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

By Veronica J. Berrocal, Adrian E. Raftery, Tilmann Gneiting. 2008

102 citations recorded by the source corpus.

Abstract : Short-range forecasts of precipitation fields are required 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 describes the distribution of precipitation with 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 governs precipitation occurrence via a truncation. The second process explains the spatial correlation in precipitation accumulation. It is related to precipitation via a site-specific transformation function, so 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 forecasts of daily precipitation accumulation over the Pacific Northwest in 2004, at a prediction horizon of 48 hours. The predictive distributions from the two-stage spatial model were calibrated and sharp, and out-performed reference forecasts for spatially composite and areally averaged quantities.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a55d968ab6eddbb97ca2a2682c0654b54bec6102

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Citations in the corpus

Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes (2012, methodology): “Using a two-step procedure such as ours has proven effective for similar types of models [Berrocal et al., 2008; Kleiber et al., 2011, 2012].”
Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau (2017, background): “However, spatial correlation between the BMA weight often failed to consider during the BMA calculation, although they were present (Berrocal et al., 2008).”
Matérn Cross-Covariance Functions for Multivariate Random Fields (2010, background): “Gaussian Markov random fields provide an alternative that is particularly suited to lattice data (Besag 1974; Mardia 1988; Rue and Held 2005; Sain and Cressie 2007).”
Comparison of non-homogeneous regression models for probabilistic wind speed forecasting (2013, methodology): “Berrocal et al. (2008). The CRPS for the TN model and the GEV model is calculated as described in Thorarinsdottir and Gneiting (2010) and Friederichs and Thorarinsdottir (2012), respectively.”
Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics (2013, methodology): “The EMOS method presented so far only yields univariate predictive distributions, but approaches similar to the one by Berrocal et al. (2008) could be used to extend it to a multivariate distribution model which can be used to simulate calibrated and spatially consistent precipitation fields.”
Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds (2008, methodology): “Berrocal et al. (2007, 2008) consider probabilistic weather field forecasts of temperature and quantitative precipitation at multiple sites simultaneously, and assess them using some of the techniques considered in our paper.”