An analysis of the impact of observational data on ETKF-based ensemble perturbations

Mozheng Wei, Zoltan Toth, Richard Wobus, Yuejian Zhu, Craig H. Bishop, Xuguang Wang

Research output: Contribution to journalConference article

Abstract

Global ensemble forecast system makes use of Ensemble Transform Kalman Filter (ETKF), to generate initial perturbation. Mathematical formulation based on Kalman Filter theory is used to make efficient ensemble forecasting. The observation from conventional data assimilation system and satellites account to the accuracy of the system. Experiments are carried out with and without Winter Storm Reconnaissance (WSR) data at particular times to study the impact of observations. The results show that more observations will reduce the errors.

Original languageEnglish
Pages (from-to)25-29
Number of pages5
JournalBulletin of the American Meteorological Society
Publication statusPublished - Jun 1 2004
EventCombined Preprints: 84th American Meteorological Society (AMS) Annual Meeting - Seattle, WA., United States
Duration: Jan 11 2004Jan 15 2004

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ASJC Scopus subject areas

  • Atmospheric Science

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