Abstract Details
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.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.