A seamless prediction of convective precipitation for a continuous range of lead times from 0-8 h requires the application of different approaches. Here, a nowcasting method and a high-resolution numerical weather prediction ensemble are combined to provide probabilistic precipitation forecasts. For the nowcast, an existing deterministic extrapolation technique was modified by the local Lagrangian method to calculate the probability of exceeding a threshold value in radar reflectivity. Numerical forecasts were obtained from an experimental high-resolution ensemble that provides 20 different deterministic forecasts of synthetic radar reflectivity. Probabilistic information was calculated by different approaches from the ensemble output. The probabilistic forecasts based on the ensemble were calibrated with the reliability diagram statistics method. The skill of the probabilistic nowcasts and forecasts was evaluated using three quality measures. Finally, a seamless probabilistic forecast was generated as an additive combination of nowcast and forecast, using a weighting function based on their relative skills. The skill of the seamless forecast was greater than or equal to that of the nowcast or ensemble forecast in all quality measures and at all lead times. Copyright c 2011 Royal Meteorological Society Key Words: short-range forecasting; blending; ensemble prediction; forecast calibration

Stochastic perturbations allow for the representation of small-scale variability due to unresolved physical processes. However, the properties of this variability depend on model resolution and weather regime. A physically based method is presented for introducing stochastic perturbations into kilometer-scale atmospheric models that explicitly account for these dependencies. The amplitude of the perturbations is based on information obtained from the model’s subgrid turbulence parameterization, while the spatial and temporal correlations are based on physical length and time scales of the turbulent motions. The stochastic perturbations lead to triggering of additional convective cells and improved precipitation amounts in simulations of two days with weak synoptic forcing of convection but different amounts of precipitation. The perturbations had little impact in a third case study, where precipitation was mainly associated with a cold front. In contrast, an unphysical version of the scheme with constant perturbation amplitude performed poorly since there was no perturbation amplitude that would give improved amounts of precipitation during the day without generating spurious convection at other times.

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This study explores the potential of regime‐dependent approaches to improve short‐range precipitation forecasts. Probabilistic forecasts have been generated from the radar nowcaster Radar Tracking and Monitoring (Rad‐TRAM) and the convection‐permitting weather prediction model of the Consortium for Small‐scale Modeling, Deutscher Wetterdienst (COSMO‐DE) using the neighbourhood method for a 99 day period during summer 2009. The convective adjustment time‐scale was used to classify the days of the investigated period into stratiform, equilibrium and non‐equilibrium convection regimes. The COSMO‐DE forecasts were calibrated using the reliability diagram method and blended with the nowcasts using an additive weighting, where the weighting function varies with lead time according to the time evolution of the conditional square root of ranked probability score (CSRR). The examination of two case studies showed large differences in the calibration and weighting functions for different regimes. Over the entire period, regime‐dependent calibration was found to produce large improvements in reliability in comparison with the uncalibrated forecasts; however, the results were only modestly better than with a single calibration function. The blending procedure successfully combined the nowcast and forecast information, in the sense that the blended forecast was as good as either of the two components, but there was no further gain expected from regime‐dependent weighting if regime‐dependent calibration had already been performed.

A probabilistic nowcasting technique based on the Local Lagrangian method is combined with probabilistic forecasts derived from a time-lagged convection-permitting model to produce seamless short-term probabilistic precipitation forecasts. The fraction, the neighbourhood and the mean method are used to derive probabilistic information from this eight member ensemble. The model-based forecasts are calibrated with the reliability diagram statistics method. The skill of the probabilistic nowcasts and forecasts is evaluated with three quality measures. Probabilistic model-based forecasts are found to outperform probabilistic radar-based nowcasts after 2.25-3.5 h. Weighting functions derived in a lead time dependent evaluation of forecast skill are used to combine nowcasts and forecasts additively. The resulting seamless blended forecasts maintain or exceed the skill of the respective best component. In comparison with similar studies, the application of the time-lagged approach increases the skill of the numerical forecasts and hence the blended forecasts.