Application of a WRF mesoscale ensemble data assimilation system to severe weather events during spring 2009
Dustan M. Wheatley, CIMMS/Univ. of Oklahoma, Norman, OK; and M. Coniglio and D. J. Stensrud
An ensemble-based data assimilation system using the Weather Research and Forecasting (WRF) model has been developed to explore the impact of conventional observations on mesoscale analyses and forecasts of severe weather events. Routinely available observations from surface stations, rawinsondes, and wind profilers are assimilated into a 40-member ensemble that is constructed from initial and boundary conditions provided by the Rapid Update Cycle (RUC) forecast cycle (at 1200 UTC). This ensemble accounts for initial condition and model physics uncertainties. The square-root ensemble Kalman filter (EnKF) approach encoded in the Data Assimilation Research Testbed (DART) framework is used in the assimilation process. Hourly analyses are generated for the period 1300 UTC to 0600 UTC the following day on a continental United States domain with a horizontal grid spacing of 20 km. Preliminary work is focused on the analyses of prolific severe weather events from spring 2009.
Performance of the assimilation system is assessed through comparison to mesoscale objective analyses of observational fields. Special emphasis is placed on the system's ability to produce realistic mesoscale structures (e.g., dry lines, cold pools) that affect convective development, but are often not present in models without data assimilation. In addition, impacts of the assimilation on environmental characteristics such as CAPE and shear, as well as other severe weather parameters (e.g., supercell motion forecast algorithms), will be discussed and implications for the future examined.
Session 17, Mesoscale predictability and data assimilation I
Thursday, 20 August 2009, 1:45 PM-3:15 PM, The Canyons
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