The potential for improving short-term model forecasts of a squall line using mesoscale observing networks (or mesonets) and radar data is addressed in this study. On 30 April 2005 between 00Z and 12Z, a squall line with severe winds and a few tornadoes passed through the Mississippi Delta. The Delta is the distinct northwest section of the state of Mississippi that lies between the Mississippi and Yazoo Rivers. This region, created by regular flooding over thousands of years, is remarkably flat and contains some of the most fertile soil in the world. Because of this region's agricultural importance, improving severe weather forecasts are of great interest to the state.

The Delta contains a number of stations in the Soil Climate Analysis Network (SCAN). SCAN uses meteor burst telemetry to obtain remote site information reliably on a near real-time basis, and focuses on the agricultural areas. The data include hourly and daily recordings of precipitation, air temperature, solar radiation, wind speed, relative humidity, soil moisture, and soil temperature. This study seeks to investigate the impact of this data on the analysis and prediction of spring squall lines.

Another research tool with tremendous potential for improving short-term thunderstorm prediction is the assimilation of Doppler radar data (radial velocity) into WRF. Xiao et al. (2005, 2006) show this technique improves rainfall forecasts, and better defines rainband structure, especially if used in a 3DVAR cycling mode. The overall goal of this research is to improve - using variational data assimilation techniques on radar and SCAN data with WRF - the prediction of the Quantitative Prediction Forecast (QPF) for this case.

The first step involves defining the background error. The background error was constructed using the "NMC method" with two weeks of 12-h runs. It will be shown this produces generally the same background errors as 6-h runs. Both runs produce different results than the default background error in WRF, showing the importance of constructing your own background error for high resolution, regional runs.

Four runs have been conducted using 1) standard weather data, 2) SCAN data, 3) radar data, and 4) radar and SCAN data. QPF totals will be shown for all four runs based on threat scores, as well as a qualitative comparison of the squall line simulations in terms of propagation speed, intensity, and appearance. It will be shown some aspects of the squal line were handled better using the radar data, especially the forecast of a new squall line. However, the SCAN data made little impact in all the runs.