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

Li, Hong; Kalnay, Eugenia

ISBN 10: 3639308123 / ISBN 13: 9783639308129

Published by VDM Verlag Dr. Müller, 2010

About this Item

Seller Inventory # I-9783639308129

Bibliographic Details

• Title: Data Assimilation with the Local Ensemble Transform Kalman Filter: addressing model errors, observation errors and adaptive inflation
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2010
• Language: English
• ISBN 10: 3639308123
• ISBN 13: 9783639308129
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

About the Author

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.