Application of a WRF mesoscale ensemble data assimilation system to severe weather events during springs 2007–2009

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 land and marine surface stations, rawinsondes, and aircraft are assimilated into a 40-member ensemble that is constructed from initial and boundary conditions provided by the North American Mesoscale (NAM) forecast cycle starting 1200 UTC. This ensemble accounts for initial condition and model physics uncertainties. Available observations are assimilated at hourly intervals for the period 1300 - 1800 UTC using the ensemble Kalman filter (EnKF) approach encoded in the Data Assimilation Research Testbed (DART) framework. The resultant analyses are used to generate a pure ensemble forecast (i.e., with no assimilation) 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 springs 2007-2009.

Performance of the assimilation system is assessed through comparison to commonly utilized analyses of mesoscale environments. 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., significant tornado parameter (STP) and MCS maintenance probability (MMP)], will be discussed and implications for the future examined.