Linked e-resources
Details
Table of Contents
Preface; Contents; 1 Introduction Through Historical Perspective; 1.1 From Gauss to Kolmogorov; 1.2 Approaching the Meteorological System; 1.3 Numerical Weather Prediction Models; 1.4 What, Where, When; References; 2 Representation of the Physical System; 2.1 The Observational System and Errors; 2.1.1 The Estimation Problem; 2.1.2 The Linear Hypothesis; 2.1.3 Optimal Estimation; 2.1.4 Minimization Methods of Cost Functions; 2.1.5 Some Properties of Estimation; 2.1.6 Estimation of the quality of analysis; 2.2 Variational Approach: 3-D VAR and 4-D VAR; 2.3 Assimilation as an Inverse Problem
2.3.1 An Illustrative ExampleReferences; 3 Sequential Interpolation; 3.1 An Effective Introduction of a Kalman Filter; 3.1.1 Linear System; 3.1.2 Building up the Kalman Filter; 3.2 More Kalman Filters; 3.2.1 The Extended Kalman Filter; 3.2.2 Sigma Point Kalman Filter (SPKF); 3.2.3 Unscented Kalman Filter (UKF); References; 4 Advanced Data Assimilation Methods; 4.1 Recursive Bayesian Estimation; 4.1.1 The Kalman Filter; 4.1.2 The Forecast Step; 4.1.3 The Analysis Step; 4.1.4 Prediction by Stochastic Filtering; 4.2 Ensemble Kalman Filter; 4.2.1 The Stochastic Ensemble Kalman Filter Menu
4.2.2 The Deterministic Ensemble Kalman Filter4.2.3 The Analysis Scheme; 4.2.4 The Deterministic Ensemble Kalman Filter Menu; 4.3 Issues Due to Small Ensembles; 4.3.1 Inbreeding; 4.3.2 Filter Divergence; 4.3.3 Spurious Correlations; 4.4 Methods to Reduce Problems of Undersampling; 4.4.1 Spatial Localization; 4.4.2 Covariance Inflation; 4.4.3 Covariance Localization; References; 5 Applications; 5.1 Lorenz Model; 5.1.1 Solution of Lorenz 63 Model; 5.1.2 Lorenz Model and Data Assimilation; 5.2 Biology and Medicine; 5.2.1 Tumor Growth; 5.2.2 Growth Tumor Data Assimilation with LETKF
5.2.3 LETFK Receipt Computation5.3 Mars Data Assimilation: The General Circulation Model; 5.3.1 Mars Data Assimilation: Methods and Solutions; 5.4 Earthquake Forecast; 5.4.1 Renewal Process as Forecast Model; 5.4.2 Sequential Importance Sampling and Beyond; 5.4.3 The Receipt of SIR; References; Appendix; Index
2.3.1 An Illustrative ExampleReferences; 3 Sequential Interpolation; 3.1 An Effective Introduction of a Kalman Filter; 3.1.1 Linear System; 3.1.2 Building up the Kalman Filter; 3.2 More Kalman Filters; 3.2.1 The Extended Kalman Filter; 3.2.2 Sigma Point Kalman Filter (SPKF); 3.2.3 Unscented Kalman Filter (UKF); References; 4 Advanced Data Assimilation Methods; 4.1 Recursive Bayesian Estimation; 4.1.1 The Kalman Filter; 4.1.2 The Forecast Step; 4.1.3 The Analysis Step; 4.1.4 Prediction by Stochastic Filtering; 4.2 Ensemble Kalman Filter; 4.2.1 The Stochastic Ensemble Kalman Filter Menu
4.2.2 The Deterministic Ensemble Kalman Filter4.2.3 The Analysis Scheme; 4.2.4 The Deterministic Ensemble Kalman Filter Menu; 4.3 Issues Due to Small Ensembles; 4.3.1 Inbreeding; 4.3.2 Filter Divergence; 4.3.3 Spurious Correlations; 4.4 Methods to Reduce Problems of Undersampling; 4.4.1 Spatial Localization; 4.4.2 Covariance Inflation; 4.4.3 Covariance Localization; References; 5 Applications; 5.1 Lorenz Model; 5.1.1 Solution of Lorenz 63 Model; 5.1.2 Lorenz Model and Data Assimilation; 5.2 Biology and Medicine; 5.2.1 Tumor Growth; 5.2.2 Growth Tumor Data Assimilation with LETKF
5.2.3 LETFK Receipt Computation5.3 Mars Data Assimilation: The General Circulation Model; 5.3.1 Mars Data Assimilation: Methods and Solutions; 5.4 Earthquake Forecast; 5.4.1 Renewal Process as Forecast Model; 5.4.2 Sequential Importance Sampling and Beyond; 5.4.3 The Receipt of SIR; References; Appendix; Index