Bayesian Processor of Output: A New Technique for Probabilistic Weather Forecasting

A coherent set of theoretically-based techniques is being developed for probabilistic forecasting of weather variates. The basic technique, called Bayesian Processor of Output (BPO), processes output from a numerical weather prediction (NWP) model and optimally fuses it with climatic data in order to quantify uncertainty about a predictand. As is well known, Bayes theorem provides the optimal theoretical framework for fusing information from different sources and for obtaining the probability distribution of a predictand, conditional on a realization of predictors. The challenge is to develop and test techniques suitable for operational forecasting.

Two versions of the BPO have already been developed and tested; they are for (i) binary predictands (e.g., indicator of precipitation occurrence), and (ii) continuous predictands (e.g., precipitation amount conditional on precipitation occurrence, temperature, visibility, ceiling height, wind speed). The primary test involves the production and verification of probabilistic quantitative precipitation forecasts (PQPFs) for up to three days ahead. The primary benchmark for evaluation of the new techniques is the Model Output Statistics (MOS) technique used currently in operational forecasting by the National Weather Service.

This talk will give a tutorial introduction to the principles and procedures behind the BPO, using the version for continuous predictands as a demonstration vehicle. It will also set the stage for a report of results from the test of the BPO in which PQPFs are produced for selected stations in the contiguous U.S. — the subject of a companion talk at this conference.