USING ISENTROPIC TECHNIQUES TO IMPROVE THE UTILITY OF GOES MOISTURE OBSERVATIONS

iAbstract Although Geostationary Operational Environmental Satellite (GOES) sounding ob-servations of moisture are unique in their ability to depict the pre-convective environment at high temporal and spatial resolutions in three-dimensions, new approaches are needed to more fully take advantage of this highly under-utilized dataset. The NearCast model is a Lagrangian trajectory model that has been developed to take advantage of the GOES tem-perature and moisture retrieval data by dynamically projecting every observation forward in space and time to improve 1-9 hour forecasts of hazardous weather. The model is run at mul-tiple levels of the atmosphere so deep-layer atmospheric stability and shear can be computed, helping to improve short-term predictions of the timing and location of convection. By using a Lagrangian framework, the model preserves fine details in the observations such as minima, maxima, and boundaries while providing output to forecasters shortly after the observations are made by the GOES sounder. Because the temperature and moisture retrievals are made from the clear sky where flow is mostly adiabatic, an isentropic approach provides a better framework to predict thei