Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV)

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.

Auteur

Seon Ki Park is Professor of Environmental Science and Engineering and Founding Director of the Severe Storm Research Center and the Center for Climate/Environment Change Prediction Research at the Ewha Womans University in Seoul, Korea. He obtained a Ph.D. in Meteorology from the University of Oklahoma and M.S. and B.S. in Meteorology from the Seoul National University, Korea. He had worked as a research scientist at the University of Oklahoma, University of Maryland and NASA/Goddard Space Flight Center. His research focuses on storm- and meso-scale meteorology, hydrometeorology, and parameter estimation and data assimilation to improve numerical weather/climate prediction.

Liang Xu is the Head of Atmospheric Dynamics & Prediction Branch and a Meteorologist at the Marine Meteorology Division, Naval Research Laboratory in Monterey, California, USA. He leads a fully integrated research program encompassing all aspects of numerical weather prediction and data assimilation, focusing on critical issues related to the analysis and prediction of atmospheric processes and phenomena within the Navy's Earth System Prediction Capability. He and his team have developed, tested, and transitioned to the Fleet Numerical Meteorology and Oceanographic Center (FNMOC), an operational global atmospheric 4DVar data assimilation system.

Contenu

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.- Improving Near-Surface Weather Forecasts with Strongly Coupled Land-Atmosphere Data Assimilation.- An Overview of KMA's Operational NWP Data Assimilation Systems.