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), will process output from a numerical weather prediction (NWP) model and optimally fuse it with climatic data in order to quantify uncertainty about a predictand. The extended technique, called Bayesian Processor of Ensemble (BPE), will process an ensemble of the NWP model output (or multiple models outputs). 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, or conditional on an ensemble of realizations. The challenge is to develop and test techniques suitable for operational forecasting.

Each technique, the BPO and the BPE, will be developed and tested in three versions, for (i) binary predictands (e.g., indicator of precipitation occurrence), (ii) multi-category predictands (e.g., indicator of precipitation type), and (iii) continuous predictands (e.g., precipitation amount conditional on precipitation occurrence, temperature, visibility, ceiling height, wind speed). The primary test will involve 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 will be 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 first version (for binary predictands) as a demonstration vehicle. It will also set the stage for a report of results from the first test of the BPO in which probabilities of precipitation occurrence are produced for selected stations in the contiguous U.S. — the subject of a companion talk at this conference.