5.5.3 Conditional quantile plots.

Forecast Verification; Contents; List of Contributors; Preface; Preface to the First Edition; 1 Introduction; 1.1 A brief history and current practice; 1.1.1 History; 1.1.2 Current practice; 1.2 Reasons for forecast verification and its benefits; 1.3 Types of forecast and verification data; 1.4 Scores, skill and value; 1.4.1 Skill scores; 1.4.2 Artificial skill; 1.4.3 Statistical significance; 1.4.4 Value added; 1.5 Data quality and other practical considerations; 1.6 Summary; 2 Basic concepts; 2.1 Introduction; 2.2 Types of predictand; 2.3 Exploratory methods.

2.4 Numerical descriptive measures2.5 Probability, random variables and expectations; 2.6 Joint, marginal and conditional distributions; 2.7 Accuracy, association and skill; 2.8 Properties of verification measures; 2.9 Verification as a regression problem; 2.10 The Murphy-Winkler framework; 2.11 Dimensionality of the verification problem; 3 Deterministic forecasts of binary events; 3.1 Introduction; 3.2 Theoretical considerations; 3.2.1 Some basic descriptive statistics; 3.2.2 A general framework for verification: the distributions-oriented approach.

3.2.3 Performance measures in terms of factorizations of the joint distribution3.2.4 Diagrams for visualizing performance measures; 3.2.5 Case study: verification of cloud-fraction forecasts; 3.3 Signal detection theory and the ROC; 3.3.1 The signal detection model; 3.3.2 The relative operating characteristic (ROC); 3.4 Metaverification: criteria for assessing performance measures; 3.4.1 Desirable properties; 3.4.2 Other properties; 3.5 Performance measures; 3.5.1 Overview of performance measures; 3.5.2 Sampling uncertainty and confidence intervals for performance measures.

3.5.3 Optimal threshold probabilitiesAcknowledgements; 4 Deterministic forecasts of multi-category events; 4.1 Introduction; 4.2 The contingency table: notation, definitions, and measures of accuracy; 4.2.1 Notation and definitions; 4.2.2 Measures of accuracy; 4.3 Skill scores; 4.3.1 Desirable attributes; 4.3.2 Gandin and Murphy equitable scores; 4.3.3 Gerrity equitable scores; 4.3.4 LEPSCAT; 4.3.5 SEEPS; 4.3.6 Summary remarks on scores; 4.4 Sampling variability of the contingency table and skill scores; 5 Deterministic forecasts of continuous variables; 5.1 Introduction.

5.2 Forecast examples5.3 First-order moments; 5.3.1 Bias; 5.3.2 Mean Absolute Error; 5.3.3 Bias correction and artificial skill; 5.3.4 Mean absolute error and skill; 5.4 Second- and higher-order moments; 5.4.1 Mean Squared Error; 5.4.2 MSE skill score; 5.4.3 MSE of scaled forecasts; 5.4.4 Correlation; 5.4.5 An example: testing the 'limit of predictability'; 5.4.6 Rank correlations; 5.4.7 Comparison of moments of the marginal distributions; 5.4.8 Graphical summaries; 5.5 Scores based on cumulative frequency; 5.5.1 Linear Error in Probability Space (LEPS); 5.5.2 Quantile-quantile plots.

Forecast Verification: A Practioner's Guide in Atmospheric Science, 2nd Edition provides an indispensible guide to this area of active research by combining depth of information with a range of topics to appeal both to professional practitioners and researchers and postgraduates. The editors have succeeded in presenting chapters by a variety of the leading experts in the field while still retaining a cohesive and highly accessible style. The book balances explanations of concepts with clear and useful discussion of the main application areas.

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