TJ11.2 A Multiscale Ensemble Radar Data Assimilation System for Storm Prediction

Tuesday, 24 January 2017: 4:15 PM
Conference Center: Skagit 5 (Washington State Convention Center )
Qingyun Zhao, NRL, Monterey, CA; and Q. Xu and F. Zhang

An ensemble radar data assimilation system has been developed at the US Naval Research Laboratory (NRL) for the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) to improve the model’s capability and accuracy in predicting very-short-term and short-term high-impact weather events.  This system utilizes an ensemble Kalman filter (EnKF) to assimilate Doppler radial velocity and radar reflectivity data along with other meteorological observations from conventional and satellite sensors into the model. A multiscale localization algorithm was developed for the EnKF that allows radar data to be assimilated into all COAMPS nested grids to initialize the model’s winds and hydrometeor fields at different scales.  To test the system, storm observations from the US Weather Surveillance Radar 88 Doppler (WSR-88D) radar network have been assimilated into the system every 12 hours for a test period of 5 days. Results from experiments have shown forecast improvement for most model variables and forecast lead times at both mesoscale and convective-scale. The radar data also show some impact on large-scale wind and temperature forecasts at upper levels where the horizontal winds are strong. In this presentation, we will give a description of the data assimilation system along with the results from our recent studies.
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