SUMMARYAn ensemble-based probabilistic precipitation forecasting scheme has been developed that blends an extrapolation nowcast with a downscaled NWP forecast, known as STEPS: Short-Term Ensemble Prediction System. The uncertainties in the motion and evolution of radar-inferred precipitation fields are quantified, and the uncertainty in the evolution of the precipitation pattern is shown to be the more important. The use of ensembles allows the scheme to be used for applications that require forecasts of the probability density function of areal and temporal averages of precipitation, such as fluvial flood forecasting-a capability that has not been provided by previous probabilistic precipitation nowcast schemes. The output from a NWP forecast model is downscaled so that the small scales not represented accurately by the model are injected into the forecast using stochastic noise. This allows the scheme to better represent the distribution of precipitation rate at spatial scales finer than those adequately resolved by operational NWP. The performance of the scheme has been assessed over the month of March 2003. Performance evaluation statistics show that the scheme possesses predictive skill at lead times in excess of six hours.

[1] There are significant uncertainties inherent in precipitation forecasts and these uncertainties can be communicated to users via large ensembles that are generated using stochastic models of forecast error. The Met Office and the Australian Bureau of Meteorology developed the Short Term Ensemble Prediction System (STEPS) was developed to address these user requirements and has been operational for a number of years. The initial formulation of Bowler et al. (2006) has been revised and extended to improve the performance over large domains, to include radar observation errors, and to facilitate the combination of forecasts from a number of sources. This paper reviews the formulation of STEPS, discusses those aspects of the formulation that have proved most problematic and presents some solutions. The performance of STEPS nowcasts is evaluated using a combination of case study examples and statistical verification from the UK. Routine forecast verification demonstrates that STEPS is capable of producing near optimal blends of a rainfall nowcast and high resolution NWP forecast. It also shows that the spread of STEPS nowcast ensembles are a good predictor of the error in the control member (unperturbed) nowcast.Citation: Seed, A. W., C. E. Pierce, and K. Norman (2013), Formulation and evaluation of a scale decomposition-based stochastic precipitation nowcast scheme, Water Resour. Res., 49, 6624-6641,

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During the past decade hydrologists have become increasingly aware of the problems of fluvial flood prediction during periods of intense convection, particularly in urbanised catchments whose rainfall‐runoff responses tend to be rapid. Existing approaches to deterministic, short‐range rainfall prediction are often deficient in their treatment of convective precipitation because they cannot resolve individual convective clouds or effectively model their evolution. In 1994 the UK Met. Office established a joint R&D programme with the Environment Agency (responsible for flood prediction in England and Wales) to explore the benefits of an Object‐Oriented conceptual Model (OOM) of convection in the nowcasting of fluvial floods. This involved the development of an automated nowcasting system (GANDOLF) designed to run the OOM during episodes of air mass convection. This paper describes the structure and function of the GANDOLF system and compares the performance of the OOM with that of two other precipitation models routinely used by Thames Region of the Agency. Copyright © 2000 Royal Meteorological Society