University of Washington - Department of Statistics
Probabilistic weather forecasting is becoming an increasingly important and active area of research. Most current statistical post-processing techniques account for forecast bias and predictive variance without regard to forecast location. We will discuss a technique that adjusts bias and predictive variance locally, called geostatistical model averaging (GMA). In particular, GMA allows the parameters of the predictive distribution to vary over the model grid. Results using a temperature dataset in the Pacific Northwest will be provided, as well as a discussion of extensions of the GMA technique to probabilistic precipitation prediction.