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Short-range forecasts of precipitation fields are needed in a wealth ofagricultural, hydrological, ecological and other applications. Forecasts fromnumerical weather prediction models are often biased and do not provideuncertainty information. Here we present a postprocessing technique for suchnumerical forecasts that produces correlated probabilistic forecasts ofprecipitation accumulation at multiple sites simultaneously. The statisticalmodel is a spatial version of a two-stage model that represents thedistribution of precipitation by a mixture of a point mass at zero and a Gammadensity for the continuous distribution of precipitation accumulation. Spatialcorrelation is captured by assuming that two Gaussian processes driveprecipitation occurrence and precipitation amount, respectively. The firstprocess is latent and drives precipitation occurrence via a threshold. Thesecond process explains the spatial correlation in precipitation accumulation.It is related to precipitation via a site-specific transformation function, soas to retain the marginal right-skewed distribution of precipitation whilemodeling spatial dependence. Both processes take into account the informationcontained in the numerical weather forecast and are modeled as stationaryisotropic spatial processes with an exponential correlation function. Thetwo-stage spatial model was applied to 48-hour-ahead forecasts of dailyprecipitation accumulation over the Pacific Northwest in 2004. The predictivedistributions from the two-stage spatial model were calibrated and sharp, andoutperformed reference forecasts for spatially composite and areally averagedquantities.

Probabilistic quantitative precipitation field forecasting using a two-stage spatial model