The EnKF is a flexible data assimilation technique designed to use all available information in order to produce the most accurate possible description of the state of the flow. Also it provides the uncertainty in the state of the flow resulting from the uncertainties in various sources of information. An ensemble-Kalman filter (EnKF) implemented recently in the Weather Research and Forecast (WRF) model has been demonstrated to be very effective in assimilating simulated surface and sounding observations with typical temporal and spatial resolutions for this MCV event, even if an imperfect model is used. Moreover, recent studies with both real-data and simulated observations also demonstrated the EnKF is also quite effective in assimilating Radar observations at the convective scales. However, it remained unanswered whether the EnKF will be effective to assimilate radar observations at the meso- and regional scales simultaneously with the regular surface and sounding observations. The forecast model in this case will have to be running at a resolution (3-10-km grid spacing) in order to cover a larger domain pertinent to the MCV evolution and to at least marginally resolve convection.
We just began to perform the assimilation of simulated observations available at both the meso- and convective scales. We expect to assimilate in-situ real-data observations of this MCV event after sufficient understanding of the EnKF behaviors has been gained through these OSSE experiments. We believe the proposed EnKF technique will not only maximize the information gained from the direct measure of low-level winds and thermodynamic properties of the boundary layer and low-troposphere but can also induce information of many unobserved state variables (such as those in data sparse area or of vertical velocity) through ensemble estimation of the flow-dependent background error covariance. The integrated data sets and subsequent numerical simulations from the data assimilation will be used to understand the role of MCVs in initiating and modulating convection and its feedback and to assess the predictability of the timing and location of the development and regeneration of convection associated with MCVs.