We consider the problem of calibrated and sharp probabilistic forecasting

PROBABILISTIC WEATHER FORECASTING USING BAYESIAN MODEL AVERAGINGAdrian E. Raftery

University of Washington

Abstract

of a future meteorological quantity. By calibrated, we mean that if we

define a predictive interval, such as a 90% probability interval, then on

average in the long run, 90% of such intervals contain the true value. By

sharp, we mean that the distribution is more concentrated than forecast

distributions from climatology alone. Mass has developed an ensemble

mesoscale forecasting system based on a set of weather forecasting deterministic

simulation models. He has established a clear relationship between

between-model variability and forecast errors, but his forecast intervals

are generally not calibrated: they are too narrow. We apply Bayesian Model

Averaging to develop probability forecasts using Mass's ensemble. The theory

of Bayesian Model Averaging explains both of Mass's main empirical findings:

the spread-error relationship, and the fact that the intervals from the Mass

ensemble are too narrow on average. We develop and Bayesian Model Averaging

forecasts and apply them to Puget Sound winter temperatures. The resulting

forecasts are well calibrated and sharp.This is joint work with Tilmann Gneiting, Fadoua Balabdaoui and Michael

Polakowski. It was supported by the DOD MURI Program.