Calibrated Probabilistic Mesoscale Weather Field Forecasting: The Geostatistical Output Perturbation (GOP) Method

• Activity Number: 365
• Type: Invited
• Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
• Sponsor: JASA, Applications and Case Studies
• Author(s): Adrian E. Raftery*+
• Companies: University of Washington
• Keywords: climatology ; deterministic models ; prediction ; spatial correlation

Abstract

Probabilistic weather forecasting consists of finding a joint probability distribution for future weather quantities. It is typically done by using a numerical weather prediction model, perturbing the inputs to the model in various ways, and running the model for each perturbed set of inputs. The result is then viewed as an ensemble of forecasts, taken to be a sample from the predictive distribution. This is typically not feasible for mesoscale weather prediction carried out locally by organizations without the vast data and computing resources of national weather centers. Instead, we propose a simpler method which breaks with previous practice by perturbing the outputs, or deterministic forecasts, from the model. Forecast errors are modeled using a geostatistical model, and ensemble members are generated by simulating realizations of the geostatistical model. In an experiment with 48-hour temperature forecasts in the Pacific Northwest, our forecast intervals turned out to be empirically well calibrated, sharper than those obtained from approximate climatology, and consistent with the spatial correlation structure of the observations.