Numerical Forecasts of the 15-16 June 2002 Southern Plains Mesoscale Convective System: Impact of Mesoscale Data and Cloud Analysis

摘要

High-resolution explicit forecasts using the Advanced Regional Prediction System (ARPS) of the 15-16 June 2002 mesoscale convective system (MCS) that occurred over the U.S. central and southern plains during the International H_2O Project (IHOP_2002) field experiment period are performed. The forecasts are designed to investigate the impact of mesoscale and convective-scale data on the initialization and prediction of an organized convective system. Specifically, the forecasts test the impact of special mesoscale surface and upper-air data collected by, but not necessarily specific to, IHOP_2002 and of level-II data from multiple Weather Surveillance Radar-1988 Doppler radars. The effectiveness of using 30-min assimilation cycles with the use of a complex cloud-analysis procedure and high-temporal-resolution surface data is also examined. The analyses and forecasts employ doubly nested grids, with resolutions of 9 and 3 km. Emphasis is placed on the solutions of the 3-km grid. In all forecasts, a strong, well-defined bow-shaped MCS is produced with structure and behavior similar to those of the observed system. Verification of these forecasts through both regular and phase-shifted equitable threat scores of the instantaneous composite reflectivity fields indicate that the use of the complex cloud analysis has the greatest positive impact on the prediction of the MCS, primarily by removing the otherwise needed "spinup" time of convection in the model. The impact of additional data networks is smaller and is reflected mainly in reducing the spinup time of the MCS too. The use of intermittent assimilation cycles appears to be quite beneficial when the assimilation window covers a time period when the MCS is present. Difficulties with verifying weather systems with high spatial and temporal intermittency are also discussed, and the use of both regular and spatially shifted equitable threat scores is found to be very beneficial in assessing the quality of the forecasts.