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A Model for Refining Precipitation-Type

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A Model for Refining Precipitation-Type A MODEL FOR REFINING PRECIPITATION-TYPE FORECASTS FOR WINTER WEATHER IN THE PIEDMONT REGION OF NORTH CAROLINA ON THE BASIS OF PARTIAL THICKNESS AND SYNOPTIC WEATHER PATTERNS CPT Matthew P. Cuviello A thesis submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Masters in the Department of Geography (Climatology). Chapel Hill 2007 Approved by: Dr. Charles E. Konrad II Dr. Peter J. Robinson Dr. John F. Florin © 2007 Matthew P. Cuviello ALL RIGHTS RESERVED ii ABSTRACT Matthew P. Cuviello: A model for refining Precipitation-Type Forecasts for Winter Weather in the Piedmont Region of North Carolina on the basis of Partial Thickness and Synoptic Weather Patterns (Under the direction of Charles E. Konrad II) Determining precipitation types and intensity of winter weather events in the Piedmont region of North Carolina is difficult for forecasters. Sounding and climatological data along with cyclone and anticyclone characteristics (e.g. pressure, location, and time) from microfilmed surface weather maps are examined for 237 winter weather events at Greensboro, North Carolina over a 37 year period. The data is used to explore precipitation type determination and synoptic (large scale) pattern influence on intensity. A model for predicting precipitation type (i.e. a nomogram) is developed from the sounding and climate data. The tracks of the cyclones and anticyclones are classified and used to characterize the intensity of observed snow, freezing rain and sleet in the study area. iii ACKNOWLEDGEMENTS The opportunity to study at the University of North Carolina at Chapel Hill has been one of the many blessings God has bestowed upon me in my life. I am thankful to the US Army and the United States Military Academy for giving me the time and the funding to complete my graduate work and the opportunity to influence the lives of Cadets. I am truly honored and humbled to have been selected. I have many people to thank in the Tar Heel State. First and foremost is to my advisor and friend Dr. Chip Konrad. From conception to completion, his guidance, mentorship and patience were constant. This work would not have been completed without his dedication and enthusiasm. Many thanks go to my committee members, Dr. Peter Robinson and Dr. John Florin, for their guidance, advice and friendship. Thanks also to the other professors who taught me classes to include Dr. Lawrence Band, Dr. Scott Kirsch, Dr. Wendy Wolford, and Dr. Jun Liang. Special thanks go to Derek Anderson and Richard Redding for helping to collect radiosonde data for this Thesis. Lastly, I would like to acknowledge the love and support of my whole family, specifically my wife and kids. My sons, Carson and Austin Joe, and daughters, Grace and Caleigh, are my inspiration and hope. My wife, Amy, is the glue that keeps our family together through all that life Army throws at us. I am forever indebted to her for her unconditional love and support in whatever crazy endeavor I choose to tackle from deployments to Graduate School, the later being the safer and more pleasant option. To all those serving their country in harms way, God bless you and keep you safe. iv TABLE OF CONTENTS Abstract …………………………………………………………………………. iii Acknowledgements ……………………………………………………………... iv Introduction ……………………………………………………………………... 1 1.1 Research Context ……………………………………………………. 1 1.2 Background ………………………………………………………….. 2 1.3 Research Design……………………………………………………...19 Data and Methods ………………………………………………………………. 21 2.1 Introduction …..…………………………………………………….. 21 2.2 Study Area ………………………………………………………….. 21 2.3 Data Sources ………………………………………………………... 22 2.4 Methodology ………………………………………………………... 32 2.5 Data Analysis ……………………………………………………….. 38 Results and Discussion ………………………………………………………….. 40 3.1 Introduction …………………………………………………………..40 3.2 Sounding Classification ………………………………………………40 3.3 Nomogram Analysis ………………………………………………….41 3.4 TRENDs Nomogram Analysis………………………………………..51 3.5 Outlier Analysis ………………………………………………………53 3.6 Cyclone Tracks ……………………………………………………… 63 v 3.7 Anticyclone Analysis………………………………………………… 69 Summary and Conclusion ………………………………………………………...75 References ………………………………………………………………………..81 vi LIST OF TABLES Table 2.1 Winter Weather Database Variables ……………………………………….... 23 Table 2.2 Observed P-Type ……..……………………………………………………... 33 Table 2.3 P-Type Intensity Definitions ………………………………………………... 39 Table 3.1 Classification of Soundings by P-Type ………………………………………41 Table 3.2 Six Hour Greensboro Nomogram Statistics ………………………………….43 Table 3.3 Three Hour Greensboro Nomogram Statistics ……………………………….46 Table 3.4 Statistics for the 6 hour P-Type Classification on the TRENDS Nomogram ………………………………………………………...54 Table 3.5 Statistics for the 3 hour P-Type Classification on the TRENDs Nomogram.........................................................................................55 Table 3.6 Outlier Statistics for the 6hr Classification …………………………………..59 Table 3.7 Outlier Statistics for the 3hr Classification …………………………………..63 Table 3.8 Miller A Snow Producing Cyclones …………………………………………68 Table 3.9 Miller A Freezing Rain Producing Cyclones ……………………….…….….68 Table 3.10 Miller A Ice Pellet Producing Cyclones ……………………………………69 Table 3.11 Miller B Snow Producing Cyclones ………………………………………..70 Table 3.12 Miller B Freezing Rain Producing Cyclones ……………………………….70 Table 3.13 Miller B Freezing Rain Producing Cyclones ……………………………….72 Table 3.14 Anticyclone Strength resulting in Snow with a Miller A Cyclone Track ……………………………………………………………….73 Table 3.15 Anticyclone Strength resulting in Freezing Rain with a Miller A Cyclone Track………..………………………………………………………73 Table 3.16 Anticyclone Strength resulting in Ice Pellets with a Miller A Cyclone Track……………………………………………………………….74 vii Table 3.17 Anticyclone Strength resulting in Snow with a Miller B Cyclone Track ………………………………………………………………74 Table 3.18 Anticyclone Strength resulting in Freezing Rain with a Miller B Cyclone Track ………………………………………………………………75 Table 3.19 Anticyclone Strength resulting in Ice Pellets with a Miller B Cyclone Track .………………………………………………………………75 Table 3.20 Anticyclone Strength resulting in Snow with a No Cyclone Track ……..….76 Table 3.21 Anticyclone Strength resulting in Freezing Rain with a No Cyclone Track .………………………………………………………………76 Table 3.22 Anticyclone Strength resulting in Ice Pellets with a No Cyclone Track .………………………………………………………………76 viii LIST OF FIGURES Figure 1.1. Greensboro, NC and the Carolina Piedmont …………………………… 1 Figure 1.2 Atmospheric Snow Profile...…………………………………………….. 4 Figure 1.3 Atmospheric Sleet Profile ......………………………………………….. 4 Figure 1.4 Atmospheric Freezing Rain Profile……………………………………… 5 Figure 1.5 Affects of Diabatic Processes on P-Type Trajectory……………………..8 Figure 1.6 Keeter et al Empirical Database ………………………………………… 9 Figure 1.7 Partial Thickness Nomogram for Raleigh, NC …………………………. 11 Figure 1.8 Miller Type A Cyclones ………………………………………………… 13 Figure 1.9 Miller Type B Cyclones ………………………………………………… 13 Figure 1.10 Eastern Anticyclone …………………………………………………….15 Figure 1.11 CAD Event, “U” shaped isobars ………………………………………. 16 Figure 2.1 Piedmont of North Carolina …………………………………………….. 21 Figure 2.2 Physiographic Map of the Land Regions of the East Coast of the US …. 21 Figure 2.3 Skew-T sounding Graph at Greensboro ………………………………… 25 Figure 2.4 Vertical Temperature and Pressure Profile ……………………………... 26 Figure 2.5 Miller A Track Map Example ……………………………………………29 Figure 2.6 Miller B Track Map Example ……………………………………………30 Figure 2.7 No Cyclone Track Map Example ……………………………………….. 31 Figure 2.8 Six Hour Nomogram …………………………………………………….. 35 Figure 2.9 Three Hour Nomogram ………………………………………………….. 35 Figure 2.10 Six Hour Climatology ………………………………………………….. 36 Figure 2.11 Three Hour Climatology ……………………………………………….. 37 ix Figure 3.1 6hr Classification Soundings on the 6hr Greensboro Nomogram ………...45 Figure 3.2 3hr Classification Soundings on the 6hr Greensboro Nomogram ………...47 Figure 3.3 Differences in the Six Hour and Three Hour Greensboro Nomogram ……49 Figure 3.4 Comparison of the Six and Three Hour Greensboro Nomogram to the TRENDs nomogram………………………………………………..……....51 Figure 3.5 6hr Classification Soundings on the TRENDs Universal Nomogram …….56 Figure 3.6 3hr Classification Soundings on the TRENDs Universal Nomogram……..57 Figure 3.7 Six Hour Outliers ……………………………………………………….…60 Figure 3.8 Three Hour Outliers …………………………………………………….…64 Figure 3.9 Miller A Tracks Producing Winter P-Types …………………………...…67 Figure 3.10 Miller B Tracks Producing Winter P-Types …………………………….71 x CHAPTER 1 INTRODUCTION 1.1 Research Context “P erhaps the most challenging winter weather forecast problem in the Southeast is the forecasting precipitation type (P-type ).” (Keeter et al, 1995 ) This is especially true in the North Carolina Piedmont where f orecasters have developed techniques that have some skill in forecasting when a winter weather event will occur. The difficult part of the forecast is to determine the precipitation or P -type (i.e. rain, snow, freezing rain, and sleet ) that will f all in a region . Frozen P-type s bring about unique problems for a population not accustomed to winter weather. Small amounts of frozen and freezing precipitation can have a significant economic and safety impact. In the North Carolina Piedmont ( Fig 1. 1), P-type forecasting is especially challenging because of two general factors . First, computer generated forecast models Greensboro Piedmont Fig 1.1. Greensboro, NC and the Ca rolina Piedmont
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