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Full Description
This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including adaptive observations, sensitivity analysis, parameter estimation and AI applications. The book is useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Contents
Data Assimilation for Chaotic Dynamics.- Multifidelity Data Assimilation for Physical Systems.- Filtering with One-Step-Ahead Smoothing for Efficient Data Assimilation.- Sparsity-Based Kalman Filters for Data Assimilation.- Perturbations by the Ensemble Transform.- Stochastic Representations for Model Uncertainty in the Ensemble Data Assimilation System.- Second-Order Methods in Variational Data Assimilation.- Statistical Parameter Estimation for Observation Error Modelling: Application to Meteor Radars.- Observability Gramian and Its Role in the Placement of Observations in Dynamic Data Assimilation.- Placement of Observations for Variational Data Assimilation: Application to Burgers' Equation and Seiche Phenomenon.- Analysis, Lateral Boundary, and Observation Impacts in a Limited Area Model.- An Overview of KMA's Operational NWP Data Assimilation Systems.
