An ensemble Kalman filter approach to data assimilation for the NY Harbor. Ensemble data assimilation experiments for the coastal ocean: Impact of different observed variables Ross N Hoffman 1 , Rui M Ponte 1 , Eric Kostelich 2 , Alan Blumberg 3 , Istvan Szunyogh 4 ,
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
An ensemble Kalman filter approach to data assimilation for the NY Harbor.
Ensemble data assimilation experiments for the coastal ocean: Impact of different observed variables
Ross N Hoffman1, Rui M Ponte1,
Eric Kostelich2, Alan Blumberg3, Istvan Szunyogh4,
and Sergey V Vinogradov1
1Atmospheric and Environmental Research, Inc.
2Arizona State University
3Stevens Institute of Technology
4University of Maryland
IGARSS 2008 (Boston)
FR3.111.4, Friday, 11 July 2008, 14:20
SST 06 UTC 27 April 2004 SST 16 UTC 28 April 2004
Free Running Forecast
Truth (Nature Run)
Filter divergence is only in unobserved river head waters. These areas eliminated in following statistics.
Tuning eliminates filter divergence
Tuning improves errors
Tuning very quickly removes bias
Sandy Hook, NJ
Pier 40, NY