Development of a mesoscale ensemble data assimilation system at the naval research laboratory

Qingyun Zhao, Fuqing Zhang, Teddy Holt, Craig H. Bishop, Qin Xu

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of highresolution observations, including those fromDoppler radars, on stormprediction. The systemhas been improved during its implementation at NRL to further enhance its capability of assimilating various types ofmeteorological data. Aparallel algorithm was also developed to increase the system's computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedurewas developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-denying experiments). The procedure was employed in this study to examine the impacts of ensemble size and localization on data assimilation and the results reveal a very interesting relationship between the ensemble size and the localization length scale. All the tests conducted in this study demonstrate the capabilities of the EnKF as a research tool for mesoscale and storm-scale data assimilation with potential operational applications.

Original languageEnglish (US)
Pages (from-to)1322-1336
Number of pages15
JournalWeather and Forecasting
Volume28
Issue number6
DOIs
StatePublished - Dec 1 2013

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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