Analysis of a Tornadic Meoscale Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-band and WSR-88D Radar Data

ii Abstract One of the goals of the National Science Foundation Engineering Research Center (ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) is to improve storm-scale numerical weather prediction (NWP) by collecting data with dense X-band radar network which provides high-resolution low-level coverage, and by assimilating such data into NWP models. During the first spring storm season after the deployment of 4 radars in the CASA IP-1 (Integrated Project-1) network in southwest Oklahoma, a tornadic mesoscale convective system (MCS) was captured by CASA and surrounding WSR-88D radars on 8-9 May 2007. The MCS moved across northwest Texas and western and central Oklahoma; 2 tornadoes of EF-1 intensity and 1 tornado of EF-0 intensity were reported during the event, just to the north of the IP-1 network. This was the first tornadic convective system observed by CASA. To quantify the impact of CASA radar data in storm-scale NWP, a set of data assimilation experiments were performed using the ARPS ensemble Kalman filter (EnKF) system configured with full model physics and high-resolution terrain. Data from 4 CASA IP-1 radars and 5 WSR-88D radars were assimilated in some of the experiments. The ensemble contained 40 members, and radar data were assimilated every 5 minutes for 1 hour. While the assimilation of WSR-88D data alone was able to produce a reasonably accurate analysis of the convective system, assimilating CASA data in addition to WSR-88D data is found to improve the representation of storm-scale circulations, particularly in the lowest few kilometers of the atmosphere, as evidenced by analyses of gust front position and comparison of simulated V r with observations. Assimilating CASA data decreased RMS innovation of the resulting ensemble mean analyses of Z, particularly in early assimilation cycles, suggesting that the addition of CASA data allowed the EnKF system to more quickly achieve a good result. Use of multiple microphysics schemes iii in the forecast ensemble was found to alleviate under-dispersion by increasing the ensemble spread. This work is the first assimilating real CASA data into an NWP model using EnKF.