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December 2008 Probabilistic quantitative precipitation field forecasting using a two-stage spatial model
Veronica J. Berrocal, Adrian E. Raftery, Tilmann Gneiting
Ann. Appl. Stat. 2(4): 1170-1193 (December 2008). DOI: 10.1214/08-AOAS203


Short-range forecasts of precipitation fields are needed in a wealth of agricultural, hydrological, ecological and other applications. Forecasts from numerical weather prediction models are often biased and do not provide uncertainty information. Here we present a postprocessing technique for such numerical forecasts that produces correlated probabilistic forecasts of precipitation accumulation at multiple sites simultaneously.

The statistical model is a spatial version of a two-stage model that represents the distribution of precipitation by a mixture of a point mass at zero and a Gamma density for the continuous distribution of precipitation accumulation. Spatial correlation is captured by assuming that two Gaussian processes drive precipitation occurrence and precipitation amount, respectively. The first process is latent and drives precipitation occurrence via a threshold. The second process explains the spatial correlation in precipitation accumulation. It is related to precipitation via a site-specific transformation function, so as to retain the marginal right-skewed distribution of precipitation while modeling spatial dependence. Both processes take into account the information contained in the numerical weather forecast and are modeled as stationary isotropic spatial processes with an exponential correlation function.

The two-stage spatial model was applied to 48-hour-ahead forecasts of daily precipitation accumulation over the Pacific Northwest in 2004. The predictive distributions from the two-stage spatial model were calibrated and sharp, and outperformed reference forecasts for spatially composite and areally averaged quantities.


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Veronica J. Berrocal. Adrian E. Raftery. Tilmann Gneiting. "Probabilistic quantitative precipitation field forecasting using a two-stage spatial model." Ann. Appl. Stat. 2 (4) 1170 - 1193, December 2008.


Published: December 2008
First available in Project Euclid: 8 January 2009

zbMATH: 1168.62086
MathSciNet: MR2655654
Digital Object Identifier: 10.1214/08-AOAS203

Keywords: Discrete-continuous distribution , ensemble forecast , gamma distribution , latent Gaussian process , numerical weather prediction , power truncated normal model , probit model , Tobit model

Rights: Copyright © 2008 Institute of Mathematical Statistics


Vol.2 • No. 4 • December 2008
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