Diagnosing large-scale tropical cyclone model moisture and exploring impacts on track and intensity

Moisture plays a vital role in vortex development. Storm intensity, size, and structure are all impacted by changes to environmental moisture. To understand how operational models resolve the moisture field around tropical cyclones, synthetic Total Precipitable Water (TPW) output from the operational Hurricane Weather Research and Forecast (HWRF) model is compared with the NOAA NESDIS operational blended TPW product. Mean absolute error, mean bias, and mean square error skill score that uses a climatological reference are used to evaluate model initialization and performance. Results show that during the 2015 hurricane season, HWRF has a moist unconditional bias in both the Eastern North Pacific and North Atlantic hurricane basins. In addition, there are forecast times that have dramatically lower initial TPW skill scores, indicating poor initialization. With these skill score anomalies in mind, the possible utility of the TPW skill score for providing guidance-on-guidance for forecast track and intensity will also be explored and discussed.