Application of synthetic GOES imagery to the Hurricane Forecast Improvement Project

The Hurricane Forecast Improvement Project (HFIP) is a relatively new ten year NOAA project to improve hurricane track and intensity forecasts. This will be accomplished through the use of advanced hurricane models, data assimilation, and observations. One limitation to model improvement is the lack of ground truth for validation, especially in the upper levels of storms. Satellite data can help to fill that gap, but the measurements are usually of different quantities than forecast by the models. . A novel approach has been developed at CIRA in which synthetic GOES satellite imagery is produced from numerically simulated weather events using forward radiative transfer models. Although this procedure is routinely applied as part of variational data assimilation systems, the CIRA procedure is being used to simulate future satellite systems and to validate numerical forecasts through the comparison of the synthetic and real satellite data. This procedure has been successfully applied to severe thunderstorm events over the Central Plains of the United States. In this study, synthetic imagery will be produced from a simulation of a tropical cyclone and used for model evaluation.

The model considered in this study is the Coupled Ocean Atmospheric Modeling Prediction System (COAMPS) will be used to simulate a tropical storm. Synthetic GOES imagery from the simulated tropical storm will be presented and compared to the corresponding real data from GOES. Attention will be given to the application of synthetic GOES imagery that focuses on the structure and intensity of tropical storms and the identification of model strengths and limitations.