was found that “experiments where physical processes such as melting of graupel, snow and cloud ice were suppressed… and where evaporation of rain water was suppressed [within the model]… produced the most intense storms [compared to other sensitivity experiments].”

23 It was also observed in many studies that the complete absence of physical processes, as they relate to microphysics, or where they are completely turned off in the model, may lead to the right results for the wrong reasons. Physically speaking, turning off a physical process completely in a model is going to have an unrealistic effect on the intensity prediction that would never be mimicked by the real storm environment. With this in mind, no “hard” sensitivity was used. Instead, some degree of sensitivity adjustment was selected (25%). For example, notice there is no 100% reduction of a variable because this is not physically realistic in a hurricane. This allowed for a 75% reduction, 50% reduction and 25% reduction of each variable to be examined. Any more frequent interval could have been used, but a quarter-interval was used to show meaningful differences between each experiment, as well as to save computation time on 18 experiments for two storms at very fine resolutions. This method of parameterization experimentation was also used to forecast the impacts of altering each microphysical variable in a fair way so as to examine one impact at a time. It is realized that in future studies, combinations of altering multiple parameters at a time should be used to prevent nonlinearity between the experiments, but in this case the interest is in forecasting the impacts only, and that is beyond the scope of this study.

4.2 Running, Post-processing and Evaluating

Utilizing the method discussed, and according to Table 4.1, each of the 18 total experiments for each hurricane was run for a full uninterrupted 48 hours using the resources of either The Florida State University Computational Sciences and Information Technology (CSIT) IBM Supercomputer Facility (Eclipse/Terragold) or the National Center for Atmospheric Research (NCAR) Computational and Information Systems Laboratory (CISL). Output files for each experiment were generated by the WRF model and processed by the WRF-To-GrADS post-processing package, in order to allow the output to be visualized by the Grid Analysis and Display System (GrADS) (available at http://grads.iges.org/grads/grads.html). The next step was to analyze the output for each experiment and to determine which experiments had the largest impact on improving the hurricane’s intensity in each

24 category. To accurately compare each microphysical parameterization experiment, skill scores and amount of improvement in intensity were used as benchmarks to determine the best runs from the rest. First, simulated storm intensity was plotted for Predicted Minimum Sea Level Pressure (slp) as well as for Predicted 10-meter Surface Winds derived as the magnitude of the u-component of the wind at 10 m and the v-component of the wind at 10 m (winds at 10 m). Next, Root Mean Square Errors (RMSE) were calculated as compared to NCEP FNL data for slp and winds at 10 m, as were Anomaly Correlation Values (ANOMCOR) were calculated for intensity based on slp and winds at 10 m as compared to NCEP FNL data. All calculations and plots were produced 6-hourly for every experiment, as well as for the control runs which did not include any alterations to the microphysics schemes. The computation of RMSE and ANOMCOR values was necessary for several reasons. RMSE is necessary for comparing observations to modeled output to obtain meaningful results. The lower the RMSE, the better the forecasted variable performs in that particular experiment. ANOMCOR is necessary for assessing the model’s skill at predicting anomalous results for that experimental parameterization. ANOMCOR is the overlay of the observed anomaly with the predicted anomaly with scores ranging from 0.0 to 1.0, and the closer to 1.0, the better the forecasted variable. The statistical definitions for RMSE and ANOMCOR are given below and are governed by the equations defined Ross and Krishnamurti (2005).

N 2/1 ⎡ 1 2 ⎤ RMSE = ⎢ ∑( f n − on ) ⎥ ⎣ N n=1 ⎦

N [( f − c )(o − c )] Anomaly Correlation = ∑n=1 n n n n N N 2/1 ( f − c ) 2 (o − c ) 2 []∑∑n==11n n n n n In these expressions: N= number of grid points

fn= forecast value at grid point n

on= observed value at grid point n

cn= climatological (mean) value at grid point n

25 Using this outlined methodology, an example for selecting the best parameterization options for the WSM-6 class graupel scheme in the WRF model for a single storm is given here. For Hurricane Dennis (2005), Experiment Series 2 (reduction of the melting of graupel) exemplifies how one chooses the optimal reduction percentage for a given series. First, slp is computed and plotted against the control run and NHC values for the three experiments in Series 2. This is shown in Fig. 4.2. Based on how it performs during the maturation and intensification stages of the hurricane (for Dennis this is between 1800 UTC July 9 and 1800 UTC July 10), with respect to the other runs and observations, it is decided if this is a potential candidate for the OPTMP run. If the experiment performs better than the CTRL run for slp, it is considered a candidate. In this case, reduction by both 50% and 25% qualify. Next the simulated hurricane’s resultant intensity values for slp and winds at 10 m are subjected to skill score testing. The RMSE and ANOMCOR values are grouped by experiment series number (ex. Experiment Series 2 varied reduction of the melting of graupel in the model), and evaluated for the lowest RMSE and highest ANOMCOR values in each category, as shown in Table 4.3 and Table 4.4. A graphical representation of these numbers that helps elucidate the trends the best is shown in Figure 4.5. It shows how the desirable choices are a low RMSE and a high ANOMCOR compared to the other runs. The best-performing experiment, in terms of these intensity guiding parameters, is chosen to be simulated in the OPTMP run for that Experiment Series.

4.3 Final Microphysical Parameterization Coefficients Selected for OPTMP Run

Based on the Intensity prediction, RMSE and ANOMCOR values from all of the individual experiments, the microphysical coefficients in the WSM-6 class graupel scheme that would be utilized to make one final “best combination” run, for each storm respectively, were chosen. These selections are shown in Table 4.6. In Hurricane Erin, the melting of snow was reduced by 25%, the melting of graupel was reduced by 75%, the evaporation of rain water was reduced by 25%, the fall speed of snow was reduced by 50%, the fall speed of graupel was reduced by 25%, and

26 no alteration was made to the intercept parameter for graupel. The intercept parameter for graupel remains at 4.e6 for the OPTMP run because it was found, in this instance, that reducing this process did not have a meaningful impact on the storm’s intensity. In Hurricane Dennis, the melting of snow was reduced by 75%, the melting of graupel was reduced by 50%, the evaporation of rain water was reduced by 25%, the fall speed of snow was reduced by 75%, the fall speed of graupel was reduced by 75%, and no alteration was made to the intercept parameter for graupel. The intercept parameter for graupel remains at 4.e6 for the OPTMP run because it was found, in this instance, that reducing this process also did not have a meaningful impact on this storm’s intensity. These two OPTMP runs (one per storm) were substituted into the model in place of the individual experiments and were subjected to the same domains, run times, boundary conditions, initializations, options and schemes as the experiments were. The model output was obtained from both runs and was processed the same way that each experiment’s output was processed.

27 Table 4.1: Percentage of Variable Reduction per Experiment Series.

Percentage of Variable Reduction Experiment Series 75% 50% 25%

1 A=0.25*variable A=0.50*variable A=0.75*variable # 2 B=0.25*variable B=0.50*variable B=0.75*variable

3 C=0.25*variable C=0.50*variable C=0.75*variable

4 D=0.25*variable D=0.50*variable D=0.75*variable

5 E=0.25*variable E=0.50*variable E=0.75*variable

6- Change the F=3.e6 F=2.e6 F=1.e6 intercept parameter constant for graupel

A=Melting of Snow; B=Melting of Graupel; C=Evaporation of Rain Water; D=Fall Speed of Snow; E=Fall Speed of Graupel; F=Intercept Parameter for Graupel There are 18 experiments per hurricane. Only one microphysical variable is altered at a time, and all other variables are held constant. #Example: Row 2, Column 3 would read as “Experiment Series One: Reduction of the Melting of Snow: by 50%”

28 A

C Fig. 4.2 A, B, & C Hurricane Dennis Experiment Series 2 example of selecting best options for OPTMP runs, for reducing the melting of graupel by 75, 50 and 25 percent, respectively. This pressure versus time plot for Hurricane Dennis (2005) compares real storm observation from the NHC (blue) with the CTRL run (green) and the microphysics example parameterization to select which parameterization has the best intensity improvement of the experiments in that series. Based on how the experiment performs during the maturation and intensification stages of the hurricane it is decided if it is a potential candidate for the OPTMP run.

29 Table 4.3 RMSE computation example for Experiment Series 2.

Table 4.4 Anomaly Correlation computation example for Experiment Series 2.

Fig. 4.5 The RMSE and ANOMCOR error bar graphs for Experiment Series 2 for minimum central pressure and maximum winds at 10m. This is the main skill score computation method by which the optimal microphysical combination choices were made.

Coefficients for OPMPT runs chosen Erin Dennis

Exp Series 1- Red. of 25% 75% Melting of Snow by Exp Series 2 - Red. of 75% 50% Melting of Graupel by Exp Series 3 - Red. of 25% 25% Evap. of Rainwater by Exp Series 4 - Red. of 50% 75% Fall Speed of Snow by Exp Series 5 - Red. of 25% 75% Fall Speed of Graupel by Exp Series 6 - Intercept 4.e6 4.e6 Parameter for Graupel

31 CHAPTER 5 RESULTS AND ANALYSIS OF RESULTS

5.1 Presentation of Results

The optimal microphysically parameterized model runs (OPTMP) for both Erin and Dennis are now compared against their respective control runs (CTRL), which are rain rate initialized but there are no alterations to any model parameterizations. To accurately compare the OPTMP and CTRL runs for each storm, each is plotted against observations and the WRF model alone for Simulated Storm Intensity, Predicted Minimum Sea Level Pressure (slp), Predicted 10-meter Surface Winds (magnitude of u10 & v10), Simulated Storm Tracks, Root Mean Square Errors (as compared to FNL) for slp and winds at 10 m, and Anomaly Correlations (also compared to NCEP FNL data) for the same slp and winds at 10 m. All were produced 6-hourly for each OPTMP, CTRL, and WRF alone run. Also Equitable Threat Scores are computed for the OPTMP and CTRL runs against observed values. ETS is vital because it indicates the degree of skill to forecast an event. Overall, it is the measure of the correctly predicted events against observations in a defined range, adjusted for hits with a random chance. Often used in the verification of rainfall in NWP models, its “equitability” allows scores to be compared more fairly across different ranges. ETS values vary between 0.0 and 1.0, with a perfect forecast scoring 1.0, and a completely deficient forecast scoring 0.0, Krishnamurti et al, 2007. The equation for computation of ETS is given as:

⎡ O ⎤ H − F( ) ⎢ N ⎥ ETS = ⎣ ⎦ 0( ≤ ETS ≤ )1 ⎡ O ⎤ F + O − H − F( ) ⎣⎢ N ⎦⎥ where N = number of grid points, F = area where event is forecasted, O = area where event is observed, and H = hit area, or overlap of areas F and O.

32 The next set of comparisons deals with comparing the OPTMP and CTRL runs against one another for storm structure and composition. These comparisons suggest which (OPTMP or CTRL) is stronger or weaker, or has the most realism in terms of typical hurricane structure. To fairly compare the two storms, each variable is plotted for a 24 hour average during the most intense phase of its respective hurricane. For hurricane Dennis, this will be from 1800 UTC July 09 through 1800 UTC July 10, 2005- encompassing 5 model time snap-shots for each modeled variable. For hurricane Erin, this period will be from 1800 UTC Sept 09 through 1800 UTC Sept 10, 2001- encompassing 5 model time snap-shots for each modeled variable. Finally, the OPTMP and CTRL runs are pitted against real observations spatially and temporally from HWIND, Land-based radar reflectivity, TRMM, and EDOP. Conclusions about the overall performances and advantages of the model runs are gleaned, and discussed.

5.2 Results

5.2.1 Intensity, Track and Skill Score Computations

After the initial 12-18 hours of model spin up, the simulated hurricane Erin’s minimum central pressure and maximum winds at 10m attempt to model the storm’s shifting intensity. This is shown clearly in Fig. 5.1 and Fig. 5.2. While the largest intensity improvement is due to RRI, OPTMP improves upon the intensity forecast of the CTRL during the mature stage of Erin. The primary time period of maturation and intensification of hurricane Erin is from 1800 UTC Sept 09 through 1800 UTC Sept 10, 2001. During the final 6 forecast hours the CTRL is continually intensifying, whereas OPTMP appears to take into account the weakening effects on Erin due to the short-wave trough interactions and the gradual breakdown of the subtropical ridge leading to Erin’s eventual rightward turn. Overall, there is approximately a 10-15% improvement seen in the intensity prediction for Erin between 1800 UTC Sept 9 ad 1800 UTC Sept 10, 2001. Again, after the initial 12-18 hours of model spin up time, the simulated hurricane Dennis’ minimum central pressure and maximum winds at 10m attempt to model the

33 storm’s deepening trend. Again, after the initial 12-18 hours of model spin up, the simulated hurricane Dennis’ minimum central pressure and maximum winds at 10m attempt to model the storm’s intensification period. While largest intensity improvement here is also due to the RRI used, the OPTMP improves upon the intensity forecast of the CTRL during the mature stage of Dennis. The primary time period of the mature, intensifying hurricane Dennis is from 1800 UTC July 09 through 1800 UTC July 10, 2005. For the most part, the timing of the simulated maximum intensity appears to coincide well with the actual observations. This is all shown in Fig. 5.3 and Fig. 5.4. The OPTMP track of hurricane Erin is quite interesting compared to the CTRL track. This is one instance, of few, when the OPTMP run produced an improved track forecast over the CTRL, instead of being the same. Here the precipitation is better described by the OPTMP run (as will be shown later) because low-level convergence is better described. This enhances the production of low-level cyclonic vorticity and so the model track moves more closely with the observed NHC track. Also note that this improved the OPTMP storm’s translation speed over the CTRL, so it more closely matches the NHC observed track, as shown in Fig. 5.5. Although the OPTMP produces a track with less error than the CTRL produces, they both still have a tendency to introduce the rightward turn of Erin too soon. Make note of the difference of the final center location of Erin between the OPTMP and CTRL runs, as this is important to remember when comparisons to observations are made later on. In general, Hurricane Dennis’ track did not tend to change that much due to the microphysical parameterization alterations. Note in Fig 5.6 that the CTRL and OPTMP 6- hourly locations coincide well. It does seem that the initialized vortex, for the Dennis case, has some inherent initial position error. Thus, in the plot it appears as if the model storms are moving faster, when in fact its translation speed is the same as the actual storm, but they are just positioned further ahead spatially. The skill scores for Erin’s OPTMP are generally an impressive improvement over the CTRL run. Fig. 5.7 shows the total 48-hr forecast period Root Mean Square Error (RMSE) for Mean Minimum Sea Level Pressure (hPa) for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. Notice that in the final 24 hour forecast period, the OPTMP has the largest improvement over the CTRL run, and in

34 general, it makes a better forecast out to 48 hours, in terms of predicted intensity by slp. On average, there seems to be an error improvement at each time step of one quarter to half of one hPa made by the OPTMP over the CTRL. Similarly, Fig. 5.8 shows the total 48-hr forecast period Anomaly Correlation (ANOMCOR) for Mean Minimum Sea Level Pressure (hPa), shown as a correlation value, for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. Again, notice that in the final 24 hour forecast period, the OPTMP has the largest improvement over the CTRL. Fig. 5.9 and Fig. 5.10 show that these same improvements of the OPTMP run over the CTRL run for RMSE and ANOMCOR continue to persist in the intensity prediction based on winds at 10 m (m/s) as well. The improvement in the final 24 hour forecast period is consistent and significant for the winds at 10 m. Meanwhile, Fig. 5.11 shows that for Hurricane Dennis (2005) the total 48-hr forecast period Root Mean Square Error (RMSE) for Mean Minimum Sea Level Pressure (hPa) for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data, there is an improvement but it is not as great as Erin’s. For hurricane Dennis, in the final 24 hour forecast period, the OPTMP has the largest improvement over the CTRL run, and in general (as with Erin), it makes a better forecast out to 48 hours, in terms of predicted intensity by slp. On average, there seems to be a small error improvement at each time step of one quarter of one hPa made by the OPTMP over the CTRL. Similarly, Fig. 5.12 shows the total 48-hr forecast period Anomaly Correlation (ANOMCOR) for Mean Minimum Sea Level Pressure (hPa), shown as a correlation value, for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data. Again, notice that in the final 24 hour forecast period, the OPTMP has the largest improvement over the CTRL. Fig. 5.13 and Fig. 5.14 show that the expected improvements of the OPTMP run over the CTRL run for RMSE and ANOMCOR do not necessarily tend to persist in the intensity prediction based on winds at 10 m (m/s). Instead, the CTRL run sometimes does as well as or better than the OPTMP run, and sometimes it is vice versa for Dennis’s skill scores based on winds at 10 m. Therefore, these results are inconclusive about Dennis’ intensity based on winds at 10 m.

35 The Equitable Threat Scores (ETS) for Day 2 in the forecast period for each storm are shown in Fig. 5.15. Fig. 5.15 A and B show the Equitable Threat Scores (ETS) versus Rainfall Threshold (mm) for the Day 2 (second 24 hours in the forecast period for each storm) forecasts of hurricanes Erin (A) and Dennis (B), and compare the CTRL run (green) and the OPTMP run (red) against the NCEP fnl analysis for each storm, respectively. Threat scores range between 0.0 and 1.0. The higher the ETS at a given threshold, the more overlap there was between the forecasted rainfall and the observed rainfall over that 24 hour period. While both Day 2 ETS charts show an improvement of the OPTMP over the CTRL, Erin’s is a more dramatic improvement. Note that the x-axis in both diagrams is different from many others in this study, in that it is neither longitude varying, nor time. Instead, it is the threshold of rainwater in mm. The higher H is in the equation, or the overlap of F and O, the higher the ETS will be, and this was the case for the OPTMP run over the CTRL run for both hurricanes.

5.2.2 Simulated Hurricane Structure and Composition Comparisons

The optimal microphysically parameterized run (OPTMP) has already been compared with observational data, along with the CTRL run, for the intensity, track, error, anomaly correlation and skill of the forecast at correctly predicting precipitation. According to section 2.2 the next step toward proving the OPTMP run to be a “realistic storm” is to show that at the appropriate times, it has proper thermodynamic structure, good rain band structure, a realistic eyewall precipitation structure, a good warm core structure, a well-correlated simulated reflectivity, a realistic microphysical representation, as well as having appropriate values of mixing ratios, horizontal and vertical velocities, and precipitation rates for a hurricane of that intensity. The endeavor, discussed earlier, to compare the ultimate model simulations by these parameters is performed and assessed in this section utilizing Fig. 5.16 – 5.29. Each diagram may be thought of as a slice through the center of the respective storm at each 6-hourly position that is averaged for a 24 hour period (5 snapshots in total). The slice is allows longitude to vary and plots the desired variable in height coordinates, with height on the y-axis given in km from the surface. For Erin, the 24 hour

36 time period is during the maturation and intensification period from 1800 UTC Sept 9 through 1800 UTC Sept 10, 2001. For Dennis, the 24 hour time period is during the maturation and intensification period of the storm, from 1800 UTC July 9 through 1800 UTC July 10, 2005. Each variable plotted is derived from the innermost domain of the model with the finest resolution. Also, it is important to note that some of the mesoscale features in the Dennis plots will tend to appear more prominently than in the Erin plots since there is a difference in their respective innermost domain resolutions. In Fig. 5.16 of 24-hr averaged vertical cross section of horizontal winds (m/s) for CTRL (A) and OPTMP (B) runs for Hurricane Erin (2001), it is noticeable that the OPTMP averaged wind field does a better job producing a more realistic core region with a more well-defined eye near 65°W/66°W and a vertically enhanced spatial distribution. The wind speeds in the eyewall region are also stronger and more characteristic of a storm of Erin’s categorical intensity during this 24 hour period. In Fig. 5.17 of 24-hr averaged vertical cross section of horizontal winds (m/s) for CTRL (A) and OPTMP (B) runs for Hurricane Dennis (2005), it is noticeable that the OPTMP averaged wind field does a better job producing a more realistic core region with a more well-defined eye near 86°W and a vertically enhanced spatial distribution. In the OPTMP run it is also clear that there is a more banded structure to the wind field, indicative of a more classic hurricane-like structure of precipitation bands. In Dennis’ case, this was prevalent more on the east side of the storm during these times, and that is certainly more the case in the OPTMP run than in the CTRL run. The WRF model overall does a good job representing the erosion of the western side of Dennis’ eye during this time period, but the OPTMP better produces the banding structure seen during these times. In Fig. 5.18 of 24-hr averaged vertical cross section of Composite Simulated Reflectivity (dBz) for CTRL (A) and OPTMP (B) runs for Hurricane Erin (2001), it is noticeable that Erin’s western structures are more prevalent in the model than its eastern structures. Hurricane Erin has been extensively analyzed (Halverson et al, 2006) and during this 24 hour time period there was a good presence of ice hydrometeors in Erin’s cloud tops. Notice that the CTRL does not show this at all (above 500 hPa where it should generally be), but the OPTMP does a fantastic job at improving the presence and

37 realistic distribution of frozen hydrometeors. This is perhaps the most interesting improvement of the OPTMP run over the control run since it has furthered the development of upper-level hydrometeor species. In Fig. 5.19 of 24-hr averaged vertical cross section of Composite Simulated Reflectivity (dBz) for CTRL (A) and OPTMP (B) runs for Hurricane Dennis (2005), the vertical hot towers simulated in the OPTMP run averaged reflectivity are enhanced and more reflectivity is present in the western eyewall region, as opposed to an absence of it in the CTRL run. This improvement shows a classic intense hurricane structure that was not captured as well before the optimal microphysical improvements took place. In Fig. 5.20 of 24-hr averaged vertical cross section of Equivalent Potential Temperature Deviation (K) for CTRL (A) and OPTMP (B) runs for Hurricane Erin (2001), positive values are shaded and negative values are contoured. This deviation is calculated as the 24 hour average ofΘe − Θe = Θe′ . In this diagram it is apparent that the OPTMP has a greater avg. theta-e deviation (in excess of 6 degrees Kelvin). The structure of this warm anomaly of theta-e is also more akin to classic hurricane theta-e profiles. In Fig. 5.21 of 24-hr averaged vertical cross section of Equivalent Potential Temperature Deviation (K) for CTRL (A) and OPTMP (B) runs for Hurricane Dennis (2005), positive values are shaded and negative values are contoured. This deviation is calculated as the 24 hour average ofΘe − Θe = Θe′ . In this diagram, while Dennis’ avg. theta-e anomaly during its intensification period may be somewhat weaker than we saw for Erin, the upper levels begin to show the most improved structure in the OPTMP. In Fig. 5.22 of 24-hr averaged vertical cross section of Temperature Deviation (K) for CTRL (A) and OPTMP (B) runs for Hurricane Erin (2001), positive values are shaded and negative values are contoured. This temperature deviation is calculated as the 24 hour average of T − T = T ′ and Erin’s avg. vertical temperature deviation improves marginally in the OPTMP run over the CTRL run, and is closer to reality, even though traditionally the location of maximum temperature deviation higher in the troposphere for a hurricane such as Erin. In Fig. 5.23 for the same variable but for Hurricane Dennis (2005), we see that although Dennis’ temperature deviation is not as strong as Erin’s, its OPTMP upper level structure is an improvement over the CTRL structure for a hurricane of Dennis’ categorical intensity during this time period.

38 In Fig. 5.24 of 24-hr averaged vertical cross section of Vertical Velocity (m/s) for CTRL (A) and OPTMP (B) runs for Hurricane Erin (2001) where positive values are shaded (upward vertical velocity), and negative values are contoured (downward vertical velocity), it is noticeable that there is an increase in the overall distribution of upward and downward vertical velocity. Note the obvious vertical velocity improvements around the “avg. eye” region for Erin and more downdrafts are present now too- likely aiding the formation of ice species, leading to greater latent heat release in the core, and likely increasing the hurricane’s intensity. With the OPTMP improving the coincidence of updrafts and downdrafts near one another and with an increase in the magnitude of these vertical velocities within the storm (over the CTRL), more sensible heat is being transported up into the storm. Also notice that both the eyewall structure appearance and the eyewall dynamic processes are more well-defined and more “realistic looking” in the OPTMP run than in the CTRL run. The stronger updraft in the OPTMP core eyewall region extends from the surface upward and aids in the transport of sensible heat and moisture fluxes upward into the eyewall which can increase the storm’s intensity. On average, this is also happening in the OPTMP rainband areas just outside of the eyewall, aiding the inward transport of sensible heat and moisture fluxes into the eyewall. This will also aid in the production of graupel and other ice species above the freezing level. The downward flow in the figure is showing a continuous outflow of energy exchange in the inner eyewall in the OPTMP run, and leads to an intensity boost. This same type of vertical velocity plot for Hurricane Dennis (2005) is given by Fig. 5.25 and in general, the same can be said for Dennis, but at a lower magnitude. This under-representation of vertical velocities in mesoscale model hurricane prediction is seen across many studies such as McFarquhar et al (2006) and Rogers et al (2006), and is an area that needs improvement. Still, on avg. in the OPTMP, we see an increase in the production of higher velocities at higher positions in the storm structure. In Fig. 5.26 of 24-hr averaged vertical cross section of Mixing Ratios (g/kg) for CTRL (A) and OPTMP (B) runs for Hurricane Erin (2001), Graupel Mixing Ratios are shaded, Snow Mixing Ratios are dotted, and Rain Water Mixing Ratios are solid. In this diagram, notice an all-around increase in the horizontal distribution of snow, graupel and Rain Water mixing ratios. All three of these species have improved eyewall distributions

39 over the CTRL run that make the OPTMP simulation more “realistic looking” than the CTRL. Notice that the horizontal distribution of snow in the OPTMP run is more continuous in the inner eyewall region whereas this appears patchy in the CTRL run. In the OPTMP we have reduced the fall speeds of snow and graupel, and graupel formation is due mostly to a greater presence of snow, which the mixing ratios show well. Since the fall speed of snow is reduced, it has more opportunity to collide with other particles and the autoconversion processes from snow to graupel can happen more frequently. Thus, the presence of more graupel than snow is found in the OPTMP run compared to the CTRL run. The increased presence of graupel should lead to enhanced rain water precipitation, which is seen as well in the OPTMP run. For Dennis, shown in Fig. 5.27, this may still be the case, but it is not nearly as dramatic. Dennis’ OPTMP run has only a slightly advantageous appearance over the CTRL run in that the magnitude of the mixing ratios for all three species has increased. Fig. 5.28 represents the 24-hr averaged vertical cross section of Mixing Ratios (g/kg) for CTRL (A) and OPTMP (B) runs for Hurricane Erin (2001), where Cloud Ice Mixing Ratios are shaded and Cloud Liquid Water Mixing Ratios are contoured. Now it is apparent that there is a more continuous distribution of cloud ice in the OPTMP run than in the CTRL. Based on what was seen for the previous Mixing Ratio plot for Erin, this is expected. A greater presence of cloud ice allows for more conversion to snow, and thus an increase in the production of graupel throughout the storm. The vertical extent of the ice distribution is higher and the horizontal distribution of ice greater in the OPTMP run. Near the eyewall, there is also a region of enhanced precipitation over the CTRL run. Interestingly, the same plot shown for Hurricane Dennis (2005), given by Fig. 5.29, has inconclusive results. It is not apparent that the CTRL performs less well than the OPTMP, and that may be the case, but there is not enough evidence in this figure to support such a claim. The diagrams are different, but neither has any particular improvement over the other that makes it appear more “realistic looking” than the other. At least there is a good presence of ice and water vapor, indicative of an environment conducive to hydrometeor growth and autoconversion processes, so the physical processes seen in the prior figure for Dennis are sensible.

40 5.2.3 Simulations versus Observations

Now that the differences in structural and physical characteristics between the OPTMP and CTRL runs for both storms have been shown, the CTRL and OPTMP runs for each storm can be compared to actual observations of Hurricanes Erin and Dennis from satellites, aircraft and radar observations. These comparisons are illustrated in Fig. 5.30- 5.39. Fig. 5.30 A, B, and C show the Hurricane Erin (2001) HRD HWIND spatial analysis at 1930 UTC Sept 09 (A) compared to simulated spatial wind analyses from the CTRL run (B) and OPTMP run (C) at 1800 UTC Sept 09. This HWIND diagram derives from a human analysis of US AF recon 700 hPa winds adjusted to the surface, ship reports, and a position extrapolation from a wind center fix. In this figure it is notable that the observed Hurricane Erin’s wind field is somewhat skewed to the east, and following the contours on plots B and C, we see that the OPTMP simulation better captures this feature for this time period than the CTRL simulation. Also, the HWIND analysis shows a tight, symmetric core wind region with wind speeds in excess of 80 kts, and clearly this is better simulated by the OPTMP run, which has a tight, symmetric core wind region that is stronger, overall, than the CTRL run offers. One day later, Fig. 5.31 A, B, and C shows the Hurricane Erin (2001) HRD HWIND spatial analysis at 1819 UTC Sept 10 (A) compared to simulated spatial wind analyses from the CTRL run (B) and OPTMP run (C) at 1800 UTC Sept 10. Obtained from slightly different observations than the first HWIND diagram, this HWIND diagram derives from a human analysis of NOAA P3 surface winds, GOES visible cloud drift winds adjacent to the surface, ship reports, 7 GPS Sonde surface winds computed from MBL and a NOAA wind center fix. Notice that at 1800 UTC on Sept 10 the actual wind field for Hurricane Erin is asymmetric and not as strong as it was 24 hours prior. Of the two model simulations, only the OPTMP captures the asymmetric wind field for Erin, even though the wind maxima is located to the south instead of the north shown by the HWIND diagram. Also, because of the difference in storm center location, explained in section 5.2.1, the location of the storm wind field for the CTRL is displaced further from the center of where the HWIND diagram’s center latitude and longitude position is than the OPTMP run is. Perhaps this may be another

41 reason why the CTRL did not perform as well as the OPTMP run when compared to the actual wind field. Due to its westward displacement, the CTRL may have been subjected to shortwave trough interactions in the model time that the real Hurricane Erin, and the OPTMP as well, had not yet experienced by 1800 UTC Sept 10, 2001. The HWIND diagram comparisons for Hurricane Dennis are given by Fig. 5.32 A, B, and C. This HRD HWIND spatial analysis at 2230 UTC July 10 (A) is compared to simulated spatial wind analyses from the CTRL run (B) and OPTMP run (C) at 0000 UTC July 10 and is derived from a human analysis of a variety of buoys, GPS Sondes, satellite derived wind fields and imagery, and SHIP guidance. The most notable feature for comparison here is the HWIND diagrams representation of Dennis’ bimodal wind field structure. This is only well-represented by the OPTMP simulation. Also notice that the overall wind field of Dennis was skewed to the northeast and this is more the case with the OPTMP run than with the CTRL run. Additionally, the CTRL run has weaker southern eyewall winds than the OPTMP does, and this compares nicely with the southern eyewall winds in the HWIND diagram. Although the winds are weaker on that side, the hurricane is not open to the south as much as the CTRL run simulates. Fig. 5.33 illustrates the next comparison between the simulated reflectivities of the two simulations. Fig. 5.33 A and B show Hurricane Erin (2001) Simulated Spatial Composite Reflectivity (dBz) at 0000 UTC Sept 10 for the CTRL run (A) versus the OPTMP run (B). In the future, NOAA P3 fuselage reflectivity data may be obtained to compare these simulations to real Erin data, since Erin was not located near any land- based radar like the Dennis images presented next are. Notice that in Fig. 5.33 the primary differences between the two simulations that make the OPTMP more of a realistic storm at this snapshot in the model are better eastern spiral banding features, a convectively closed eye (during a fairly intense phase of Erin), and an increased presence of convective rain on the western side of the storm. Fig. 5.34 A, B and C show Hurricane Dennis (2005) Simulated Spatial Composite Reflectivity (dBz) at 1600 UTC July 09 for the Key West radar reflectivity (dBz) versus 1800 UTC July 09 for the CTRL run (B) and the OPTMP run (C). The Key West radar imagery was utilized to compare spatial and structural differences between the CTRL and OPTMP runs to reality. Here, it is notable that Radar beam attenuation forbids us from

42 seeing the western half of Dennis’ radar reflectivity, but spiral band features across FL are better matched by OPTMP, as well as across Cuba, and the OPTMP has a more well- pronounced eye than the CTRL shows. Approximately 24 hours later, Fig. 5.35 A, B, and C show Hurricane Dennis (2005) Simulated Spatial Composite Reflectivity (dBz) at 1900 UTC July 10 for the Pensacola radar reflectivity (dBz) versus 1800 UTC July 10 for the CTRL run (B) and the OPTMP run (C). The Pensacola radar imagery was utilized to compare spatial and structural differences between the CTRL and OPTMP runs to reality for this time snapshot. Improved precipitation structure at landfall in the panhandle region of Florida and a rain-wrapped eye region make the OPTMP more similar to the Pensacola radar than the CTRL run. Also, the tighter rainband structure in the OPTMP run more closely matches actual radar reflectivity than that of the CTRL run. Fig. 5.36 A, B, and C show Hurricane Erin (2001) Accumulated Rainfall (mm) for Day 2 of the forecast (second 24 hours or from 0000 UTC Sept 10 through 0000 UTC Sept 11) shown by Tropical Rainfall Measuring Mission (TRMM) (A) compared to Simulated Accumulated Rainfall (mm) in the CTRL run (B) and the OPTMP run (C). The black arrow is the same for every diagram, and shows the translation direction of the actual hurricane and associated precipitation. This line is plotted at the same location in the diagrams of the OPTMP and CTRL precipitation. The purple arrow shows the translation direction and associated precipitation for the CTRL storm and aids in visualizing the differences that exist between the CTRL and the actual precipitation structure. Although there is a tendency to over-represent the accumulated precipitation over this 24 hour time period for both model simulations, the OPTMP simulation tends to better place the center of precipitation and the tilt of it than the CTRL run does. This is one factor that aided the OPTMP ETS scores for Erin in Day 2 over the CTRL since the CTRL had the rain in the entirely wrong location. Again, this is due to a track error on the part of the model CTRL run, and the OPTMP run corrected for this bias. Fig. 5.37 A, B, and C show Hurricane Dennis (2005) Accumulated Rainfall (mm) for Day 2 of the forecast (second 24 hours or from 0000 UTC July 10 through 0000 UTC July 11) shown by Tropical Rainfall Measuring Mission (TRMM) (A) compared to Simulated Accumulated Rainfall (mm) in the CTRL run (B) and the OPTMP run (C). 24 hour totals again show that the model tends to overestimate the total precipitation.

43 However, the OPTMP matches the bimodal structure of the FL coastline rainfall better than the CTRL run, and the north-central FL TRMM rainfall better matches the OPTMP than the CTRL 24 hour accumulated rainfall. These features are outlined by the arrows (north-central FL) and the circle and line features (bimodal structure) seen in Fig. 5.37. Lastly, comparisons with ER-2 Doppler Radar (EDOP) are made for Hurricanes Erin and Dennis from their respective field campaign observations. These snapshots are some of the most impressive figures showing the vast improvement the OPTMP method has on hurricane simulation. This is one example of many promoting the necessity for increased field observations of tropical cyclones. Each diagram is like a slice through the storm allowing longitude to vary and plotting reflectivity and simulated reflectivity by height in kilometers. Hurricane Erin was observed by EDOP during CAMEX-4. Fig. 5.38 A, B, and C shows Erin Simulated Vertical Profile of Reflectivity (dBz) at 35°N at 2000 UTC on Sept 10 for the CTRL run (C) and the OPTMP run (B) versus Observed CAMEX-4 ER-2 Doppler Radar (EDOP) Reflectivity (dBz) (A) at the same latitude and longitudes (68°W to 60°W) for 1906-1950 UTC. The most notable feature of the figure is the remarkable way that the OPTMP simulates the western eyewall region reflectivity seen in the EDOP reflectivity. The CTRL misses the upper-level structure seen by EDOP, but the OPTMP does not. Although under-represented, the OPTMP still captures the two convective band features on the eastern side of the eyewall, whereas the CTRL does not do this well. Hurricane Dennis was observed by EDOP during TCSP. Fig. 5.39 A, B, and C shows Dennis Simulated Vertical Profile of Reflectivity (dBz) at 24°N at 1200 UTC on July 09 for the CTRL run (C) and the OPTMP run (B) versus Observed TCSP ER-2 Doppler Radar (EDOP) Reflectivity (dBz) (A) at the same latitude and longitudes (84°W to 80°W) for 1420-1440 UTC. The most notable features of this figure are the improved upper level representation of the reflectivity in the eyewall and the extent of the vertical hot tower representation (as noted in the introduction, this is an important feature impacting intensity) by the OPTMP compared to the CTRL. The EDOP reflectivity captures it well, but this slice through the simulated Dennis really shows the improved rainband representation, eyewall region and upper level simulated reflectivity by the OPTMP run over the CTRL.

Fig. 5.1 Hurricane Erin (2001) Minimum Central Pressure versus Time chart for the WRF model Only (purple), NHC Storm data (blue), the CTRL run (green), and the OPTMP run (red).

Fig. 5.2 Hurricane Erin (2001) Maximum Winds at 10m versus Time chart for the WRF model Only (purple), NHC Storm data (blue), the CTRL run (green), and the OPTMP run (red).

Fig. 5.3 Hurricane Erin (2001) Minimum Central Pressure versus Time chart for the WRF model Only (purple), NHC Storm data (blue), the CTRL run (green), and the OPTMP run (red).

Fig. 5.4 Hurricane Erin (2001) Maximum Winds at 10m versus Time chart for the WRF model Only (purple), NHC Storm data (blue), the CTRL run (green), and the OPTMP run (red).

Fig. 5.5 Hurricane Erin (2001) Storm Track for NHC Storm data (blue), the CTRL run (green), and the OPTMP run (red).

Fig. 5.6 Hurricane Dennis (2005) Storm Track for NHC (OFCI) Storm data (red), the CTRL run (purple), and the OPTMP (RINIT) run (green).

Fig. 5.11 Hurricane Dennis (2005) total 48-hr forecast period Root Mean Square Error (RMSE) for Mean Minimum Sea Level Pressure (hPa) for both the CTRL (green) and OPTMP (red) runs, as compared with the NCEP fnl data.

A B Fig. 5.15 A and B Equitable Threat Scores (ETS) versus Rainfall Threshold (mm) for the Day 2 (second 24 hours in the forecast period for each storm) forecasts of hurricanes Erin (A) and Dennis (B), comparing the CTRL run (green) and the OPTMP run (red) against the NCEP fnl analysis for each storm, respectively. Threat scores range between 0.0 and 1.0. The higher the ETS at a given threshold, the more overlap there was between the forecasted rainfall and the observed rainfall over that 24 hour period.

A B Fig. 5.16 Hurricane Erin (2001) 24-hr averaged vertical cross section of horizontal winds (m/s) for CTRL (A) and OPTMP (B) runs.

A B Fig. 5.17 Hurricane Dennis (2005) 24-hr averaged vertical cross section of horizontal winds (m/s) for CTRL (A) and OPTMP (B) runs.

A B Fig. 5.18 Hurricane Erin (2001) 24-hr averaged vertical cross section of Composite Simulated Reflectivity (dBz) for CTRL (A) and OPTMP (B) runs.

A B Fig. 5.19 Hurricane Dennis (2005) 24-hr averaged vertical cross section of Composite Simulated Reflectivity (dBz) for CTRL (A) and OPTMP (B) runs.

A B Fig. 5.20 Hurricane Erin (2001) 24-hr averaged vertical cross section of Equivalent Potential Temperature Deviation (K) for CTRL (A) and OPTMP (B) runs. Positive va lues are shaded, and negative values are contoured.

A B Fig. 5.21 Hurricane Dennis (2005) 24-hr averaged vertical cross section of Equivalent Potential Temperature Deviation (K) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured.

A B Fig. 5.22 Hurricane Erin (2001) 24-hr averaged vertical cross section of Temperature Deviation (K) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured.

A B Fig. 5.23 Hurricane Dennis (2005) 24-hr averaged vertical cross section of Temperature Deviation (K) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured.

A B Fig. 5.24 Hurricane Erin (2001) 24-hr averaged vertical cross section of Vertical Velocity (m/s) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured.

A B Fig. 5.25 Hurricane Dennis (2005) 24-hr averaged vertical cross section of Vertical Velocity (m/s) for CTRL (A) and OPTMP (B) runs. Positive values are shaded, and negative values are contoured.

A B Fig. 5.26 Hurricane Erin (2001) 24-hr averaged vertical cross section of Mixing Ratios (g/kg) for CTRL (A) and OPTMP (B) runs. Graupel Mixing Ratios are shaded, Snow Mixing Ratios are dotted, and Rain Water Mixing Ratios are solid.

A B Fig. 5.27 Hurricane Dennis (2005) 24-hr averaged vertical cross section of Mixing Ratios (g/kg) for CTRL (A) and OPTMP (B) runs. Graupel Mixing Ratios are shaded, Snow Mixing Ratios are dotted, and Rain Water Mixing Ratios are solid.

A B Fig. 5.28 Hurricane Erin (2001) 24-hr averaged vertical cross section of Mixing Ratios (g/kg) for CTRL (A) and OPTMP (B) runs. Cloud Ice Mixing Ratios are shaded and Cloud Liquid Water Mixing Ratios are contoured.

A B Fig. 5.29 Hurricane Erin (2001) 24-hr averaged vertical cross section of Mixing Ratios (g/kg) for CTRL (A) and OPTMP (B) runs. Cloud Ice Mixing Ratios are shaded and Cloud Liquid Water Mixing Ratios are contoured.

58 A

BC Fig. 5.30 A, B, and C Hurricane Erin (2001) HRD HWIND spatial analysis at 1930 UTC Sept 09 (A) compared to simulated spatial wind analyses from the CTRL run (B) and OPTMP run (C) at 1800 UTC Sept 09. This HWIND diagram derives from a human analysis of US AF recon 700 hPa winds adjusted to the surface, ship reports, and a position extrapolation from a wind center fix.

59 A

BC Fig. 5.31 A, B, and C Hurricane Erin (2001) HRD HWIND spatial analysis at 1819 UTC Sept 10 (A) compared to simulated spatial wind analyses from the CTRL run (B) and OPTMP run (C) at 1800 UTC Sept 10. This HWIND diagram derives from a human analysis of NOAA P3 surface winds, GOES visible cloud drift winds adjacent to the surface, ship reports, 7 GPS Sonde surface winds computed from MBL and a NOAA wind center fix.

60 A

BC Fig. 5.32 A, B, and C Hurricane Dennis (2005) HRD HWIND spatial analysis at 2230 UTC July 10 (A) compared to simulated spatial wind analyses from the CTRL run (B) and OPTMP run (C) at 0000 UTC July 10. This HWIND diagram derives from a human analysis of a variety of buoys, GPS Sondes, satellite derived wind fields and imagery, and SHIP guidance.

A B Fig. 5.33 A and B Hurricane Erin (2001) Simulated Spatial Composite Reflectivity (dBz) at 0000 UTC Sept 10 for the CTRL run (A) versus the OPTMP run (B).

A Key West Radar Reflectivity 16:00 UTC July 09, 2005

B C Fig. 5.34 A, B and C Hurricane Dennis (2005) Simulated Spatial Composite Reflectivity (dBz) at 1600 UTC July 09 for the Key West radar reflectivity (dBz) versus 1800 UTC July 09 for the CTRL run (B) and the OPTMP run (C). The Key West radar imagery was utilized to compare spatial and structural differences between the CTRL and OPTMP runs to reality.

A Pensacola Radar Reflectivity 19:00 UTC July 10, 2005

B C Fig. 5.35 A, B, and C Hurricane Dennis (2005) Simulated Spatial Composite Reflectivity (dBz) at 1900 UTC July 10 for the Pensacola radar reflectivity (dBz) versus 1800 UTC July 10 for the CTRL run (B) and the OPTMP run (C). The Pensacola radar imagery was utilized to compare spatial and structural differences between the CTRL and OPTMP runs to reality.

63 AB

C Fig. 5.36 A, B, and C Hurricane Erin (2001) Accumulated Rainfall (mm) for Day 2 of the forecast (second 24 hours or from 0000 UTC Sept 10 through 0000 UTC Sept 11) shown by Tropical Rainfall Measuring Mission (TRMM) (A) compared to Simulated Accumulated Rainfall (mm) in the CTRL run (B) and the OPTMP run (C).

64 AB

C Fig. 5.37 A, B, and C Hurricane Dennis (2005) Accumulated Rainfall (mm) for Day 2 of the forecast (second 24 hours or from 0000 UTC July 10 through 0000 UTC July 11) shown by Tropical Rainfall Measuring Mission (TRMM) (A) compared to Simulated Accumulated Rainfall (mm) in the CTRL run (B) and the OPTMP run (C).

C Fig. 5.38 A, B, and C Erin Simulated Vertical Profile of Reflectivity (dBz) at 35° N at 2000 UTC on Sept 10 for the CTRL run (C) and the OPTMP run (B) versus Observed CAMEX-4 ER-2 Doppler Radar (EDOP) Reflectivity (dBz) (A) at the same latitude and longitudes for 1906-1950 UTC.

66 A

C Fig. 5.39 A, B, and C Dennis Simulated Vertical Profile of Reflectivity (dBz) at 24° N at 1200 UTC on July 09 for the CTRL run (C) and the OPTMP run (B) versus Observed TCSP ER-2 Doppler Radar (EDOP) Reflectivity (dBz) (A) at the same latitude and longitudes for 1420-1440 UTC.

67 CHAPTER SIX: SUMMARY AND CONCLUSIONS

The experiments performed in this study have effectively shown a method for developing proper microphysical parameterization coefficients for a given tropical cyclone during its maturation and intensification stages. The experimental design was developed based on the results of prior studies, addressed in the literature review, and based on the fundamental roles of microphysical processes in tropical cyclones. It is clearly shown that the most effective way to parameterize a tropical cyclone for microphysics is to take it on a case by case basis. This was seen to be effective for both Hurricane Erin (2001) and Dennis (2005). Recognizing the many facets involved in intensity prediction, what was presented here is a procedure, and by no means a final solution. However, through comparison with observations and between model simulations, all of the goals this study set out to achieve were accomplished. The OPTMP runs for both hurricanes had improved microphysical parameterization in a mesoscale model that positively affected intensity forecasting out to 48 hours while more accurately simulating the real storm environment. One primary objective was to have an improved intensity prediction from a more “realistic-looking storm” than the WRF can produce on its own. A “realistic-looking” storm for the purposes of this research should have ideally met a particular set of criteria at the appropriate times, and did. These included proper thermodynamic structure, close-matching intensity prediction based on sea level pressure and surface winds, a track prediction that is close to the observed track, good rain band structure, a realistic eyewall precipitation structure, a good warm core structure, a well- correlated simulated reflectivity, a realistic microphysical representation, as well as having appropriate values of mixing ratios, horizontal and vertical velocities, and precipitation rates for a hurricane of that intensity. It was seen in the results chapter (Chapter 5), that changing the microphysical parameterizations alters the processes optimally and provides a better hurricane intensity forecast. Some other results gleaned from this study follow. It was shown that the

68 OPTMP consistently performs better than the CTRL for a ll of the variable profiles examined. The method by which we chose the OPTMP parameterizations was effective for both Dennis and Erin. The evaluation of intensity via RMSE and ANOMCOR is an effective method to help choose those combinations. Threat scores showed a consistent improv ement to the precipitation in the final 24 hours of both forecasts. The hurricane vertical cross sections of the inner core and the structure of the hydrometeors for the optimal run were better represented than in the control run, for a “realistic-looking” hurricane of that respective intensity. In general, the hurricane track is minimally affected by the microphysical parameterizations, and when there was a small impact, it was positiv e because of the improvements to hurricane inner core structure and rainfall distribution. Though the optimal combinations of microphysical changes between the two hurricanes are not the same, this is attributable to the characteristic features of each storm, such as model resolution, categorical intensity, and location (Dennis had terrain impacts, Erin was in the open Atlantic). There was a consistent improvement seen in the OPTMP forecast (wind field, rainband structure, Day 2 precipitation forecast and radar reflectivity). Lastly, observations showed that the OPTMP produced a more realistic storm than the CTRL for spatial simulated reflectivity, wind field representation, 24 hour rainfall, and vertical simulated reflectivity profiles. Although the Krishnamurti et al (2006) technique of Rain Rate Initialization is the most responsible for the statistical improvements to the hurricane intensity forecast seen, the methodology of producing an optimal microphysically parameterized simulation (post- RRI) is a great enhancement to this. This study showed an improvement to the structure, precipitation, microphysical characteristics, simulated reflectivity and overall intensity improvement with regard to minimum central sea level pressure and maximum winds at 10 m when RRI was enhanced by an optimal microphysical parameterization uniquely derived for each storm based on skill scores and measurable intensity prediction improvement.

69 CHAPTER SEVEN: FUTURE WORK

Though this work explored a new technique for improving the microphysical parameterization of a mesoscale model, there is only minor physical basis for why these specific parameterized coefficients were used. Many scientists in the field would like to see the modeling community as a whole “place more emphasis on physical processes occurring within hurricanes and on the development of parameterization schemes with more physical basis (McFarquhar et al, 2006).” This is likely the next phase of this research. Although a unified, single parameterization scheme is not likely to work optimally across a wide distribution of tropical cyclones, it would be a huge step forward in the hurricane intensity prediction issue if tropical field experiment observations can lend a hand to a more realistic parameterization for mesoscale modeling of TCs. Coefficients developed specifically for microphysical parameterization of these storms is shown here to work better than the model’s parameterization schemes built on results from mid-latitude field observations. Another important aspect of future research in this area should include examination of Planetary Boundary Layer (PBL) scheme parameterizations and the subsequent effect on microphysics fields will be necessary since it “calculates the surface fluxes and vertical mixing within the boundary layer, key processes that determine the structure and intensity of updrafts originating from the boundary layer (Rogers and Black et al, 2007).” Clearly, more observations are needed to calibrate the sophisticated explicit moisture parameterization schemes and so continued funding of field research should be a science community priority. Comparison with even more observations such as AMPR, NOAA P3 & ER-2 dropsondes and P3 fuselage data would allow this method to be further explored for validity. Also, this method should be assessed for a wide distribution of storms. Weaker TCs as well as extremely intense TCs need to be tested to see if the microphysical parameterization techniques shown can stand up to those storm types as well. Additionally, more storms would need more observations to compare in situ and

70 remote sensing observations against simulated forecasts. Furth er validation across more storms would allow this method of microphysical parameterization to be judged for consistent improvements to hurricane intensity prediction. Future work that will definitely be explored is determining how to utilize this methodology, so it can be further enhanced to construct a superensemble based upon statistical changes in microphysical parameterization schemes to improve the intensity prediction. This would be a particularly good way to make changes to a broad range of models in a suite where microphysics is explicitly considered. Wherever the meteorological community’s focus shifts to, we will inevitably return to the issue of a lack of skill when forecasting TC intensity days in advance, and microphysics will always play a large role in this issue. This is one area of mesoscale modeling that is not likely to have a hard and fast solution any time soon.

71 REFERENCES

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Selected Figure images courtesy of: Fig. 1.2 http://cimss.ssec.wisc.edu/tropic/archive/

Fig. 1.3 NASA GSFC- Hurricane Erin GOES-8 Multi-spectral image

BIOGRAPHICAL SKETCH

Cerese Marie Albers was born on November 19, 1983 in West Islip, NY. In June of 2001 she graduated from West Islip High School and was the first Finalist in the National Hispanic Recognition Program in her high school’s history. In the fall of 2001 she began attending The Florida State University on a full academic scholarship. In 2004 she interned at NASA Goddard Space Flight Center (GSFC) where she began her research on improving hurricane intensity forecasting in the area of cloud microphysics. In 2005 Cerese was accepted into Chi Epsilon Pi, the Meteorology Honor Society. She received her Bachelor of Science degree in Meteorology as an Honors graduate from The Florida State University in 2005 as well. Cerese has been a Graduate Research Assistant in Dr. T.N. Krishnamurti's lab since her 2005 admittance to the graduate program. She participated as a student forecaster in both the NASA Tropical Cloud Systems and Processes Mission (TCSP) in 2005 as well as the NASA African Monsoon Multidisciplinary Analyses (NAMMA) Mission in 2006 and her research interests include utilizing observations made during these, and other, field experiments to validate mesoscale model intensity prediction.