The Use of Mesoscale Ensemble NWP Information in a Hybrid 3DEnVar System for Severe Convective Weather Events

         Many of the convective storm modeling studies assume horizontally homogeneous and temporally constant environmental conditions as provided by an observed near-storm sounding. Thus, any rapid temporal and spatial changes in environmental conditions are not captured in these studies and likely influence the accuracy of the resulting forecasts. Recent results by Aksoy et al. (2009) illustrate that even simple representations of mesoscale environmental uncertainty are critical to producing accurate analyses of convective weather events from radar observations. Stensrud and Gao (2010) further shows that the ensemble with horizontally and vertically inhomogeneous background fields provides improved predictions of thunderstorm structure, mesocyclone track, and low-level circulation track. Over the past two decades, short-range ensemble forecasting (SREF) at the NCEP has come to the fore as a major element in defining the future of mesoscale operational weather forecasting. The NCEP now runs operationally an 18-member ensemble daily with its SREF with horizontal resolution with 16 km. The product is available every 3 hours interval for 87 hours. This product may provide very good information about mesoscale environmental uncertainty or variability. In this study, we explore to incorporate this mesoscale environmental information into a hybrid 3DEnVAR system to form a multi-scale data assimilation (DA) system with the hope of improving the radar DA and storm scale NWP. The mesoscale ensemble information will be incorporated by using it as a component to estimate ensemble covariance. The main purpose here is simply to better represent mesoscale environmental uncertainty in the storm scale DA system. The overall cost should not be significantly increased because the mesoscale ensemble product that already exists will be used. The usefulness of mesoscale ensemble information to the storm scale NWP will be accessed with several real data cases about tornadic severe weather events collected during NOAA Hazardous Weather Testbed Spring experiments.