Hurricanes are nature's most destructive storms and they are becoming more powerful as the globe warms. Hurricane Climatology explains how to analyze and model hurricane data to better understand and predict present and future hurricane activity. It uses the open-source and now widely used R software for statistical computing to create a tutorial-style manual for independent study, review, and reference. The text is written around the code that when copied will reproduce the graphs, tables, and maps. The approach is different from other books that use R. It focuses on a single topic and explains how to make use of R to better understand the topic. The book is organized into two parts, the first of which provides material on software, statistics, and data. The second part presents methods and models used in hurricane climate research.
Author(s): James B. Elsner, Thomas Herbert Jagger
Series: 1st Edition missing chapter
Publisher: Oxford University Press
Year: 2013
Language: English
Pages: 493
Tags: R software, Hurricane, Climatology
Cover......Page 1
Preface......Page 4
Contents......Page 6
List of Figures......Page 14
List of Tables......Page 19
I Software, Statistics, and Data......Page 21
1.1 Hurricanes......Page 22
1.2 Climate......Page 25
1.3 Statistics......Page 26
1.4 R......Page 29
1.5 Organization......Page 30
2 R Tutorial......Page 33
2.1.1 What is R?......Page 34
2.1.2 Get R......Page 35
2.1.3 Packages......Page 36
2.1.4 Calculator......Page 37
2.1.5 Functions......Page 38
2.1.7 Assignments......Page 39
2.1.8 Help......Page 40
2.2.1 Small Amounts......Page 41
2.2.2 Functions......Page 42
2.2.3 Vectors......Page 44
2.2.4 Structured Data......Page 48
2.2.5 Logic......Page 49
2.2.6 Imports......Page 51
2.3.1 Tables and Summaries......Page 55
2.3.2 Quantiles......Page 57
2.3.3 Plots......Page 58
12.1.2 Conditional losses......Page 0
Scatter Plots......Page 60
2.4 R functions used in this chapter......Page 63
3.1 Descriptive Statistics......Page 65
3.1.1 Mean, median, and maximum......Page 66
3.1.2 Quantiles......Page 69
3.1.3 Missing values......Page 70
3.2.1 Random samples......Page 71
3.2.2 Combinatorics......Page 73
3.2.3 Discrete distributions......Page 74
3.2.4 Continuous distributions......Page 76
3.2.6 Densities......Page 78
3.2.7 Cumulative distribution functions......Page 80
3.2.8 Quantile functions......Page 82
3.2.9 Random numbers......Page 83
3.3 One-Sample Tests......Page 85
3.4 Wilcoxon Signed-Rank Test......Page 92
3.5 Two-Sample Tests......Page 94
3.6 Statistical Formula......Page 97
3.8 Two-Sample Wilcoxon Test......Page 100
3.9 Correlation......Page 101
3.9.2 Spearman's rank and Kendall's correlation......Page 105
3.9.3 Bootstrap confidence intervals......Page 106
3.10 Linear Regression......Page 108
3.11 Multiple Linear Regression......Page 117
3.11.1 Predictor choice......Page 122
3.11.2 Cross validation......Page 123
4.1 Learning About the Proportion of Landfalls......Page 125
4.3 Credible Interval......Page 132
4.4 Predictive Density......Page 134
4.5 Is Bayes Rule Needed?......Page 137
4.6 Bayesian Computation......Page 138
4.6.1 Time-to-Acceptance......Page 139
4.6.3 JAGS......Page 146
4.6.4 WinBUGS......Page 151
5 Graphs and Maps......Page 157
5.1.1 Box plot......Page 158
5.1.2 Histogram......Page 160
5.1.3 Density plot......Page 163
5.1.5 Scatter plot......Page 168
5.1.6 Conditional scatter plot......Page 171
5.2.1 Time-series graph......Page 173
5.2.3 Dates and times......Page 177
5.3.1 Boundaries......Page 179
Point data......Page 183
Field data......Page 192
5.4 Coordinate Reference Systems......Page 195
5.6.1 lattice......Page 201
5.6.2 ggplot2......Page 202
6.1 Best-Tracks......Page 208
6.1.1 Description......Page 209
6.1.2 Import......Page 211
6.1.3 Intensification......Page 214
6.1.4 Interpolation......Page 215
6.1.5 Regional activity......Page 218
6.1.7 Regional maximum intensity......Page 220
6.1.8 Tracks by location......Page 222
6.2.1 Annual cyclone counts......Page 227
6.2.2 Environmental variables......Page 228
6.3.1 Description......Page 236
6.3.2 Counts and magnitudes......Page 238
6.4 NetCDF Files......Page 240
II Models and Methods......Page 244
7.1 Counts......Page 245
7.1.2 Inhomogeneous Poisson process......Page 249
7.2 Environmental Variables......Page 252
7.3 Bivariate Relationships......Page 253
7.4.1 Limitation of linear regression......Page 255
7.4.3 Method of maximum likelihood......Page 256
7.4.4 Model fit......Page 258
7.4.5 Interpretation......Page 259
7.5 Model Predictions......Page 261
7.6.1 Metrics......Page 263
7.6.2 Cross validation......Page 264
7.7 Nonlinear Regression Structure......Page 266
7.8 Zero-Inflated Count Model......Page 269
7.9 Machine Learning......Page 273
7.10 Logistic Regression......Page 276
7.10.1 Exploratory analysis......Page 278
7.10.3 Fit and interpretation......Page 281
7.10.4 Prediction......Page 283
7.10.5 Fit and adequacy......Page 285
7.10.6 Receiver operating characteristics......Page 287
8.1 Lifetime Highest Intensity......Page 291
8.1.1 Exploratory analysis......Page 292
8.2.1 Exploratory analysis......Page 306
8.2.3 Extreme value theory......Page 309
8.2.6 Intensity and frequency model......Page 315
8.2.7 Confidence intervals......Page 316
8.2.8 Threshold intensity......Page 318
8.3.1 Marked Poisson process......Page 321
8.3.2 Return levels......Page 322
8.3.3 Covariates......Page 324
8.3.4 Miami-Dade......Page 326
9 Time Series Models......Page 329
9.1 Time Series Overlays......Page 330
9.2.1 Count variability......Page 332
9.2.2 Moving average......Page 334
9.2.3 Seasonality......Page 335
9.3.1 Counts......Page 340
9.3.2 Covariates......Page 343
9.4 Continuous Time Series......Page 345
9.5.1 Time series visibility......Page 351
9.5.2 Network plot......Page 353
10.1 Time Clusters......Page 361
10.1.1 Cluster detection......Page 362
10.1.2 Conditional counts......Page 364
10.1.3 Cluster model......Page 366
10.1.4 Parameter estimation......Page 367
10.1.5 Model diagnostics......Page 368
10.1.6 Forecasts......Page 372
10.2 Spatial Clusters......Page 374
10.2.2 Spatial density......Page 379
10.3 Feature Clusters......Page 385
10.3.1 Dissimilarity and distance......Page 386
10.3.2 K-means clustering......Page 389
10.3.3 Track clusters......Page 391
10.3.4 Track plots......Page 394
11.1.1 Poisson-gamma conjugate......Page 398
11.1.2 Prior parameters......Page 400
11.1.3 Posterior density......Page 401
11.3.1 Bayesian model averaging......Page 411
11.3.3 Model selection......Page 415
11.3.4 Consensus hindcasts......Page 423
11.4 Space-Time Model......Page 425
11.4.1 Lattice data......Page 426
11.4.2 Local independent regressions......Page 433
11.4.3 Spatial autocorrelation......Page 438
11.4.4 BUGS data......Page 439
11.4.5 MCMC output......Page 440
11.4.6 Convergence and mixing......Page 443
11.4.7 Updates......Page 448
12.1 Extreme Losses......Page 452
12.1.1 Exploratory analysis......Page 453
12.1.3 Industry loss models......Page 456
12.2.1 Historical catalogue......Page 457
12.2.2 Gulf of Mexico hurricanes and SST......Page 462
12.2.3 Intensity changes with SST......Page 463
12.2.4 Stronger hurricanes......Page 466
A.1 Functions......Page 468
A.2 Packages......Page 479
A.3 Data Sets......Page 480
B Install Package From Source......Page 482
References......Page 484
