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.

Abstract. The simple scaling hypothesis is applied to the intensity-duration-frequency (IDF) description of rainfall. It is shown that the cumulative distribution function for the annual maximum series of mean rainfall intensity has a simple scaling property over the range 30 min to 24 hours and in some instances to 48 hours. This behavior is demonstrated through an examination of the scaling properties of the moments and the scaling of the parameters of an extreme value distribution fitted to the data. A simple analytical formula for the IDF relationship is proposed, which embodies the scaling behavior. Once the scaling parameter has been obtained for a gauge or set of gauges in a region, this formula enables the calculation of rainfall amounts, of a chosen return period and duration shorter than a day, directly from the information obtained from the analysis of daily data. IntroductionThe relationship between rainfall intensity, duration, and frequency has been of considerable interest to practicing engineers and hydrologists for over a century. Sherman [1905] where 0 and ,/ are phenomenological parameters to be estimated.Daily rainfall is by far the most accessible form of rainfall data, and long sequences of rainfall data at higher time resolution are still relatively rare despite the fact that the technology to record such data has been available since the 1880s. There is much to be gained therefore in developing a methodology that is able to use daily rainfall statistics to infer the IDF characteristics for short duration rainfall. One such attempt was made by Adamson [1981], who compiled the statistics for 2500 rain gauges in South Africa. For each of the stations, he listed (inter alia) the estimates of the 1-, 2-, 3-, and 7-day total rainfall occurring with return periods of 2-200 years. To make these useful for intervals of less than a day, he compiled a table of disaggregation coefficients for the coastal and inland regions of South Africa, based on digitized data, fitting a model of the type [Bell, 1969] referred to above and purporting to be independent of return periods. In contrast to the above treatments, which depend on curvefitting techniques, a natural source for theories regarding the rescaling of rainfall statistics is to be found in the scaling hypotheses popularized by Mandelbrot [1982] and Lovejoy and Schertzer [1985]. Burlando and Rosso [1996] in a pioneering paper sought to apply the scaling hypotheses to annual maximum series of rainfall depth. In their work the scaling and multiscaling properties of the statistical moments of rainfall depth of different duration were analyzed and a lognormal probability distribution was used to model its statistical properties.In the present paper it will be shown that based on the empirically observed scaling properties of rainfall and some general assumptions about the cumulative distribution function (CDF) for the annual maximum of the mean rainfall 335

Abstract. Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting, that is, very-short-range forecasting (0–6 h). The aim of pysteps is to serve two different needs. The first is to provide a modular and well-documented framework for researchers interested in developing new methods for nowcasting and stochastic space–time simulation of precipitation. The second aim is to offer a highly configurable and easily accessible platform for practitioners ranging from weather forecasters to hydrologists. In this sense, pysteps has the potential to become an important component for integrated early warning systems for severe weather. The pysteps library supports various input/output file formats and implements several optical flow methods as well as advanced stochastic generators to produce ensemble nowcasts. In addition, it includes tools for visualizing and post-processing the nowcasts and methods for deterministic, probabilistic and neighborhood forecast verification. The pysteps library is described and its potential is demonstrated using radar composite images from Finland, Switzerland, the United States and Australia. Finally, scientific experiments are carried out to help the reader to understand the pysteps framework and sensitivity to model parameters.

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[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,