This study evaluates the skill of the PSU/NCAR mesoscale model (MM5) in predicting the storm tracks and associated precipitation during tropical cyclone events within the Philippine Area of Responsibility (PAR). Simulated storm tracks and accumulated rainfall forecasts of six tropical
cyclones that occurred in 2003 are compared with satellite and station data. Twelve cases using different cumulus, planetary boundary layer and explicit moisture parameterization schemes are used for each run. The model is initialized using large-scale forecast data from the Japan Meteorological Agency’s Global Spectral Model and run on a domain that includes the PAR (100-180E longitude and 0-60N latitude) with a 90 km horizontal resolution. Forecasts are made for 72 hours at 6-hour intervals.
Results show that the performance of MM5 in predicting the tropical cyclone tracks and precipitation associated with tropical cyclones in the Philippine domain depends highly on the cumulus parameterization scheme (CPS) and less on the planetary boundary layer (PBL) and explicit moisture (MPHYS) parameterization schemes. Among the three CPS used in this study, the Betts-Miller CPS consistently gives the most accurate and smoothest tracks. The Betts-Miller CPS also provides a well-defined eye for all the storms. Results suggest that the MM5 configuration using Betts-Miller CPS, Medium-Range Forecast (MRF) PBL and Schultz explicit moisture parameterization scheme is the optimal configuration for forecasting typhoon tracks over the Philippine domain. With regard to the rainfall forecasts, the case using Anthes-Kuo cumulus, Medium-Range Forecast planetary boundary layer and Simple Ice explicit moisture parameterization scheme proves to be the best configuration among the cases tested. However, it should be noted that despite this, it still overestimates low rainfall values and underestimates high rainfall values. The rainfall forecasts are also found to be sensitive to the individual characteristics of the storm, namely, its intensity and whether or not it achieves landfall during the simulation period.
This study shows that MM5 has the capability to predict tropical cyclone tracks and associated rainfall. It also opens up more possibilities for improving these forecasts.