13.4 The Analyses and Prediction of a Supercell Storm from Assimilating Radar and Satellite Observations using an Ensemble Kalman Filter

Thursday, 10 January 2013: 4:15 PM
Room 9C (Austin Convention Center)
Matthew Thomas Vaughan, Embry-Riddle Aeronautical University, Daytona Beach, FL; and N. Yussouf and T. A. Jones

This research assesses the impact of assimilating satellite-derived cloud liquid water path data along with radar reflectivity and radial velocity observations into high-resolution model forecasts of a tornadic supercell that struck Moore, OK on 8 May 2003. The project uses a combined 45-member 18-km resolution mesoscale, and 3-km resolution convective-scale ensemble data assimilation and prediction system utilizing the Weather Research and Forecasting model (WRF) and Data Assimilation Research Testbed (DART) Ensemble Kalman Filter (EnKF) assimilation scheme. The project consists of a control run assimilating only radar data, an experiment assimilating only satellite data, and two experiments assimilating both radar and satellite observations in different combinations. For each experiment, one-hour forecasts at 1-minute intervals are generated from the 2200 UTC analyses.

The ensembles correctly initiate a supercell by the end of the assimilation cycle at 2200 UTC. The experiment assimilating satellite data for 2 hours prior to the forecast improves the near-storm environment in the analysis; thus, producing more realistic reflectivity and updraft helicity structures for most forecast times out to 2300 UTC. These results provide an introduction for the potential of including satellite data into radar data-assimilating convective-scale forecast models.

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