18th Conference on Weather and Forecasting, 14th Conference on Numerical Weather Prediction, and Ninth Conference on Mesoscale Processes

Monday, 30 July 2001: 4:45 PM
A comparison study of cumulus parameterization schemes for precipitating systems in the Taiwan area
Ming-Jen Yang, Chinese Culture University, Taipei, Taiwan; and Q. C. Tung
Poster PDF (62.5 kB)
A comparison study of four cumulus parameterization schemes (CPSs), the Anthes-Kuo, Betts-Miller, Grell, and Kain-Fritsch schemes, for precipitating systems in the Taiwan area is conducted using the Penn State/NCAR MM5 model. Performance of these CPSs is examined using six rainfall events (cold-air outbreak, spring cold front, Mei-Yu front, summertime thunderstorm, landfalling typhoon, and autumn cold front) over the Taiwan area for four seasons. Grid resolutions of 45 and 15 km are used to represent the current operational mesoscale model grids in Taiwan. Precipitation forecast is evaluated statistically over the grid points using the threat score and bias score for different threshold values based on island-wide rain-gauge observations.

It is found that the general 12-h precipitation forecast skill for these CPSs is fairly good in predicting four out of six events examined in this study, even for higher thresholds. The forecast skill is generally higher for heavy rainfall events (Mei-Yu front, landfalling typhoon, autumn cold front, and cold-air outbreak) than for light rainfall events (spring cold front and summertime thunderstorm). The schemes with moist downdrafts, such as Grell scheme and Kain-Fritsch scheme, appear to perform better. The Anthes-Kuo scheme has a systematic behavior of producing less rainfall over a wider area. The Betts-Miller scheme tends to moisten the atmosphere too quickly and produce too much rainfall. An ensemble forecast by a simple arithmetic average of rainfall predictions by four CPSs is done for all six precipitating events. In general, the rainfall ensemble forecast outperforms the individual CPS forecast, and the improvement is more obvious for heavy rainfall scenarios.

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