Editorial Type:
Article
Article Type:
Research Article

Development of a Mesoscale Ensemble Data Assimilation System at the Naval Research Laboratory

Qingyun Zhao Marine Meteorology Division, Naval Research Laboratory, Monterey, California

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Fuqing Zhang The Pennsylvania State University, University Park, Pennsylvania

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Teddy Holt Marine Meteorology Division, Naval Research Laboratory, Monterey, California

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Craig H. Bishop Marine Meteorology Division, Naval Research Laboratory, Monterey, California

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Qin Xu National Severe Storms Laboratory, Norman, Oklahoma

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Print Publication:
01 Dec 2013
DOI:
https://doi.org/10.1175/WAF-D-13-00015.1
Page(s):
1322–1336
Restricted access

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 high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel 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 procedure was 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.

Corresponding author address: Dr. Qingyun Zhao, Naval Research Laboratory, 7 Grace Hopper Ave., Mail Stop II, Monterey, CA 93943. E-mail: allen.zhao@nrlmry.navy.mil

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 high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel 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 procedure was 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.

Corresponding author address: Dr. Qingyun Zhao, Naval Research Laboratory, 7 Grace Hopper Ave., Mail Stop II, Monterey, CA 93943. E-mail: allen.zhao@nrlmry.navy.mil
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