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Statistical Methods in the Atmospheric Sciences: An Introduction, Volume 59 [Hardback]

(Department of Earth and Atmospheric Sciences, Cornell University, USA)
  • Formāts: Hardback, 467 pages, height x width: 229x151 mm, weight: 680 g, references, tables, index
  • Sērija : International Geophysics
  • Izdošanas datums: 01-Mar-1995
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0127519653
  • ISBN-13: 9780127519654
Citas grāmatas par šo tēmu:
  • Formāts: Hardback, 467 pages, height x width: 229x151 mm, weight: 680 g, references, tables, index
  • Sērija : International Geophysics
  • Izdošanas datums: 01-Mar-1995
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0127519653
  • ISBN-13: 9780127519654
Citas grāmatas par šo tēmu:
This book introduces and explains the statistical methods used to describe, analyze, test, and forecast atmospheric data. It will be useful to students, scientists, and other professionals who seek to make sense of the scientific literature in meteorology, climatology, or other geophysical disciplines, or to understand and communicate what their atmospheric data sets have to say. The book includes chapters on exploratory data analysis, probability distributions, hypothesis testing, statistical weather forecasting, forecast verification, time(series analysis, and multivariate data analysis. Worked examples, exercises, and illustrations facilitate understanding of the material; an extensive and up-to-date list of references allows the reader to pursue selected topics in greater depth.

Key Features
* Presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting
* Includes extensive and up-to-date references
* Features numerous worked examples and exercises
* Contains over 130 illustrations

Recenzijas

The book is a must-buy if you use statistics in your classes or research. It can serve as a text or a reference work.-JAMES J. OBRIEN, The Florida State UniversityI recommend this book, without hesitation, as either a reference or course text. I found the books range of topics, depth of treatment, and pedagogical style with relatively few exceptions appropriate for upper-division undergraduates and beginning graduate students...Wilks excellent book provides a thorough base in applied statistical methods for atmospheric scientists. I hope its availability will encourage university atmospheric programs to provide all of their degree candidates with a treatment of this important subject that is as broad and in-depth as that provided by Wilks at Cornell.--Robert E. Livezey, National Centers fo Environmental Prediction, in BULLETIN OF THE AMSIt combines a clear introduction of both fundamentals and advanced applications with many examples of how they can be used. It could be used as an effective teaching tool at both the undergraduate and graduate level. A book of this type was needed.-DENNIS L. HARTMANN, University of WashingtonWilks has produced such a 'suitable replacement and I recommend his book, without hesitation, as either a reference or course text....Wilks excellent book provides a thorough base in applied statistical methods for atmospheric scientists. I hope its availability will encourage university atmospheric science programs to provide all of their degree candidates with a treatment of this important subject that is as broad and in-depth as that provided by Wilks at Cornell.Robert E. Livezey, Climate Prediction Center of the National Oceanic and Atmospheric Administrations National Centers for Environmental Prediction, Camp Springs, Maryland, in BULLETIN OF AMERICAN METEOROLOGY SOCIETYThe presentation is very readable and the material accurately presented.TECHNOMETRICS

Papildus informācija

Key Features * Presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting * Includes extensive and up-to-date references * Features numerous worked examples and exercises * Contains over 130 illustrations
Preface xi
Introduction
What Is Statistics?
1(1)
Descriptive and Inferential Statistics
2(1)
Uncertainty about the Atmosphere
2(2)
An Aside on Notation
4(2)
Review of Probability
Background
6(1)
The Elements of Probability
7(2)
Events
7(1)
The Sample Space
7(1)
The Axioms of Probability
8(1)
The Meaning of Probability
9(1)
Frequency Interpretation
9(1)
Bayesian (Subjective) Interpretation
9(1)
Some Properties of Probability
10(11)
Domain, Subsets, Complements, and Unions
10(2)
Conditional Probability
12(2)
Independence
14(2)
Law of Total Probability
16(1)
Bayes' Theorem
17(2)
Exercises
19(2)
Empirical Distributions and Exploratory Data Analysis
Background
21(3)
Robustness and Resistance
21(1)
Quantiles
22(2)
Numerical Summary Measures
24(3)
Location
24(1)
Spread
25(1)
Symmetry
26(1)
Graphical Summary Techniques
27(9)
Stem-and-Leaf Display
27(2)
Boxplots
29(1)
Schematic Plots
30(3)
Other Boxplot Variants
33(1)
Histograms
33(1)
Cumulative Frequency Distributions
34(2)
Reexpression
36(8)
Power Transformations
37(4)
Standardized Anomalies
41(3)
Exploratory Techniques for Paired Data
44(10)
Scatterplots
44(1)
Pearson (``Ordinary'') Correlation
45(5)
Rank Correlation
50(1)
Serial Correlation
51(2)
Autocorrelation Function
53(1)
Exploratory Techniques for Higher-Dimensional Data
54(10)
The Correlation Matrix
55(2)
The Scatterplot Matrix
57(2)
Correlation Maps
59(3)
Exercises
62(2)
Theoretical Probability Distributions
Background
64(2)
What Is a Theoretical Distribution?
65(1)
Parameters versus Statistics
65(1)
Discrete versus Continuous Distributions
66(1)
Discrete Distributions
66(8)
Binomial Distribution
66(4)
Geometric Distribution
70(1)
Poisson Distribution
71(3)
Statistical Expectations
74(2)
Expected Value of a Random Variable
74(1)
Expected Value of a Function of a Random Variable
74(2)
Continuous Distributions
76(23)
Distribution Functions and Expected Values
76(3)
Gaussian Distribution
79(7)
Gamma Distribution
86(7)
Weibull Distribution
93(2)
Lognormal Distribution
95(1)
Beta Distribution
95(2)
Gumbel Distribution
97(2)
Multivariate Probability Distributions
99(5)
Bivariate Normal Distribution
99(3)
Multivariate Normal Distribution
102(2)
Qualitative Assessments of the Goodness of Fit
104(4)
Superposition of a Fitted Theoretical Distribution and Data Histogram
104(2)
Probability Plots
106(2)
Parameter Fitting Using Maximum Likelihood
108(6)
Exercises
111(3)
Hypothesis Testing
Background
114(7)
Parametric versus Nonparametric Tests
114(1)
The Sampling Distribution
115(1)
The Elements of Any Hypothesis Test
115(1)
Test Levels and p Values
116(1)
Error Types and the Power of a Test
116(1)
One-Sided versus Two-Sided Tests
117(1)
Confidence Intervals: Inverting Hypothesis Tests
118(3)
Some Parametric Tests
121(16)
One-Sample t Test
121(1)
Tests for Differences of Mean under Independence
122(3)
Tests for Differences of Mean under Serial Dependence
125(4)
Goodness-of-Fit Tests
129(6)
Likelihood Ratio Test
135(2)
Nonparametric Tests
137(14)
``Classical'' Nonparametric Tests for Location
138(7)
Resampling Tests
145(6)
Field Significance and Multiplicity
151(8)
The Multiplicity Problem for Independent Tests
151(1)
Field Significance Given Spatial Correlation
152(5)
Exercises
157(2)
Statistical Weather Forecasting
Background
159(1)
Review of Least-Squares Regression
160(21)
Simple Linear Regression
160(3)
Distribution of the Residuals
163(2)
The Analysis-of-Variance Table
165(1)
Goodness-of-Fit Measures
166(2)
Sampling Distributions of the Regression Coefficients
168(3)
Examining Residuals
171(4)
Prediction Intervals
175(6)
Objective Forecasts---Without NWP
181(18)
Stratification and Compositing
182(1)
When the Predictand Is a Probability
182(3)
Predictor Selection
185(3)
Screening Regression
188(1)
Stopping Rules
189(5)
Cross-Validation
194(4)
Analog Forecasting
198(1)
Objective Forecasts---With NWP
199(11)
Perfect Prog Forecasts
200(1)
Model Output Statistics (MOS) Forecasts
201(1)
Comparisons between the Classical, Perfect Prog, and MOS Approaches
202(7)
Operational MOS Forecasts
209(1)
Probabilistic Field (Ensemble) Forecasts
210(11)
Stochastic Dynamic Forecasts
211(2)
Ensemble Forecasting
213(2)
Choosing Initial Ensemble Members
215(1)
Ensemble Averaging
216(1)
Dispersion of the Ensemble
217(4)
Subjective Probability Forecasts
221(12)
The Subjective Distribution
223(2)
Credible Interval Forecasts
225(2)
Assessing Discrete Probabilities
227(2)
Assessing Continuous Distributions
229(1)
Exercises
230(3)
Forecast Verification
Background
233(5)
Purposes of Forecast Verification
233(1)
Factorization of the Joint Distribution of Forecasts and Observations
234(1)
Scalar Attributes of Forecast Performance
235(2)
Forecast Skill
237(1)
Categorical Forecasts of Discrete Predictands
238(12)
The Contingency Table
238(1)
Accuracy Measures for Binary Forecasts
239(2)
Bias
241(1)
Accuracy Measures for Multicategory Forecasts
242(2)
Conversion of Probabilistic to Categorical Forecasts
244(4)
Skill Measures for Contingency Tables
248(2)
Categorical Forecasts of Continuous Predictands
250(8)
Conditional Quantile Plots
251(1)
Scalar Accuracy Measures
251(4)
Skill Scores
255(2)
Skill in Probability Space
257(1)
Probability Forecasts
258(14)
Dichotomous Events
259(1)
Brier Score
259(1)
Algebraic Decomposition of the Brier Score
260(3)
The Attributes Diagram
263(2)
The Reliability Diagram
265(2)
Hedging and Strictly Proper Scoring Rules
267(1)
Probability Forecasts for Multicategory Events
268(1)
Ranked Probability Score
269(3)
Categorical Forecasts of Fields
272(12)
S1 Score
274(1)
Mean-Squared Error
275(2)
Anomaly Correlation
277(4)
Exercises
281(3)
Time Series
Background
284(3)
Stationarity
284(1)
Time-Series Models
285(1)
Time-Domain versus Frequency-Domain Approaches
286(1)
Time Domain. I. Discrete Data
287(15)
Markov Chains
287(1)
Two-State, First-Order Markov Chains
288(4)
Test for Independence versus First-Order Serial Dependence
292(3)
Some Applications of Two-State Markov Chains
295(2)
Multistate Markov Chains
297(1)
Higher-Order Markov Chains
298(2)
Deciding among Alternative Orders of Markov Chains
300(2)
Time Domain. II. Continuous Data
302(23)
First-Order Autoregression
302(6)
Higher-Order Autoregression
308(5)
Order Selection Criteria
313(2)
The Variance of a Time Average
315(3)
Autoregressive Moving-Average Models
318(1)
Simulation and Forecasting with Time-Domain Models
319(4)
Multivariate Autoregressions
323(2)
Frequency Domain. I. Harmonic Analysis
325(16)
Cosine and Sine Functions
325(1)
Representing a Simple Time Series with a Harmonic Function
326(4)
Estimation of the Amplitude and Phase of a Single Harmonic
330(3)
Higher Harmonics
333(3)
Harmonic Dials
336(3)
The Harmonic Functions as Uncorrelated Regression Predictors
339(2)
Frequency Domain. II. Spectral Analysis
341(18)
The Periodogram or Fourier Line Spectrum
341(5)
Computing Spectra
346(1)
Aliasing
347(3)
Sampling Properties of Spectral Estimates
350(2)
Theoretical Spectra of Autoregressive Models
352(5)
Exercises
357(2)
Methods for Multivariate Data
Background
359(1)
Matrix Algebra Notation
360(13)
Vectors
360(2)
Matrices
362(11)
Principal-Component (EOF) Analysis
373(25)
Basics of PCA
374(4)
Truncation of the Principal Components
378(2)
How Many Principal Components Should Be Retained?
380(3)
PCA Based on the Covariance Matrix versus the Correlation Matrix
383(3)
Application of PCA to Fields
386(7)
Rotation of the Eigenvectors
393(1)
The Varied Terminology of PCA
394(3)
Scaling Conventions in PCA
397(1)
Canonical Correlation Analysis
398(10)
Canonical Variates
399(1)
Computation of CCA
400(3)
CCA Applied to Fields
403(1)
Combining PCA and CCA
403(5)
Discriminant Analysis
408(11)
Linear Discriminant Analysis
409(6)
Multiple Discriminant Analysis
415(4)
Cluster Analysis
419(10)
Distance Measures and Clustering Methods
420(3)
The Dendrogram, or Tree Diagram
423(1)
How Many Clusters?
424(3)
Exercises
427(2)
Appendix A: Example Data Sets 429(3)
Appendix B: Probability Tables 432(7)
Appendix C: Answers to Exercises 439(5)
References 444(11)
Index 455(10)
International Geophysics Series 465


Daniel S. Wilks has been a member of the Atmospheric Sciences faculty at Cornell University since 1987, and is the author of Statistical Methods in the Atmospheric Sciences (2011, Academic Press), which is in its third edition and has been continuously in print since 1995. Research areas include statistical forecasting, forecast postprocessing, and forecast evaluation.