New Statistical Techniques for Probabilistic Weather Forecasting

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

Rational decision making by industries, agencies, and the public in anticipation of heavy precipitation, snow storm, flood or other disruptive weather phenomenon, requires information about the degree of confidence that the user can place in a weather forecast. It is vital, therefore, to advance the meteorologist's capability of quantifying forecast uncertainty to meet the society's rising expectations for reliable information. The long-term goal of this research is to lay down a methodological foundation for the next generation of probabilistic forecasting systems. The specific objective is to develop and test (i) a set of statistical techniques for probabilistic forecasting of weather variates and (ii) a set of performance measures for verification of probabilistic forecasts. 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. The techniques will harness recent advances in Bayesian statistical theory, multivariate distributions, estimation methods, and ensemble forecasting. Each technique 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 3 days ahead. The primary benchmark for evaluation of the new techniques will be the 30-year old Model Output Statistics (MOS) technique used currently in operational forecasting. The expected impacts of these new, state-of-the-art techniques for probabilistic forecasting will be improved forecasts of all major weather variates, and hence increased societal benefits. In particular, reliable and informative PQPFs, produced by the BPO or the BPE and suited to requirements of hydrologic models, will enable the production of probabilistic river stage forecasts, probabilistic flood forecasts, and flood warnings with explicitly stated detection probabilities.