The skill and potential value of fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) ensembles are evaluated for short-range (24 h) probabilistic quantitative precipitation forecasts over Arizona during the Southwest monsoon. The sensitivity of different ensemble constructs is examined with respect to analysis uncertainty, model parameterization uncertainty, and a combination of both. Model uncertainty is addressed through different cumulus and planetary boundary layer parameterizations and through stochastic forcing representative of a component of subgrid-scale uncertainty, in which a first-order autoregression model adds a stochastic perturbation to the Kain-Fritsch cumulus scheme and Medium-Range Forecast Model PBL scheme. The results indicate that the precipitation forecasts are skillful and may assist operational weather forecasters during the monsoon; however, the forecasts are highly dependent on the cumulus parameterization. The addition of a stochastic element in the Kain-Fritsch cumulus scheme produces a small increase in skill and dispersion. Ensembles that incorporate mixed physics and perturbed analyses are the most skillful. A simple cost-loss model reveals that the monsoon ensembles can aid decision makers. Operational application is demonstrated for a heavy rain event over southern Arizona.
|Original language||English (US)|
|Number of pages||21|
|Journal||Weather and Forecasting|
|State||Published - Oct 2002|
ASJC Scopus subject areas
- Atmospheric Science