Monday, 21 June 2004
Quantitative Precipitation Forecasting (QPF) remains one of the foremost challenges in numerical weather prediction, in spite of advances in computing power and the degree of sophistication of regional models. In this study, the skill of a mesoscale model in predicting orographic precipitation during high-impact precipitation events in the Sierra Nevada, and the sensitivity of that skill to the choice of the microphysical parameterization and horizontal resolution are examined. The performance of four existing microphysical schemes in the PSU/NCAR Mesoscale Model MM5 was examined. The verification data set consists of ground precipitation measurements from a selected number of wintertime storms documented during the Sierra Co-operative Pilot Project (SCPP) in the 1980's. The results show a tendency for the existing microphysical schemes in MM5 to produce an over-prediction of precipitation on both the windward and lee slopes of the Sierra Nevada. Examination of the statistical skill scores reveals that the MM5 QPF skill for the Sierra Nevada orographic precipitation is fairly low, that the skill is not improved by increasing the horizontal resolution, and that on average the QPF skill is better on the windward than on the lee side. A statistical significance test showed that the differences in skill scores obtained for different microphysical schemes were not statistically significant.
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