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

Fuqing Zhang, Yonghui Weng, Jason A. Sippel, Zhiyong Meng, Craig H. Bishop

Research output: Contribution to journalArticlepeer-review

243 Scopus citations

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 coast, closely represents the best-track position and intensity of Humberto. Deterministic forecasts initialized from the EnKF analysis, despite displaying considerable variability with different lead times, are also capable of predicting the rapid formation and intensification of the hurricane. These forecasts are also superior to simulations without radar data assimilation or with a three-dimensional variational scheme assimilating the same radar observations. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. However, the large ensemble spread of peak intensity, which ranges from a tropical storm to a category 2 hurricane, echoes limited predictability in deterministic forecasts of the storm and the potential of using ensembles for probabilistic forecasts of hurricanes.

Original languageEnglish (US)
Pages (from-to)2105-2125
Number of pages21
JournalMonthly Weather Review
Volume137
Issue number7
DOIs
StatePublished - Oct 29 2009

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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