A Regional GSI-Based Ensemble Kalman Filter Data Assimilation System for the Rapid Refresh Configuration: Testing at Reduced Resolution

A regional ensemble Kalman filter (EnKF) system is established for potential Rapid Refresh (RAP) operational application. The system borrows data processing and observation operators from the gridpoint statistical interpolation (GSI), and precalculates observation priors using the GSI. The ensemble...

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Bibliographic Details
Published in:Monthly weather review 2013-11, Vol.141 (11), p.4118-4139
Main Authors: KEFENG ZHU, YUJIE PAN, MING XUE, XUGUANG WANG, WHITAKER, Jeffrey S, BENJAMIN, Stanley G, WEYGANDT, Stephen S, MING HU
Format: Article
Language:English
Subjects:
Covariance
Data assimilation
Data collection
Data processing
Earth, ocean, space
Errors
Exact sciences and technology
Experiments
External geophysics
Hydrologic data
Kalman filters
Mathematical analysis
Mathematical models
Meteorology
Parametrization
Physics
Precipitation
Regional
Relative humidity
Weather forecasting
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Summary:A regional ensemble Kalman filter (EnKF) system is established for potential Rapid Refresh (RAP) operational application. The system borrows data processing and observation operators from the gridpoint statistical interpolation (GSI), and precalculates observation priors using the GSI. The ensemble square root Kalman filter (EnSRF) algorithm is used, which updates both the state vector and observation priors. All conventional observations that are used in the operational RAP GSI are assimilated. To minimize computational costs, the EnKF is run at of the operational RAP resolution or about 40-km grid spacing, and its performance is compared to the GSI using the same datasets and resolution. Short-range (up to 18 h, the RAP forecast length) forecasts are verified against soundings, surface observations, and precipitation data. Experiments are run with 3-hourly assimilation cycles over a 9-day convectively active retrospective period from spring 2010. The EnKF performance was improved by extensive tuning, including the use of height-dependent covariance localization scales and adaptive covariance inflation. When multiple physics parameterization schemes are employed by the EnKF, forecast errors are further reduced, especially for relative humidity and temperature at the upper levels and for surface variables. The best EnKF configuration produces lower forecast errors than the parallel GSI run. Gilbert skill scores of precipitation forecasts on the 13-km RAP grid initialized from the 3-hourly EnKF analyses are consistently better than those from GSI analyses.
ISSN:0027-0644
1520-0493
DOI:10.1175/MWR-D-13-00039.1