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Activity Number: 575
Type: Invited
Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #307179
Title: Statistical Postprocessing of Numerical Weather Predictions Using a Stochastic Advection-Diffusion Model
Author(s): Hans Rudolf Kunsch*+ and Fabio Sigrist and Werner A. Stahel
Companies: Seminar fur Statistik, ETH Zurich and Seminar fur Statistik, ETH Zurich and Seminar fur Statistik, ETH Zurich
Keywords: Space-time models ; Stochastic partial differential equations ; Numerical weather prediction ; Hierarchical Bayes methods ; Statistical postprocessing
Abstract:

Numerical weather prediction (NWP) models are capable of producing predictive fields at spatially and temporally high frequencies. They provide however only point forecasts, or in the case of ensemble forecasts,they are typically underdispersed. Statistical postprocessing serves to overcome these shortcomings. We present here a method which is able to take the space-time correlations of prediction errors from the NWP into account. For this we use a stochastic advection-diffusion partial differential equation (SPDE) whose parameters have a physical interpretation. We derive computationally efficient spectral methods to fit such a model on a dense grid, based on possibly censored and noisy observations of the solution at some grid points. The proposed model is applied to precipitation forecasts for northern Switzerland. Our postprocessed forecasts outperform the raw NWP predictions and they quantify prediction uncertainty.


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