{"product_id":"forecast-verification-a-practioners-guide-inatmospheric-science-2e-9780470660713","title":"Forecast Verification  A Practioners Guide inAtmospheric Science 2e","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eForecast Verification: A Practioner''s Guide in Atmospheric Science, 2nd Edition\u003c\/b\u003e 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. \u003cp\u003eReviews of first edition:\u003c\/p\u003e \u003cp\u003eThis book will provide a good reference, and I recommend it especially for developers and evaluators of statistical forecast systems. (\u003ci\u003eBulletin of the American Meteorological Society\u003c\/i\u003e; April 2004)\u003c\/p\u003e \u003cp\u003e...a good mixture of theory and practical applications...well organized and clearly written... (\u003ci\u003eRoyal Statistical Society\u003c\/i\u003e, Vol.168, No.1, January 2005)\u003c\/p\u003e \u003cp\u003e\u003cb\u003eNEW to the second edition:\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eList of Contributors xi\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003ePreface to the First Edition xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eIan T. Jolliffe and David B. Stephenson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 A brief history and current practice 1\u003c\/p\u003e \u003cp\u003e1.1.1 History 1\u003c\/p\u003e \u003cp\u003e1.1.2 Current practice 2\u003c\/p\u003e \u003cp\u003e1.2 Reasons for forecast verification and its benefits 3\u003c\/p\u003e \u003cp\u003e1.3 Types of forecast and verification data 4\u003c\/p\u003e \u003cp\u003e1.4 Scores, skill and value 5\u003c\/p\u003e \u003cp\u003e1.4.1 Skill scores 6\u003c\/p\u003e \u003cp\u003e1.4.2 Artificial skill 6\u003c\/p\u003e \u003cp\u003e1.4.3 Statistical significance 7\u003c\/p\u003e \u003cp\u003e1.4.4 Value added 8\u003c\/p\u003e \u003cp\u003e1.5 Data quality and other practical considerations 8\u003c\/p\u003e \u003cp\u003e1.6 Summary 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Basic concepts 11\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJacqueline M. Potts\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 11\u003c\/p\u003e \u003cp\u003e2.2 Types of predictand 11\u003c\/p\u003e \u003cp\u003e2.3 Exploratory methods 12\u003c\/p\u003e \u003cp\u003e2.4 Numerical descriptive measures 15\u003c\/p\u003e \u003cp\u003e2.5 Probability, random variables and expectations 20\u003c\/p\u003e \u003cp\u003e2.6 Joint, marginal and conditional distributions 20\u003c\/p\u003e \u003cp\u003e2.7 Accuracy, association and skill 22\u003c\/p\u003e \u003cp\u003e2.8 Properties of verification measures 22\u003c\/p\u003e \u003cp\u003e2.9 Verification as a regression problem 23\u003c\/p\u003e \u003cp\u003e2.10 The Murphy–Winkler framework 25\u003c\/p\u003e \u003cp\u003e2.11 Dimensionality of the verification problem 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Deterministic forecasts of binary events 31\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRobin J. Hogan and Ian B. Mason\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 31\u003c\/p\u003e \u003cp\u003e3.2 Theoretical considerations 33\u003c\/p\u003e \u003cp\u003e3.2.1 Some basic descriptive statistics 33\u003c\/p\u003e \u003cp\u003e3.2.2 A general framework for verification: the distributions-oriented approach 34\u003c\/p\u003e \u003cp\u003e3.2.3 Performance measures in terms of factorizations of the joint distribution 37\u003c\/p\u003e \u003cp\u003e3.2.4 Diagrams for visualizing performance measures 38\u003c\/p\u003e \u003cp\u003e3.2.5 Case study: verification of cloud-fraction forecasts 41\u003c\/p\u003e \u003cp\u003e3.3 Signal detection theory and the ROC 42\u003c\/p\u003e \u003cp\u003e3.3.1 The signal detection model 43\u003c\/p\u003e \u003cp\u003e3.3.2 The relative operating characteristic (ROC) 44\u003c\/p\u003e \u003cp\u003e3.4 Metaverification: criteria for assessing performance measures 45\u003c\/p\u003e \u003cp\u003e3.4.1 Desirable properties 45\u003c\/p\u003e \u003cp\u003e3.4.2 Other properties 49\u003c\/p\u003e \u003cp\u003e3.5 Performance measures 50\u003c\/p\u003e \u003cp\u003e3.5.1 Overview of performance measures 51\u003c\/p\u003e \u003cp\u003e3.5.2 Sampling uncertainty and confidence intervals for performance measures 55\u003c\/p\u003e \u003cp\u003e3.5.3 Optimal threshold probabilities 57\u003c\/p\u003e \u003cp\u003eAcknowledgements 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Deterministic forecasts of multi-category events 61\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRobert E. Livezey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 61\u003c\/p\u003e \u003cp\u003e4.2 The contingency table: notation, definitions, and measures of accuracy 62\u003c\/p\u003e \u003cp\u003e4.2.1 Notation and definitions 62\u003c\/p\u003e \u003cp\u003e4.2.2 Measures of accuracy 64\u003c\/p\u003e \u003cp\u003e4.3 Skill scores 64\u003c\/p\u003e \u003cp\u003e4.3.1 Desirable attributes 65\u003c\/p\u003e \u003cp\u003e4.3.2 Gandin and Murphy equitable scores 66\u003c\/p\u003e \u003cp\u003e4.3.3 Gerrity equitable scores 69\u003c\/p\u003e \u003cp\u003e4.3.4 LEPSCAT 71\u003c\/p\u003e \u003cp\u003e4.3.5 SEEPS 72\u003c\/p\u003e \u003cp\u003e4.3.6 Summary remarks on scores 73\u003c\/p\u003e \u003cp\u003e4.4 Sampling variability of the contingency table and skill scores 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Deterministic forecasts of continuous variables 77\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMichel Déqué\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 77\u003c\/p\u003e \u003cp\u003e5.2 Forecast examples 77\u003c\/p\u003e \u003cp\u003e5.3 First-order moments 79\u003c\/p\u003e \u003cp\u003e5.3.1 Bias 79\u003c\/p\u003e \u003cp\u003e5.3.2 Mean Absolute Error 80\u003c\/p\u003e \u003cp\u003e5.3.3 Bias correction and artificial skill 81\u003c\/p\u003e \u003cp\u003e5.3.4 Mean absolute error and skill 81\u003c\/p\u003e \u003cp\u003e5.4 Second- and higher-order moments 82\u003c\/p\u003e \u003cp\u003e5.4.1 Mean Squared Error 82\u003c\/p\u003e \u003cp\u003e5.4.2 MSE skill score 82\u003c\/p\u003e \u003cp\u003e5.4.3 MSE of scaled forecasts 83\u003c\/p\u003e \u003cp\u003e5.4.4 Correlation 84\u003c\/p\u003e \u003cp\u003e5.4.5 An example: testing the ‘limit of predictability’ 86\u003c\/p\u003e \u003cp\u003e5.4.6 Rank correlations 87\u003c\/p\u003e \u003cp\u003e5.4.7 Comparison of moments of the marginal distributions 88\u003c\/p\u003e \u003cp\u003e5.4.8 Graphical summaries 90\u003c\/p\u003e \u003cp\u003e5.5 Scores based on cumulative frequency 91\u003c\/p\u003e \u003cp\u003e5.5.1 Linear Error in Probability Space (LEPS) 91\u003c\/p\u003e \u003cp\u003e5.5.2 Quantile-quantile plots 92\u003c\/p\u003e \u003cp\u003e5.5.3 Conditional quantile plots 92\u003c\/p\u003e \u003cp\u003e5.6 Summary and concluding remarks 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Forecasts of spatial fields 95\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBarbara G. Brown, Eric Gilleland and Elizabeth E. Ebert\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 95\u003c\/p\u003e \u003cp\u003e6.2 Matching methods 96\u003c\/p\u003e \u003cp\u003e6.3 Traditional verification methods 97\u003c\/p\u003e \u003cp\u003e6.3.1 Standard continuous and categorical approaches 97\u003c\/p\u003e \u003cp\u003e6.3.2 S1 and anomaly correlation 98\u003c\/p\u003e \u003cp\u003e6.3.3 Distributional methods 99\u003c\/p\u003e \u003cp\u003e6.4 Motivation for alternative approaches 100\u003c\/p\u003e \u003cp\u003e6.5 Neighbourhood methods 103\u003c\/p\u003e \u003cp\u003e6.5.1 Comparing neighbourhoods of forecasts and observations 104\u003c\/p\u003e \u003cp\u003e6.5.2 Comparing spatial forecasts with point observations 104\u003c\/p\u003e \u003cp\u003e6.6 Scale separation methods 105\u003c\/p\u003e \u003cp\u003e6.7 Feature-based methods 108\u003c\/p\u003e \u003cp\u003e6.7.1 Feature-matching techniques 108\u003c\/p\u003e \u003cp\u003e6.7.2 Structure-Amplitude-Location (SAL) technique 110\u003c\/p\u003e \u003cp\u003e6.8 Field deformation methods 111\u003c\/p\u003e \u003cp\u003e6.8.1 Location metrics 111\u003c\/p\u003e \u003cp\u003e6.8.2 Field deformation 112\u003c\/p\u003e \u003cp\u003e6.9 Comparison of approaches 113\u003c\/p\u003e \u003cp\u003e6.10 New approaches and applications: the future 114\u003c\/p\u003e \u003cp\u003e6.11 Summary 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Probability forecasts 119\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJochen Broecker\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 119\u003c\/p\u003e \u003cp\u003e7.2 Probability theory 120\u003c\/p\u003e \u003cp\u003e7.2.1 Basic concepts from probability theory 120\u003c\/p\u003e \u003cp\u003e7.2.2 Probability forecasts, reliability and sufficiency 121\u003c\/p\u003e \u003cp\u003e7.3 Probabilistic scoring rules 122\u003c\/p\u003e \u003cp\u003e7.3.1 Definition and properties of scoring rules 122\u003c\/p\u003e \u003cp\u003e7.3.2 Commonly used scoring rules 124\u003c\/p\u003e \u003cp\u003e7.3.3 Decomposition of scoring rules 125\u003c\/p\u003e \u003cp\u003e7.4 The relative operating characteristic (ROC) 126\u003c\/p\u003e \u003cp\u003e7.5 Evaluation of probabilistic forecasting systems from data 128\u003c\/p\u003e \u003cp\u003e7.5.1 Three examples 128\u003c\/p\u003e \u003cp\u003e7.5.2 The empirical ROC 130\u003c\/p\u003e \u003cp\u003e7.5.3 The empirical score as a measure of performance 130\u003c\/p\u003e \u003cp\u003e7.5.4 Decomposition of the empirical score 131\u003c\/p\u003e \u003cp\u003e7.5.5 Binning forecasts and the leave-one-out error 132\u003c\/p\u003e \u003cp\u003e7.6 Testing reliability 134\u003c\/p\u003e \u003cp\u003e7.6.1 Reliability analysis for forecast A: the reliability diagram 134\u003c\/p\u003e \u003cp\u003e7.6.2 Reliability analysis for forecast B: the chi-squared test 136\u003c\/p\u003e \u003cp\u003e7.6.3 Reliability analysis for forecast C: the PIT 138\u003c\/p\u003e \u003cp\u003eAcknowledgements 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Ensemble forecasts 141\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAndreas P. Weigel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 141\u003c\/p\u003e \u003cp\u003e8.2 Example data 142\u003c\/p\u003e \u003cp\u003e8.3 Ensembles interpreted as discrete samples 143\u003c\/p\u003e \u003cp\u003e8.3.1 Reliability of ensemble forecasts 144\u003c\/p\u003e \u003cp\u003e8.3.2 Multidimensional reliability 152\u003c\/p\u003e \u003cp\u003e8.3.3 Discrimination 157\u003c\/p\u003e \u003cp\u003e8.4 Ensembles interpreted as probabilistic forecasts 159\u003c\/p\u003e \u003cp\u003e8.4.1 Probabilistic interpretation of ensembles 159\u003c\/p\u003e \u003cp\u003e8.4.2 Probabilistic skill metrics applied to ensembles 160\u003c\/p\u003e \u003cp\u003e8.4.3 Effect of ensemble size on skill 163\u003c\/p\u003e \u003cp\u003e8.5 Summary 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Economic value and skill 167\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDavid S. Richardson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 167\u003c\/p\u003e \u003cp\u003e9.2 The cost\/loss ratio decision model 168\u003c\/p\u003e \u003cp\u003e9.2.1 Value of a deterministic binary forecast system 169\u003c\/p\u003e \u003cp\u003e9.2.2 Probability forecasts 172\u003c\/p\u003e \u003cp\u003e9.2.3 Comparison of deterministic and probabilistic binary forecasts 174\u003c\/p\u003e \u003cp\u003e9.3 The relationship between value and the ROC 175\u003c\/p\u003e \u003cp\u003e9.4 Overall value and the Brier Skill Score 178\u003c\/p\u003e \u003cp\u003e9.5 Skill, value and ensemble size 180\u003c\/p\u003e \u003cp\u003e9.6 Applications: value and forecast users 182\u003c\/p\u003e \u003cp\u003e9.7 Summary 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Deterministic forecasts of extreme events and warnings 185\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChristopher A.T. Ferro and David B. Stephenson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 185\u003c\/p\u003e \u003cp\u003e10.2 Forecasts of extreme events 186\u003c\/p\u003e \u003cp\u003e10.2.1 Challenges 186\u003c\/p\u003e \u003cp\u003e10.2.2 Previous studies 187\u003c\/p\u003e \u003cp\u003e10.2.3 Verification measures for extreme events 189\u003c\/p\u003e \u003cp\u003e10.2.4 Modelling performance for extreme events 191\u003c\/p\u003e \u003cp\u003e10.2.5 Extreme events: summary 194\u003c\/p\u003e \u003cp\u003e10.3 Warnings 195\u003c\/p\u003e \u003cp\u003e10.3.1 Background 195\u003c\/p\u003e \u003cp\u003e10.3.2 Format of warnings and observations for verification 196\u003c\/p\u003e \u003cp\u003e10.3.3 Verification of warnings 197\u003c\/p\u003e \u003cp\u003e10.3.4 Warnings: summary 200\u003c\/p\u003e \u003cp\u003eAcknowledgements 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Seasonal and longer-range forecasts 203\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSimon J. Mason\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 203\u003c\/p\u003e \u003cp\u003e11.2 Forecast formats 204\u003c\/p\u003e \u003cp\u003e11.2.1 Deterministic and probabilistic formats 204\u003c\/p\u003e \u003cp\u003e11.2.2 Defining the predictand 206\u003c\/p\u003e \u003cp\u003e11.2.3 Inclusion of climatological forecasts 206\u003c\/p\u003e \u003cp\u003e11.3 Measuring attributes of forecast quality 207\u003c\/p\u003e \u003cp\u003e11.3.1 Skill 207\u003c\/p\u003e \u003cp\u003e11.3.2 Other attributes 215\u003c\/p\u003e \u003cp\u003e11.3.3 Statistical significance and uncertainty estimates 216\u003c\/p\u003e \u003cp\u003e11.4 Measuring the quality of individual forecasts 217\u003c\/p\u003e \u003cp\u003e11.5 Decadal and longer-range forecast verification 218\u003c\/p\u003e \u003cp\u003e11.6 Summary 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Epilogue: new directions in forecast verification 221\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eIan T. Jolliffe and David B. Stephenson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 221\u003c\/p\u003e \u003cp\u003e12.2 Review of key concepts 221\u003c\/p\u003e \u003cp\u003e12.3 Forecast evaluation in other disciplines 223\u003c\/p\u003e \u003cp\u003e12.3.1 Statistics 223\u003c\/p\u003e \u003cp\u003e12.3.2 Finance and economics 225\u003c\/p\u003e \u003cp\u003e12.3.3 Medical and clinical studies 226\u003c\/p\u003e \u003cp\u003e12.4 Current research and future directions 228\u003c\/p\u003e \u003cp\u003eAcknowledgements 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix: Verification Software 231\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMatthew Pocernich\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eA.1 What is good software? 231\u003c\/p\u003e \u003cp\u003eA.1.1 Correctness 232\u003c\/p\u003e \u003cp\u003eA.1.2 Documentation 232\u003c\/p\u003e \u003cp\u003eA.1.3 Open source\/closed source\/commercial 232\u003c\/p\u003e \u003cp\u003eA.1.4 Large user base 232\u003c\/p\u003e \u003cp\u003eA.2 Types of verification users 232\u003c\/p\u003e \u003cp\u003eA.2.1 Students 233\u003c\/p\u003e \u003cp\u003eA.2.2 Researchers 233\u003c\/p\u003e \u003cp\u003eA.2.3 Operational forecasters 233\u003c\/p\u003e \u003cp\u003eA.2.4 Institutional use 233\u003c\/p\u003e \u003cp\u003eA.3 Types of software and programming languages 233\u003c\/p\u003e \u003cp\u003eA.3.1 Spreadsheets 235\u003c\/p\u003e \u003cp\u003eA.3.2 Statistical programming languages 235\u003c\/p\u003e \u003cp\u003eA.4 Institutional supported software 238\u003c\/p\u003e \u003cp\u003eA.4.1 Model Evaluation Tool (MET) 238\u003c\/p\u003e \u003cp\u003eA.4.2 Ensemble Verification System (EVS) 239\u003c\/p\u003e \u003cp\u003eA.4.3 EUMETCAL Forecast Verification Training Module 239\u003c\/p\u003e \u003cp\u003eA.5 Displays of verification information 239\u003c\/p\u003e \u003cp\u003eA.5.1 National Weather Service Performance Management 240\u003c\/p\u003e \u003cp\u003eA.5.2 Forecast Evaluation Tool 240\u003c\/p\u003e \u003cp\u003eGlossary 241\u003c\/p\u003e \u003cp\u003eReferences 251\u003c\/p\u003e \u003cp\u003eIndex 267\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":51767549952343,"sku":"9780470660713","price":76.9,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470660713.jpg?v=1758713742","url":"https:\/\/bookcurl.com\/products\/forecast-verification-a-practioners-guide-inatmospheric-science-2e-9780470660713","provider":"Book Curl","version":"1.0","type":"link"}