Probability, Statistics, And Decision Making In The Atmospheric Sciences

by Allan Murphy, Richard W. Katz

Overview

Methodology drawn from the fields of probability. statistics and decision making plays an increasingly important role in the atmosphericsciences. both in basic and applied research and in experimental and operational studies. Applications of such methodology can be found in almost every facet of the discipline. from the most theoretical and global (e.g., atmospheric predictability. global climate modeling) to the most practical and local (e.g., crop-weather modeling forecast evaluation). Almost every issue of the multitude of journals published by the atmospheric sciences community now contain some or more papers involving applications of concepts and/or methodology from the fields of probability and statistics. Despite the increasingly pervasive nature of such applications. very few book length treatments of probabilistic and statistical topics of particular interest to atmospheric scientists have appeared (especially inEnglish) since the publication of the pioneering works of Brooks andCarruthers (Handbook of Statistical Methods in Meteorology) in 1953 and Panofsky and Brier-(some Applications of)statistics to Meteor) in 1958. As a result. many relatively recent developments in probability and statistics are not well known to atmospheric scientists and recent work in active areas of meteorological research involving significant applications of probabilistic and statistical methods are not familiar to the meteorological community as a whole.

Product Details

ISBN-13:	9780367284336
Publisher:	CRC Press
Publication date:	10/02/2019
Pages:	560
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Allan H. Murphy and Richard W. Katz

Table of Contents

Preface — 1. EXPLORATORY ANALYSIS OF ATMOSPHERIC DATA Thomas E. Graedel and Beat Kleiner — 1. Introduction — 2. Summarization and Exposure of Sets of One- Dimensional Data — 3. Analyzing Two Sets of Data — 4. Comparing Several Sets of Data — 5. Summary and Conclusions — 2. DEVELOPING EMPIRICAL MODELS WITH MULTIPLE REGRESSION: BIASED ESTIMATION TECHNIQUES Donald W. Marquardt and Ronald D. Snee — 1. Introduction — 2. Theory and Illustrative Examples — 3. Use of Biased Estimation in Data Analysis. — 3. EXPLORATORY MULTIVARIATE ANALYSIS OF A SINGLE BATCH OF DATA /K. Ruben Gabriel — 1. Introduction — 2. One Batch of Multivariate Data and Their Descriptive Statistics — 3. The Geometry and Display of a Batch of Multivariate Data — 4. Data Analysis of the Variables' Configuration. — 5. Analyzing the Scatter of the Units — 6. Joint Analysis of Variables and Units — Modeling. — 7. Other Literature — 4. MULTIVARIATE COMPARISONS OF DATA FROM SEVERAL BATCHES /K. Ruben Gabriel — Comparing Several Batches of Observations On Tests of Significance — 5. TIME SERIES ANALYSIS — FREQUENCY DOMAIN Richard H. Jones — 1. Introduction — 2. Terminology — 3. Periodic Mean Functions — 4. Estimating the Spectral Density — 5. Estimating via the Covariance Function. — 6. Estimating the Covariance Function via Two Passes of the FFT — 7. The Effects of Filtering. — 8. Testing Hypotheses — 9. Multivariate Time Series — 6. TIME SERIES ANALYSIS — TIME DOMAIN Richard H. Jones. — 1. Introduction — 2. First-Order Autoregression — 3. Higher-Order Autoregressions — 4. Order Selection — 5. Autoregressive-Moving Average Models. — 6. Hypothesis Tests — 7. Multivariate Time Series — 8. State Space Recursive Estimation — 7. PROBABILISTIC MODELS, Richard W. Katz. — 1. Introduction — 2. Examples — 3. Probability Theory — 4. Statistical Inference — 8. STATISTICAL WEATHER FORECASTING, Harry R. Glahn. — 1. Introduction — 2. Methods of Application. — 3. Histograms — 4. Scatter Diagrams — 5. Regression — 6. Discriminant Analysis — 7. Canonical Correlation — 8. Logit Model — 9. Map Typing — 10. Analogues — 11. Present Status — 12. Future of Statistical Weather Forecasting — 9. PROBABILISTIC WEATHER FORECASTING Allan H. Murphy — 1. Introduction — 2. Probability Forecasts: Definition, Interpretation, and Motivation — 3. Objective Probability Forecasts Subjective Probability Forecasts Communication of Uncertainty in Weather Forecasts — 10 FORECAST EVALUATION Allan H. Murphy and Harald Daan. — 1. Introduction — 2. Nature and Purposes of Evaluation — 3. Predictands, Forecasts, and Attributes — 4. Some Desirable Properties of Evaluation Measures — 5. Some Inferential Measures for Categorical Forecasts — 6. Some Inferential Measures for Probability Forecasts — 7. Some Related Topics — 11 DESIGN AND EVALUATION OF WEATHER MODIFICATION EXPERIMENTS Paul W. Mielke, Jr. — 1. Introduction — 2. Experimental Designs — 3. Evaluation Procedures — 12 BAYESIAN INFERENCE Robert L. Winkler — 1. Introduction — 2. Bayes' Theorem — 3. Bayesian Inference with Discrete Prior Distributions — 4. Bayesian Inference with Continuous Prior Distributions — 5. Assessment of Likelihood Functions and Prior Distributions. — 6. Estimation, Hypothesis Testing, Prediction, and Decision Making — 7. Bayesian Inference in Meteorology. — 13 DECISION ANALYSIS Robert L. Winkler and Allan H. Murphy. — 1. Introduction — 2. Elements of Decision Analysis. — 3. Decision Criteria — 4. Assessment of Probabilities. — 5. Assessment of Utilities — 6. Value of Information — 7. Sequential Decisions — 8. Sensitivity Analysis — 9. Some Applications of Decision Analysis in Meteorology — About the Contributors — Index — Other Titles of Interest from Westview Press — About the Book and Editors.