Simple Search

Query:

Enter a query and click »Search«...

Format:

Display: entries per page entries

Zbl 1168.62086
Berrocal, Veronica J.; Raftery, Adrian E.; Gneiting, Tilmann
Probabilistic quantitative precipitation field forecasting using a two-stage spatial model. (English)
[J] Ann. Appl. Stat. 2, No. 4, 1170-1193 (2008). ISSN 1932-6157

Summary: 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. We present a postprocessing technique for such numerical forecasts that produces correlated probabilistic forecasts of precipitation accumulation at multiple sites simultaneously.\par 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 forecasts and are modeled as stationary isotropic spatial processes with an exponential correlation function.\par 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.

MSC 2000:: *62M20 Prediction, etc. (statistics)
62P12 Applications to environmental and related topics
62M30 Statistics of spatial processes
65C60 Computational problems in statistics
86A32 Geostatistics

Keywords: discrete-continuous distributions; ensemble forecasts; gamma distributions; latent Gaussian processes; numerical weather prediction; power truncated normal models; probit models; tobit models

Comment on this Item PDF MathML XML ASCII DVI PS BibTeX Online Ordering Article Journal Journal

	Scientific prize winners of the ICM 2010
	Overhang
	Lie groups, physics and geometry. An introduction for physicists, engineers and chemists.

Simple Search

Zentralblatt MATH Berlin [Germany]