PROBABILISTIC WEATHER FORECASTING

Tilmann Gneiting

University of Washington

Probabilistic weather prediction consists of finding joint probability distributions of future weather quantities or events. Information about the uncertainty of weather forecasts can be important for decision-makers (e.g., public transportation authorities, airlines, shipping, and the military), as well as the public, but currently is routinely provided only for the probability of precipitation, and not for other weather quantities such as temperature, wind speed, or the amount of precipitation. It is typically done by using a numerical weather prediction model, perturbing the inputs to the model (initial conditions, lateral boundary conditions, and physics parameters) in various ways, and running the model forwards for each perturbed set of inputs. The result is then viewed as an ensemble of forecasts, taken to be a sample from the joint probability distribution of future weather events. However, forecast ensembles are typically biased and underdispersive, and there is a need for statistical postprocessing.

We first consider probabilistic forecasting of a single weather quantity, such as the temperature at a given place in 48 hours time.

We propose a statistical method for postprocessing ensemble output that is based on Bayesian Model Averaging (BMA), which is a standard method for combining predictive distributions from different sources. We next consider probabilistic forecasting of an entire weather field. We introduce a simple method, the Geostatistical Output Perturbation (GOP) technique, which breaks with much previous practice by perturbing the outputs, rather than the inputs, from the numerical weather prediction model, using a geostatistical approach. The two techniques can be combined into the Spatial BMA method.

We apply these methods to obtain probabilistic forecasts of surface temperature and quantitative precipitation over the Pacific Northwest, with good verification results. The forecasts are disseminated to the public in real time via the world wide web, at probcast.washington.edu.

This is joint work with Adrian E Raftery, Veronica Berrocal, McLean Sloughter, Chris Fraley, Patrick Tewson (University of Washington), Yulia Gel (University of Waterloo), Fadoua Balabdaoui (Paris–Dauphine) and Michael Polakowski (Oregon State University).