9B.4
A multi-case comparative assessment of storm-scale ensemble forecasts initialized with ensemble Kalman filter radar-data assimilation
PAPER WITHDRAWN
Altug Aksoy, NCAR, Boulder, CO; and D. C. Dowell and C. Snyder
The assimilation of radar observations for ensemble-based mesoscale and storm-scale applications is one of the most challenging partially-solved problems of data assimilation. In this study, we focus on the performance of the ensemble Kalman filter (EnKF), assimilating radar observations, in cases with various convective environmental characteristics such as supercellular, linear, and multicellular. The parallel EnKF algorithm of the Data Assimilation Research Testbed (DART) is used for data assimilation, while the Weather Research and Forecasting (WRF) model is employed as a simplified cloud model at 2-km horizontal grid spacing. In each case, reflectivity and radial velocity measurements are utilized from a single WSR-88D radar within the U.S. operational network. Observations are assimilated every 2 minutes for a duration of 60 minutes. Initial ensemble uncertainty includes random perturbations to the horizontal wind components of the initial environmental sounding and randomly distributed warm bubbles that initiate convection.
Previously, we have presented our evaluation of filter performance based on the quality of analyses after 60-minute cycling of observations. In this second phase of the study, we focus on ensemble forecasts initialized from such analyses. For each of our cases, we perform 30-minute ensemble forecasts and verify our forecasts using observed radar data. We will discuss in what ways ensemble forecasts are different than deterministic forecasts initialized from ensemble-mean analyses at convective scales, through the focus on the variability among ensemble-member states at different stages of forecasts. Evaluations of forecast quality based on ensemble members' capacity to represent the mode/strength/evolution of convection and to maintain a smooth transition from analysis to forecast states will be presented. We will also discuss the dependence of forecast quality on the length of assimilation period.
Session 9B, Data Assimilation
Tuesday, 28 October 2008, 4:30 PM-6:00 PM, South Ballroom
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