Probabilistic Quantitative Precipitation Forecast for Flood Prediction: An Application

Abstract

This paper outlines a methodology to produce probabilistic quantitative precipitation forecasts by means of a dedicated uncertainty processor for weather model output. The uncertainty processor is developed as a component of a Bayesian forecasting system for river flow prediction. In this context the quantitative precipitation forecast is envisaged as a mixed binary–continuous predictand. The processor is applied to the quantitative precipitation forecasts and to precipitation observations covering a 5-yr period, whereby the forecasted and observed relative air humidity are used as ancillary meteorological indicators. The application of the processor to the selected dataset highlights a significantly larger skill of the quantitative precipitation forecast in predicting event occurrence rather than event depth and provides an objective quantification of the forecast uncertainty. The methodology applied here remains restricted to small basins, in which spatial variability of precipitation can be considered negligible. The need for processing the uncertainty induced by spatial variability of rainfall is briefly addressed.