[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,

High-resolution precipitation estimates from weather radar and radar-based precipitation forecasts are key inputs to hydrological applications and, in particular, to flood forecasting models. This paper examines the processes applied to the radar-measured reflectivity data from the UK weather radar network in order to derive products useful for hydrological applications. This starts with the quality control of the reflectivity scan data then looks at processes to convert the measured reflectivity into estimates of precipitation rate close to the ground. The approaches applied operationally at the UK Meteorological Office are compared with other operational approaches. In order to use radar data for hydrological applications, it is important to understand the likely error characteristics of the precipitation estimates. Two different approaches to representing this uncertainty are outlined. The first considers a quality index, formed by combining a number of different components, representing different sources of error, multiplicatively. The second approach considers the generation of ensemble precipitation estimates which represent the likely spread of errors. The use of the precipitation estimates in generating short-period probabilistic precipitation forecasts is discussed. The methodology adopted in the short-term ensemble prediction system is outlined. Characteristics of these radar products are illustrated with a precipitation event.

ABSTRACT:As the societal impacts of hazardous weather and other environmental pressures grow, the need for integrated predictions that can represent the numerous feedbacks and linkages between sub-systems is greater than ever. This was well illustrated during winter 2013/2014 when a prolonged series of deep Atlantic depressions over a 3 month period resulted in damaging wind storms and exceptional rainfall accumulations. The impact on livelihoods and property from the resulting coastal surge and river and surface flooding was substantial. This study reviews the observational and modelling toolkit available to operational meteorologists during this period, which focusses on precipitation forecasting months, weeks, days and hours ahead of time. The routine availability of high-resolution (km scale) deterministic and ensemble rainfall predictions for short-range weather forecasting as well as weather-resolving seasonal prediction capability represent notable landmarks that have resulted from significant progress in research and development over the past decade. Latest results demonstrated that the suite of global and high-resolution UK numerical weather prediction models provided excellent guidance during this period, supported by high-resolution observations networks, such as weather radar, which proved resilient in difficult conditions. The specific challenges for demonstrating this performance for high-resolution precipitation forecasts are discussed. Despite their good operational performance, there remains a need to further develop the capability and skill of these tools to fully meet user needs and to increase the value that they deliver. These challenges are discussed, notably to accelerate the progress towards understanding the value that might be delivered through more integrated environmental prediction.