Karen Blouin

University of Alberta Lightning prediction models for the province of Alberta, Canada by Karen Blouin A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of requirements for degree of Master of Science in Forest Biology and Management Department of Renewable Resources © Karen Blouin Spring 2014 Edmonton, Alberta Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission. ABSTRACT Lightning is widely acknowledged as a major cause of wildland fires in Canada. On average, 250,000 cloud-to-ground lightning strikes occur in Alberta every year. Lightning-caused wildland fires in remote areas have considerably larger suppression costs and a much greater chance of escaping initial attack. Geographic and temporal covariates were paired with Reanalysis and Radiosonde observations to generate a series of 6-hour and 24-hour lightning prediction models valid from April to October. These models, based on cloud-to-ground lightning from the CLDN, were developed and validated for the province of Alberta, Canada. The ensemble forecasts produced from these models were most accurate in the Rocky Mountain and Foothills Natural Regions achieving hits rates of ~85%. The Showalter index, convective available potential energy, Julian day, and geographic covariates were highly important predictors. Random forest classification is introduced as a viable modelling method to generate lightning forecasts. Limitations and recommendations are also discussed. ACKNOWLEDGEMENTS I am sincerely grateful to my Supervisor, Dr. Mike Flannigan. His continual support, encouragement, and genuine concern for all of his students did not go unnoticed. I appreciate the input and recommendation made by my committee members, Bob Kochtubajda, Dr. Scott Nielsen (chair), Dr. Gerhard Reuter, and Dr. Xianli Wang. I am extremely thankful for the numerous hours of programming support provided by Dr. Xianli Wang of the University of Alberta. Without his expertise this could not have been accomplished. I am also thankful to the Western Partnership for Wildland Fire Science and the University of Alberta for providing the opportunity for this research to take place. A big thank you to the Fire Lab Group for being a sounding board and continually providing new and interesting perspectives. I am very thankful for my family and their never ending support and love (even the tough love). Finally, I thank my husband Jerald for being my rock and always encouraging me to chase my dreams. The funding for this study was generously provided by Alberta Environment and Sustainable Resource Development. Lightning data were provided by Bob Kochtubajda of Environment Canada, and Radiosonde data were provided by Dr. Larry Oolman of the University of Wyoming. CONTENTS ABSTRACT ACKNOWLEDGEMENTS 3 LIST OF FIGURES LIST OF ACRONYMS CHAPTER 1. INTRODUCTION 1 1.1 PREAMBLE 1 1.2 LIGHTNING : BACKGROUND 2 1.3 CONVECTIVE BASICS 4 1.4 CLOUD ELECTRIFICATION 8 1.5 LIGHTNING 10 1.6 FIRE 14 1.7 LIGHTNING-FIRE INTERACTIONS 19 1.8 PREVIOUS WORKS 22 1.9 OBJECTIVES 25 CHAPTER 2. DATA AND METHODS 27 2.1 STUDY AREA 27 2.2 DATA 28 2.2.1 PREDICTAND 31 2.2.2 PREDICTORS 32 Geographic and Temporal Covariates 32 Reanalysis ІІ : Pressure Levels 32 Reanalysis ІІ : Surface Grid 33 Reanalysis І : Pressure Levels 34 Radiosonde Observations 34 Calculated Variables 38 2.3 DATA PROCESSING AND MODIFICATIONS 40 2.3.1 LIGHTNING DATA PROCESSING 42 2.3.2 REANALYSIS DATA PROCESSING 43 2.3.3 RADIOSONDE DATA PROCESSING 44 2.3.4 THIN-PLATE SPLINE 45 2.4 MODELLING METHODS 48 2.4.1 RANDOM FOREST: BACKGROUND 48 2.4.2 CONUNDRUM OF IMBALANCED DATA 49 2.4.3 RANDOM FOREST : MODELLING 51 Preliminary Runs 51 Creating Random Forest Models 53 CHAPTER 3. RESULTS 56 3.1 EXPLORATORY RUNS 56 3.2 DETERMINING TOP PREDICTORS 60 3.3 LIGHTNING PREDICTION MODELS 64 3.3.1 2.5° LATITUDE by 2.5° LONGITUDE 67 Daily Lightning Prediction 67 6-hour Lightning Prediction 68 3.3.2 1.25° LATITUDE by 2.5° LONGITUDE 70 Daily Lightning Prediction 71 6-hour Lightning Prediction 72 3.3.3 50KM BY 50KM 74 Boreal Forest : Daily Lightning Prediction 74 Boreal Forest : 6-hour Lightning Prediction 76 Parkland and Grassland : Daily Lightning Prediction 79 Parkland and Grassland : 6-hour Lightning Prediction 82 Mountain and Foothills : Daily Lightning Prediction 84 Mountain and Foothills : 6-hour Lightning Prediction 85 CHAPTER 4. DISCUSSION 91 4.1 TOP MODEL SELECTION 91 4.2 PREDICTOR IMPORTANCE AND CONTRIBUTION 93 4.2.1 PARTIAL DEPENDENCE PLOTS 93 4.3 RANDOM FOREST MODELLING 98 4.3.1 BENEFITS 99 4.3.2 DATA IMBALANCE 99 4.3.3 OOB ERROR ESTIMATE VS. INDEPENDENT PREDICTIONS 100 4.3.4 EFFECTS OF CORRELATION 101 4.3.5 NUMBER OF TREES 101 4.3.6 DISADVANTAGES 102 4.4 POSSIBLE SOURCES OF ERROR 102 4.4.1 THIN-PLATE SPLINE 103 4.4.2 LIGHTNING MISCLASSIFICATION 103 4.5 LOOKING FORWARD 104 CHAPTER 5. CONCLUSIONS 106 BIBLIOGRAPHY 109 LIST OF TABLES Table 1: List of input variables. Abbreviations, full names, units of measure, and some additional information about data sources and resolution are given for all of the predictors initially considered. __________________________________________________________________ 30 Table 2: List of the 10 data sets created for the two time frames (daily and 6-hour) and three spatial scales. The finest resolution scale (50km by 50km) is further subdivided into three separate zones. The number of observations remaining after all rows with NAs present were removed are also provided for all data sets. These 10 data sets are the basis for the lightning prediction models. _____________________________________________________________________ 47 Table 3: Contingency matrix for model forecasts. The A and D cells (highlighted in grey) show the correctly forecasted events and non-events, respectively. ______________________________ 55 Table 4: List of the forecast skill criterion used to analyses the lightning prediction models. The inputs for the formulas can be found in the contingency matrix shown in Table 3. ___________ 55 Table 5: Level of imbalance between the positive (events) and negative class (non-events) for each data set. The number of observations are for the training sets, however, the proportions hold true for both the training and validation sets. ___________________________________________ 58 Table 6: Variable importance for a 1:1 BRF model with ntree=300 generated for the 6-hour Boreal Forest data set. The higher the MeanDecreaseGini value, the greater the variable importance and thus the rank. ___________________________________________________ 62 Table 7: List of top 15 predictors for each data set. Based on models built with a Balanced Random Forest (BRF) approach. The various data sets are listed. _______________________ 63 Table 8: Six predictions models were generated with the top 15 and top 12 variables from each data set. Let n be the number of observations in the minority class. The second and third columns show how the sampsize arguments for each model were specified to change the number of observations sampled from each class with replacement. The unbalanced model was run under default conditions making the imbalance similar to the original data imbalance outlined in Table 5. __________________________________________________________________________ 64 Table 9: Optimum daily ensemble models (0.6:1) produced with 15 variables for the 2.5° latitude by 2.5° longitude scale. The 15 variable balanced models are included for comparison. The three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. As H increases, the FAR and F increase, and the PAG and PC decrease. _________________________________ 68 Table 10: Comparison of the two top ensemble models (0.6:1) produced with the top 12 and 15 variables for the 2.5° latitude by 2.5° longitude 6-hour forecast. The ensemble variations for the three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. _________ 69 Table 11: Comparison of the top two daily ensembles lightning forecast models for the 1.25° latitude by 2.5° longitude scale. The three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. _________________________________________________________ 72 Table 12: Forecast skill comparison for the top two 6-hour ensembles lightning forecast models for the 1.25° latitude by 2.5° longitude scale. The three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. ______________________________________________ 73 Table 13: Ensemble forecast skill comparison of the optimum forecast model (five variable 0.6:1) generated for daily lightning prediction in the Boreal Forest. The optimum model is compared to a model variation often selected as a top forecast model for the coarser spatial scales (12 variable 0.6:1). The three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. 76 Table 14: Ensemble forecast skill comparison of the 0.6:1 BRF models generated for 6-hour lightning prediction in the Boreal Forest. Ensemble models created from the top 12, 10, eight and five variables are included. The three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. _________________________________________________________ 79 Table 15: Comparison of the daily Parkland and Grassland ensemble forecasts generated from the 0.6:1 BRF models for the top 12, 10, eight and five variables.

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