Hurricane Climatology explains how to analyze and model hurricane data to better understand and predict present and future hurricane activityHinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
James B. Elsner is the Earl and Sophia Shaw Professor of Geography at Florida State University where he teaches applied spatial statistics and hurricane climatology. Dr. Elsner is President and CEO of Climatek; a company that develops software for hurricane-risk models. His research interests include the changing nature of hurricane risk. He has written more than 110 research articles and two books. Dr. Thomas Herbert Jagger is Vice President of Climatek and independent researcher. He has written more than 25 papers advancing the use of statistical analysis in weather and climate. As Vice president of Climatek he has incorporated Bayesian analysis and extreme value modeling into his research on hurricane climatology and hurricane risk analysis.
Inhaltsangabe
I Software, Statistics, and Data 1 Hurricanes, Climate, and Statistics 1.1 Hurricanes 1.2 Climate 1.3 Statistics 1.4 R 1.5 Organization 2 R Tutorial 2.1 Introduction 2.2 Data 2.2.1 Small amounts 2.2.2 Functions 2.2.3 Vectors 2.2.4 Structured data 2.2.5 Logic 2.2.6 Imports 2.3 Tables and Plots 3 Classical Statistics 3.1 Descriptive Statistics 3.2 Probability and Distributions 3.3 One-Sample Tests 3.4 Wilcoxon Signed-Rank Test 3.5 Two-Sample Tests 3.6 Statistical Formula 3.7 Compare Variances 3.8 Two-Sample Wilcoxon Test 3.9 Correlation 3.10 Linear Regression 3.11 Multiple Linear Regression 4 Bayesian Statistics 4.1 Learning About the Proportion of Landfalls 4.2 Inference 4.3 Credible Interval 4.4 Predictive Density 4.5 Is Bayes Rule Needed? 4.6 Bayesian Computation 5 Graphs and Maps 5.1 Graphs 5.2 Time series 5.3 Maps 5.4 Coordinate Reference Systems 5.5 Export 5.6 Other Graphic Packages 6 Data Sets 6.1 Best-Tracks 6.2 Annual Aggregation 6.3 Coastal County Winds 6.4 NetCDF Files II Models and Methods 7 Frequency Models 7.1 Counts 7.2 Environmental Variables 7.3 Bivariate Relationships 7.4 Poisson Regression 7.5 Model Predictions 7.6 Forecast Skill 7.7 Nonlinear Regression Structure 7.8 Zero-Inflated Count Model 7.9 Machine Learning 7.10 Logistic Regression 8 Intensity Models 211 8.1 Lifetime Highest Intensity 8.2 Fastest Hurricane Winds 8.3 Categorical Wind Speeds by County 9 Spatial Models 9.1 Track Hexagons 9.2 SST Data 9.3 SST and Intensity 9.4 Spatial Autocorrelation 9.5 Spatial Regression Models 9.6 Spatial Interpolation 10 Time Series Models 10.1 Time Series Overlays 10.2 Discrete Time Series 10.3 Change Points 10.4 Continuous Time Series 10.5 Time Series Network 11 Cluster Models 11.1 Time Clusters 11.2 Spatial Clusters 11.3 Feature Clusters 12 Bayesian Models 12.1 Long-Range Outlook 12.2 Seasonal Model 12.3 Consensus Model 12.4 Space-Time Model 13 Impact Models 13.1 Extreme Losses 13.2 Future Wind Damage A Functions, Packages, and Data A.1 Functions A.2 Packages A.3 Data Sets B Install Package From Source
