Improving above-surface water vapor analyses using satellite and RUC data

An accurate depiction of the pre-storm environment requires not only good estimates of surface temperature and dew point, but also information about temperature and water vapor profiles within and just above the boundary layer. Mixed-layer Convective Available Potential Energy (MLCAPE), for example, is sensitive to water vapor contents just above the surface. An overforecast of 850-mb dewpoint can result in a poor surface dewpoint forecast since thin moist layers often mix out, resulting in a significant overforecast of CAPE.

Current techniques of diagnosing MLCAPE include using observed temperature and dewpoint, and Rapid Update Cycle (RUC) 1-hour forecasts for profiles above the surface. If RUC forecasts are in error, a poor prediction for MLCAPE can result. This study attempts to improve analyses of low-level water vapor by starting with RUC 1-hour forecasts and adjusting based on GOES satellite observations and surface moisture. This is accomplished by collecting 3 months of summertime RUC, satellite, and sounding data, and using multiple regression to produce the best analyses of above-the-surface moisture. Preliminary results show that using these additional data sources improve the RUC 1-hour forecasts.