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Analysis of Prospective Fog Warning Systems Using AWOS/ASOS Station Data Throughout the State of Florida Justin Rivard

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Analysis of Prospective Fog Warning Systems Using AWOS/ASOS Station Data Throughout the State of Florida Justin Rivard Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2014 Analysis of Prospective Fog Warning Systems Using AWOS/ASOS Station Data Throughout the State of Florida Justin Rivard Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] FLORIDA STATE UNIVERSITY COLLEGE OF ARTS AND SCIENCES ANALYSIS OF PROSPECTIVE FOG WARNING SYSTEMS USING AWOS/ASOS STATION DATA THROUGHOUT THE STATE OF FLORIDA By JUSTIN RIVARD A Thesis submitted to the Department of Earth Ocean and Atmospheric Sciences in partial fulfillment of the requirements for the degree of Master of Science Degree Awarded: Summer Semester, 2014 Justin Rivard defended this thesis on July 17, 2014. The members of the supervisory committee were: Peter Ray Professor Directing Thesis Jeffrey Chagnon Committee Member Robert Hart Committee Member The Graduate School has verified and approved the above-named committee members, and certifies that the thesis has been approved in accordance with university requirements. ii This thesis is dedicated to my parents, Denis and Darlene Rivard and my brother Shawn, who have always guided and supported my career ambitions. Most of all, this thesis is dedicated to my life long friend, Katie Carlton. Who, for seven years of my life, was my motivation to do well and make a better life for myself. Without her support and dedication, I would not have been able to achieve my goals. I am forever thankful I was able to spend a major part of my life with her and learn so much. iii ACKNOWLEDGMENTS Many thanks are due to many people. Especially to my major professor, Peter Ray for his supervision, knowledge, and patience. Dr. Jeffrey Chagnon and Dr. Hart for their input and guidance, and to all my friends and family. iv TABLE OF CONTENTS ListofTables....................................... vii ListofFigures ....................................... viii ListofAbbreviations................................. ... xi Abstract........................................... xii 1 Introduction 1 1.1 FogTypes..................................... 6 1.1.1 AdvectionFog............................... 7 1.1.2 UpslopeFog................................ 8 1.1.3 FrontalFog ................................ 9 1.1.4 RadiationFog............................... 9 1.2 TheFogForecastingProblem . .. 12 1.2.1 Numerical Weather Prediction Limitations . ..... 14 1.3 FogForecastingTechniques . ... 15 1.3.1 The Croft et al. Conceptual Model for the Southern U.S. ...... 16 1.3.2 UPSAirlinesConceptualModel . 17 1.3.3 Forecasting Using Model Output Statistics . ..... 18 1.3.4 OtherFogForecastingTechniques . ... 20 2 Methodology 22 2.1 ThesisGoals.................................... 22 2.2 TheData ..................................... 23 2.3 Methodology for Detecting Fog at an AWOS/ASOS Station . ...... 27 2.4 Methodologies for Forecasting Fog at an AWOS/ASOS Station........ 28 3 Results 31 3.1 Fog/FireClimatologyofFlorida . .. 31 3.1.1 Topography ................................ 34 3.1.2 Fire .................................... 34 3.2 Forecasts...................................... 36 3.2.1 MesonetDataClimatology. 36 3.2.2 ConditionsFavorableforFogFormation . .... 42 3.2.3 CrossoverTemperature. 45 3.2.4 SkillScores ................................ 47 3.2.5 Detecting/DiagnosingFog . 48 3.2.6 Forecast .................................. 49 3.2.7 Characteristics of the Fog Forecast Model . ..... 52 3.2.8 ModelResultsandComparison . 54 v 4 Conclusions 66 References......................................... 68 BiographicalSketch ................................. ... 72 vi LIST OF TABLES 3.1 Climatological reference forecast example. Percentages represent what a fore- cast chance of fog would be dependent on month of year and station. ..... 47 3.2 Climatological probability of fog for a given lead time and dewpoint depression 50 3.3 Skill scores for MOS 12-UTC forecast for fog formation . .......... 54 3.4 Skill scores for 3-hour model forecast for 12-UTC . ......... 55 3.5 BrierandHeidkeskillscore . ... 55 vii LIST OF FIGURES 1.1 Observed soundings taken on 09/29/2012 at 12z for Jacksonville, FL (left) and Tampa Bay, FL (Right). (Courtesy of The University of Wyoming) . ..... 3 1.2 Meteogram of Jacksonville, FL for 24-hr period starting January 29, 2012 at 00Z.(PlymouthStateUniversity) . .. 4 1.3 Fog and Smoke Related Crash Density for 2006–2010. Units are in crashes per square mile. Data from Department of Transportation (DOT) accident reports. 5 1.4 Percentage of total fog occurrence given by time of day. Floridaonly. 11 1.5 Percentage of total fog occurrence given by month of year. Floridaonly. 12 2.1 All mesonet stations in Florida. Primary stations are defined as AWOS, ASOS, FAWN, and FWMD sites. Secondary sites are defined as individual and pri- vately owned weather stations. Latitiude and longitude data from NOAA. 23 2.2 PrimaryMesonetstationsinFlorida. ...... 24 2.3 ContingencyTable ................................ 29 3.1 Climatological location and frequency of fog in Florida, averaged per year from 2006 to 2010. GIS kriging technique used to draw contours . ........ 32 3.2 Estimated amount of low visibility occurrences using Low Visibility Occurrence Risk Index (LVORI) (Lavdas and Achtemeier, 1995). ..... 33 3.3 Topography of Florida in meters using shapefile data from the United States GeologicSurvey(USGS).............................. 35 3.4 Smoke dispersion modeling results at Paynes Prairie site. Concentrations in mi- crograms per meter-cubed. Top left emission rate = 0.0003gs−1m−2, Top right emission rate = 0.0006gs−1m−2, Bottom left emission rate = 0.0008gs−1m−2, Bottom right emission rate = 0.0016gs−1m−2. Outermost contour represents 100 micrograms per meter-cubed. Second outermost contour represents 500 microgramspermeter-cubed. 37 3.5 Number of prescribed burns and wild fires in Florida from 2006 to 2010. Data plottedinmonthlyintervals . .. 38 3.6 Total amount of wild fires from 2006 to 2010 for first six-months of year. Values represent wildfire events per 1000 square kilometers per county. Data from DOF wildfirecounts ................................... 39 viii 3.7 Total amount of wild fires from 2006 to 2010 for last six-months of year. Values represent wildfire events per 1000 square kilometers per county. Data from DOFwildfirecounts ................................ 40 3.8 Distance in miles to the closest fire at the time of a fog event ......... 41 3.9 Distribution of distances, in miles, from crash to the closest AWOS/ASOS station 41 3.10 Dewpoint depression, temperature and wind speed conditions present when AWOS and ASOS reports fog (left) and no fog (right). Amounts are tallied based on all METAR reports and are divided by total amount of eachevent . 57 3.11 Precipitation and cloud cover conditions present when AWOS and ASOS re- ports fog (left) and no fog (right). Amounts are tallied based on all METAR reports and are then divided by total amount of each event . ........ 58 3.12 Average cooling rates examined for fog and no fog events for all seasons and all stations. These events are defined as to whether fog did, or did not occur, between 11 and 12 UTC. Error bars represent a 99% confidence interval. 59 3.13 Average cooling rates examined for fog and no fog events during the cool season (i.e. November to March) only. These events are defined as to whether fog did, or did not occur, between 11 and 12 UTC. Error bars represent a 99% confidence interval........................................ 59 3.14 Conditions pre-existing fog and no-fog events occurring between 11 and 12- UTC. Top- Nocturnal dewpoint depression changes, Middle- Nocturnal wind speed changes, Bottom- Nocturnal cloud cover percentage changes. Error bars represent99%confidenceintervals . ... 60 3.15 Example atmospheric soundings iillustrating the theory of the cross over tem- perature. Top: Dewpoint (green) and temperature (black) profile at noon. Middle: Dewpoint (green) and temperature (black) profile at warmest part of day. Bottom: Dewpoint (green) and temperature (black) profile at the time fogoccurs....................................... 61 3.16 Average variations in temperature and dewpoint when fog did occur (right), and did not occur (left), for all stations, only for cool season. Error bars represent a99%confidenceinterval. 62 3.17 Skill scores for detecting fog at the time a fog event is reported using only wind speed and dewpoint depression. Percent Correct (PC), Probability of Detection (POD), False Alarm Rate (FAR), and Critical Success Index (CSI) are plotted. PC, POD, and FAR are plotted on primary Y-axis. CSI is plotted on secondary Y-axis ....................................... 63 ix 3.18 Nocturnal hourly cooling rates for temperature and dewpoint, averaged over all stations. Equations displayed are least-squares linear regression. Error bars representa99%confidenceinterval.. .... 63 3.19 Forecast temperature error for 12-UTC using a 10, 6, and 3-hour forecast. Results represent the percentage of time the forecasting method had a specific temperatureerror.................................. 64 3.20 Skill scores for forecasting fog, 3-hours in advance, when always forecasting fog belowaforecasteddewpointdepression . .... 64 3.21 Equation used in model to determine fog forecast. Top equation represents the conditions that must be met in order for fog to not be forecasted. Bottom equation represents conditions that must be met for fog to be forecasted . 65 x LIST OF ABBREVIATIONS AERMOD American Meteorological
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