Journal article

Cloud-resolving hurricane initialization and prediction through assimilation of doppler radar observations with an ensemble Kalman filter

F Zhang, Y Weng, JA Sippel, Z Meng, CH Bishop

Monthly Weather Review | AMER METEOROLOGICAL SOC | Published : 2009

Abstract

This study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), the first landfalling hurricane in the United States since the end of the 2005 hurricane season and the most rapidly intensifying near-landfall storm in U.S. history. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. It is found that the EnKF analysis, after assimilating radial velocity observations from three Weather Surveillance Radars-1988 Doppler (WSR-88Ds) along the Gulf c..

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University of Melbourne Researchers

Grants

Awarded by NSF


Awarded by Office of Navy Research


Awarded by Div Atmospheric & Geospace Sciences; Directorate For Geosciences


Funding Acknowledgements

The authors benefited from discussions with Kerry Emanuel, Chris Snyder, Chris Davis, Jim Hansen, Jim Doyle, Pete Black, Tim Dunkerton, Shuyi Chen, and Dave Nolan. We thank two anonymous reviewers and the editor Tom Hamill for their valuable review comments. This research is supported by NSF Grant ATM-0205599 and by the Office of Navy Research under the Young Investigator Program (Award N000140410471).