INVESTIGATING PROBABILISTIC FORECASTING OF TROPICAL CYCLOGENESIS OVER THE NORTH ATLANTIC USING LINEAR AND NON-LINEAR CLASSIFIERS DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Christopher C. Hennon, M.S. * * * * * The Ohio State University 2003 Dissertation Committee: Approved by Dr. Jay Hobgood, Adviser Dr. John Rayner ________________________________ Adviser Dr. Hugh Willoughby Atmospheric Sciences Graduate Program ABSTRACT Current numerical weather prediction models experience great difficulty in forecasting tropical cyclogenesis, primarily because of limitations of cloud parameterizations and observations. Forecasters have also struggled with the problem since they rely on the numerical models as an objective source of information. This research was performed with the aim of filling the void of objective guidance for tropical cyclogenesis. A new dataset of cloud clusters is created through the examination of infrared (IR) satellite imagery over the tropical Atlantic during the 1998-2001 hurricane seasons. Eight large-scale predictors of tropical cyclogenesis were then calculated from NCEP-NCAR Reanalysis dataset for each 6-hour interval of the cloud cluster life cycle extending back to 48 hours prior to genesis. Independent classifications were then performed on the entire dataset using both discriminant analysis (DA) and an artificial neural network (NN). The classifiers are fundamentally different from each other in that DA performs classifications based solely on linear trends in the predictors; the NN is potentially a more powerful classifier as it can find non-linear relationships in the data. The performance of each classifier was investigated through statistical scores and a series of case studies from the 1998- 2001 Atlantic hurricane seasons. Tropical cyclogenesis is a rare event. Climatologically only about 15% of all cloud clusters develop into tropical depressions over the Atlantic Basin. The new cloud cluster database reflects that. 432 cloud clusters, of which 62 developed into tropical depressions, were tracked during the four seasons. Independent DA classifications show forecast skill over climatology. For the “prime” development season of August – October, the DA correctly forecast a higher percentage of clusters than climatology for all forecast periods. The most important predictors are latitude and the vertical shear structure. A comparison of DA forecasts with NN forecasts on the same dataset produced mixed results. The NN generally performed better with non-developing cloud clusters; however, there are indications that the NN suffers from over fitting to a greater degree than DA. An investigation of six case studies shows that both classifiers performed well in the majority of the cases. The DA appears to generalize much better than the NN in most cases. Danielle (1998) and a non-developing cluster (ND-6, 2000) brought to light several possible deficiencies in the statistical model. The large-scale predictors over-forecast genesis in a favorable shear environment, even if the thermodynamic environment is marginal. ii Also, the lack of any information on the convective structure of the cloud cluster will decrease forecast accuracy in some cases. Danielle (2000) developed explosively despite an unfavorable large-scale shear environment, perhaps due to mesoscale interactions that are not resolved in this model. Results suggest that this model has sufficient potential to be implemented as an objective forecast tool. Each predictor can easily be calculated from an analysis field that is routinely available to forecasters. The inclusion of mesoscale predictors, especially satellite derived temperature, moisture, and wind data, is thought to be an important next step for improvement of forecasts; especially since the current literature suggests that the important physical interactions for tropical cyclogenesis occur at the smaller scales. iii Dedicated to Paula and our baby iv ACKNOWLEDGMENTS This research is the product of contributions from many people. I would like to thank my advisor, Dr. Jay Hobgood, for the intellectual freedom to pursue this project and the sound advice that kept it going in the right direction. Dr. Hugh Willoughby graciously donated his time and energy to serve on my examining and defense committees. He also provided valuable guidance in the formulation of the project three years ago and has kept me scientifically thorough when I got lazy. I am also grateful for Dr. John Rayner’s insightful advice in regards to this work and early guidance in my student career at Ohio State. Comments from National Hurricane Center (NHC) forecasters James Franklin, Stacy Stewart, Miles Lawrence, Richard Pasch, and Jack Beven were an invaluable source of information regarding how tropical cyclogenesis is handled operationally in the Atlantic Basin. Commander Marge Nordman at the Joint Typhoon Warning Center (JTWC) provided me with similar information for the Central and Western Pacific Basins. I would also like to thank Dr. Jeff Halverson at NASA Goddard Space Flight Center for making my first professional manuscript submission a better one. Technical support was generously provided from a number of sources. Dr. Caren Marzban at Oklahoma was a willing and exhaustive source of information, constructive criticisms, and expert advice on the implementation and use of my neural network. Without his support this document would not have been possible in its present form. Dr. Michael Tiefelsdorf, Ohio State Department of Geography, helped me to sort out many statistical issues, especially in the early stages of the project. Department of Geography technicians Jim DeGrand and Jens Blegvad generously allowed me to perform this research within their quiet, secured office and were quick to address any hardware/software issues that arose. Irene Casas provided valuable guidance early on in developing the implementation of the neural network and the automated resampling of the sea surface temperature data. All data that were used in this project were obtained at no cost. The Space Science and Engineering Center (SSEC) at the University of Wisconsin provided all of the GOES-8 Infrared imagery for the western North Atlantic. Coverage in the far eastern North Atlantic was provided from Meteosat-7 imagery made available by the European Organisation for the Exploitation of v Meteorological Satellites (EUMETSAT). Dr. Greg Holland provided the computer software that allowed the calculation of the Maximum Potential Intensity predictor. Finally, I would like to acknowledge those persons who provided personal support at all stages of this work. My wife Paula has been an unending source of love, support, friendship, and intellectual stimulation. I am grateful that we shared this journey together and I hope I can provide even half of what she’s given me in return as she completes her dissertation. I am grateful to have parents that have always supported me unconditionally. Thank you Mom and Dad. I would also like to acknowledge Karen LeBel, a special person that I have known since my graduate work at Purdue University. She is a continuing source of friendship, laughter, and motivation. No, we are not going to name the baby “Z-O”. Finally, I’d like to thank Jim DeGrand, Jens Blegvad, and Joe Szymczak for endless hours of distraction and entertainment playing Age of Empires and Age of Mythology. Ready to play? vi VITA October 3, 1972 ……………………………….Born – Cleveland, Ohio, USA 1994 …………………………………………….B.A. with Honors, Aeronautics-Mathematics, Miami University (Ohio) 1996 …………………………………………….M.S., Atmospheric Science, Purdue University 1996 - 1998 ……………………………….……Meteorologist/Software Engineer, TASC, Inc., Reading, Massachusetts 1998 – 2000, 2003 …………………………… Graduate Teaching Assistant, The Ohio State University 2001 – 2002 …………………………………….Graduate Research Assistant, Byrd Polar Research Center, The Ohio State University FIELDS OF STUDY Major Field: Atmospheric Sciences vii TABLE OF CONTENTS Abstract………………………………………………………………………………………………………ii Dedication…………………………………………………………………………………………………..iv Acknowledgements…………………………………………………………………………………………v Vita………………………………………………………………………………………………………….vii List of Tables……………………………………………………………………………………………….xi List of Figures……………………………………………………………………………………………...xii List of Acronyms and Abbreviations……………………..……………………………………………..xvi Chapters: 1. Introduction………………………………………………………………………………………………1 1.1 Motivation………………………………………………………………………………….…1 1.2 Definition and Philosophy of Tropical Cyclogenesis……………………………………..4 1.3 Justification for the Use of Large-Scale Data…………………………………………….5 1.4 Summary……………………………………………………………………………………..6 2. Atlantic Tropical Cyclogenesis – Literature Review………………………………………………...8 2.1 Tropical Cyclogenesis Theory……………………………………………………………...8 2.1.1 CISK Theory……………………………………………………………………...8 2.1.2 WISHE………………………………………………………………………….…9 2.1.3 Mesoscale Convective Vorticees……………………………………………..10 2.1.4 Potential Vorticity……………………………………………………………….12 2.2 North Atlantic Basin Tropical Cyclogenesis…………………………………………..…12 2.2.1 Easterly Waves Over the Atlantic Basin…………………………………..…13 2.2.2 Favored Genesis Regions and Variability…………………………………...14 2.3 Forecasting Tropical Cyclogenesis Over the Atlantic Basin…………………………..15 2.4 Summary…………………………………………………………………………………....16 3. Datasets………………………………………………………………………………………………..17 3.1 National Hurricane Center “Best Track” Data File……………………………………...17