Artículos relacionados a Data Assimilation with the Local Ensemble Transform...

Data Assimilation with the Local Ensemble Transform Kalman Filter: addressing model errors, observation errors and adaptive inflation - Tapa blanda

Li, Hong; Kalnay, Eugenia

 
9783639308129: Data Assimilation with the Local Ensemble Transform Kalman Filter: addressing model errors, observation errors and adaptive inflation
Tapa blanda
ISBN 10: 3639308123 ISBN 13: 9783639308129
Editorial: VDM Verlag Dr. Müller, 2010

Sinopsis

Our work has addressed several issues relating to Ensemble Kalman Filter (EnKF) for assimilating real data, 1) model errors, 2) inconvenience or infeasibility of manually tuning the inflation factor when it is regional and/or variable dependent and 3) erroneously specified observation error statistics. A Local Ensemble Transform Kalman Filter (LETKF) is used as an efficient representative of other EnKF systems. For the model errors issue, we assimilate observations generated from the NCEP/NCAR reanalysis fields into the SPEEDY model. Several methods to handle model errors including model bias and system-noise are investigated. We address the second and third issues by simultaneously estimating both inflation factor and observation error variance on-line. Our research in this book suggests the need to develop a more advanced LETKF with both bias correction and adaptive estimation of inflation within the system.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

Our work has addressed several issues relating to Ensemble Kalman Filter (EnKF) for assimilating real data, 1) model errors, 2) inconvenience or infeasibility of manually tuning the inflation factor when it is regional and/or variable dependent and 3) erroneously specified observation error statistics. A Local Ensemble Transform Kalman Filter (LETKF) is used as an efficient representative of other EnKF systems. For the model errors issue, we assimilate observations generated from the NCEP/NCAR reanalysis fields into the SPEEDY model. Several methods to handle model errors including model bias and system-noise are investigated. We address the second and third issues by simultaneously estimating both inflation factor and observation error variance on-line. Our research in this book suggests the need to develop a more advanced LETKF with both bias correction and adaptive estimation of inflation within the system.

Biografía del autor

Hong Li is currently an associate professor in Shanghai Typhoon Institute, China. She received her Ph.D. from University of Maryland (UMD) in 2007. Eugenia Kalnay is a Distinguished University Professor of Atmospheric and Oceanic Science at the UMD in the United States.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
VDM Verlag Dr. Müller
Año de publicación
2010
Idioma
Inglés
ISBN 10 
3639308123
ISBN 13 
9783639308129
Encuadernación
Tapa blanda
Número de páginas
136
Contacto del fabricante
no disponible
Persona responsable
no disponible