Jason Otkin, University of Reading / University of Wisconsin-Madison

Assimilation of satellite infrared brightness temperatures and Doppler radar observations during a convection-resolving Observing System Simulation Experiment

Rebecca Cintineo, Thomas Jones, Steve Koch, Louis Wicker, and Dave Stensrud


This study uses an Observing System Simulation Experiment to explore the impact of assimilating GOES-R Advanced Baseline Imager (ABI) infrared brightness temperatures and Weather Surveillance Radar (WSR)-88D radar reflectivity and radial velocity observations in an ensemble data assimilation system. A high-resolution truth simulation was used to create synthetic radar and satellite observations of a severe weather event that occurred across the central United States on 4-5 June 2005. The data assimilation experiments employ the Weather Research and Forecasting (WRF) model at 4-km horizontal grid spacing and the ensemble adjustment Kalman filter algorithm in the Data Assimilation Research Testbed (DART) system. The ability of the GOES-R ABI infrared brightness temperatures to improve the analysis and forecast accuracy when assimilated separately or simultaneously with Doppler radar reflectivity and radial velocity observations was assessed, along with the use of bias correction and different covariance localization radii for the brightness temperatures. The relative benefits of each observation type in convection-resolving data assimilation will be discussed. Results show that the assimilation of the radar and satellite observations separately improves the storm structure, but that more of the thunderstorms and atmospheric features were reproduced when the infrared brightness temperatures were assimilated. The most accurate short-range forecasts occurred for the case in which both radar and satellite observations were simultaneously assimilated. This provides evidence that these observation types should be assimilated together because they provide independent information about the atmospheric state.

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