A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator

The need for the development of a method for generating an ensemble of rain fall scenarios, which are conditioned on the observed rainfall, and its pla ce in the HYREX programme is discussed. A review of stochastic models for r ainfall, and rainfall forecasting techniques, is followed by a justificatio n for the choice of the Modified Turning Bands (MTB) model in this context. This is a stochastic model of rainfall which is continuous over space and time, and which reproduces features of real rainfall fields at four distinc t scales: raincells, cluster potential regions, rainbands and the overall o utline of a storm at the synoptic scale. The model can be used to produce s ynthetic data sets, in the same format as data From a radar. An inversion p rocedure for inferring a construction of the MTB model which generates a gi ven sequence of radar images is described. This procedure is used to genera te an ensemble of future rainfall scenarios which are consistent with a cur rently observed storm. The combination of deterministic modelling at the la rge scales and stochastic modelling at smaller scales, within the MTB model , makes the system particularly suitable for short-term forecasts. As the l ead time increases, so too does the variability across the set of generated scenarios.