The methodology of Warning Decision Making (WDM) takes into account both meteorological and non-meteorological factors (Quoetone and Huckabee, 1995). One of the most important meteorological aspects to effective WDM in NWS forecast offices is integration of the Near-Storm Environment (NSE) data into the convective warning process. Detailed information from analyzing the mesoscale convective environment can provide clues to potential storm evolutions by helping forecasters recognize particular patterns and features that are known to produce specific types of severe weather. The recognition of expected severe weather evolution through careful analysis of the NSE before and during an event is a very important factor in enhancing a forecaster’s confidence in issuing warnings. Often, signals in the NSE will provide “evidence” that will help "tip the scales" in the eventual warning decision.

The high-resolution data sets currently available in AWIPS such as the Local Analysis and Prediction System (LAPS) provide forecasters with an unprecedented means to perform comparative evaluations of the mesoscale environment for various storms across the County Warning Area (CWA). Careful investigation of buoyancy and shear-related parameters in LAPS, combined with high resolution radar, satellite, and spotter reports often highlight which regions are more (or less) likely to support (and sustain) strong tornadic storm development. Verifying the LAPS fields with the background Rapid Update Cycle (RUC) model output and local surface observations are a necessary component to successfully using NSE analysis in the WDM.

This presentation summarizes the benefits and limitations of using NSE data sets in AWIPS such as LAPS for supporting WDM. The results and impacts of a NSE WDM case exercise used in our teletraining session, which was taught in the Spring of 2001, are shown.