Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5–10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.

This paper presents the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which has been developed for use in mountainous terrain. Analysis and nowcasting fields include temperature, humidity, wind, precipitation amount, precipitation type, cloudiness, and global radiation. The analysis part of the system combines surface station data with remote sensing data in such a way that the observations at the station locations are reproduced, whereas the remote sensing data provide the spatial structure for the interpolation. The nowcasting part employs classical correlation-based motion vectors derived from previous consecutive analyses. In the case of precipitation the nowcast includes an intensity-dependent elevation effect. After 2-6 h of forecast time the nowcast is merged into an NWP forecast provided by a limited-area model, using a predefined temporal weighting function. Cross validation of the analysis and verification of the nowcast are performed. Analysis quality is high for temperature, but comparatively low for wind and precipitation, because of the limited representativeness of station data in mountainous terrain, which can be only partially compensated by the analysis algorithm. Significant added value of the system compared to the NWP forecast is found in the first few hours of the nowcast. At longer lead times the effects of the latest observations becomes small, but in the case of temperature the downscaling of the NWP forecast within the INCA system continues to provide some improvement compared to the direct NWP output.

Ensemble forecasts depend on representations of model uncertainties. Here, we introduce a model uncertainty representation where a novel approach is taken to the established methodology of perturbing model parameters. The Stochastically Perturbed Parametrizations (SPP) scheme applies spatially and temporally varying perturbations to 20 parameters and variables in the ECMWF IFS model. The perturbed quantities are chosen from the IFS parametrizations of (a) turbulent diffusion and subgrid orography, (b) convection, (c) clouds and large-scale precipitation, and (d) radiation. The perturbations are drawn from prescribed distributions. Numerous configurations of SPP are compared in experiments with the ECMWF ensemble forecasts at T L 399 resolution up to 15 day lead times. Halving the standard deviations of the perturbations considerably reduces the ensemble spread. Smaller variations of the standard deviations lead to minor changes to the ensemble spread. Experiments with different space and time correlations for the perturbations suggest optimal correlation scales of 2000 km and 72 h. SPP displays a lower skill for upper-air variables in the medium range than the current operational model uncertainty scheme Stochastically Perturbed Parametrization Tendencies (SPPT) for a given set of fixed initial-state perturbations.However, in short ranges the two schemes display similar skill. Moreover, verification against surface observations shows SPP is more skilful than SPPT in 2 m temperature for the first couple of forecast days. We show that the direct perturbation of cloud (and radiation) processes in SPP has a greater impact on radiative fluxes than the indirect perturbation via SPPT. SPP also produces a better model climate for a range of variables when comparing long model integrations with the two schemes, indicating the potential advantage of a physically consistent model uncertainty representation. A comparison of the tendency perturbations introduced by SPP and SPPT suggests that the two schemes represent different aspects of model uncertainty.

This study applies statistical postprocessing to ensemble forecasts of near‐surface temperature, 24 h precipitation totals, and near‐surface wind speed from the global model of the European Centre for Medium‐Range Weather Forecasts (ECMWF). The main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the postprocessed forecasts. Reliability and sharpness, and hence skill, of the former is expected to improve over time. Thus, the gain by postprocessing is expected to decrease. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations, we generate postprocessed forecasts by ensemble model output statistics (EMOS) for each station and variable. Given the higher average skill of the postprocessed forecasts, we analyze the evolution of the difference in skill between raw ensemble and EMOS. This skill gap remains almost constant over time indicating that postprocessing will keep adding skill in the foreseeable future.

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Snow cover properties have a large impact on the partitioning of surface energy fluxes and thereby on near‐surface weather parameters. Snow schemes of intermediate complexity have been widely used for hydrological and climate studies, whereas their impact on typical weather forecast time scales has received less attention. A new multilayer snow scheme is implemented in the European Centre for Medium‐range Weather Forecasts Integrated Forecasting System and its impact on snow and 2‐m temperature forecasts is evaluated. The new snow scheme is evaluated offline at well‐instrumented field sites and compared to the current single‐layer scheme. The new scheme largely improves the representation of snow depth for most of the sites considered, reducing the root‐mean‐square error averaged over all sites by more than 30%. The improvements are due to a better description of snow density in thick and cold snowpacks, but also due to an improved representation of sporadic melting episodes because of the inclusion of a thin top snow layer with a low thermal inertia. The evaluation of coupled 10‐day weather forecasts shows an improved representation of snow depth at all lead times, demonstrating a positive impact at the global scale. Regarding the impact on weather parameters, the multilayer snow scheme improves the simulated minimum 2‐m temperature, by decreasing the positive bias and improving the amplitude of the diurnal cycle over snow‐covered regions. However, the increased variability of the 2‐m temperature can have a detrimental impact in regions characterized by preexisting errors in the daily mean temperature, associated with errors in cloud processes or surface albedo.

Abstract. Quantifying the uncertainty of flood forecasts by ensemble methods is becoming increasingly important for operational purposes. The aim of this paper is to examine how the ensemble distribution of precipitation forecasts propagates in the catchment system, and to interpret the flood forecast probabilities relative to the forecast errors. We use the 622 km 2 Kamp catchment in Austria as an example where a comprehensive data set, including a 500 yr and a 1000 yr flood, is available. A spatially-distributed continuous rainfall-runoff model is used along with ensemble and deterministic precipitation forecasts that combine rain gauge data, radar data and the forecast fields of the ALADIN and ECMWF numerical weather prediction models. The analyses indicate that, for long lead times, the variability of the precipitation ensemble is amplified as it propagates through the catchment system as a result of non-linear catchment response. In contrast, for lead times shorter than the catchment lag time (e.g. 12 h and less), the variability of the precipitation ensemble is decreased as the forecasts are mainly controlled by observed upstream runoff and observed precipitation. Assuming that all ensemble members are equally likely, the statistical analyses for five flood events at the Kamp showed that the ensemble spread of the flood forecasts is always narrower than the distribution of the forecast errors. This is because the ensemble forecasts focus on the uncertainty in forecast precipitation as the dominant source of uncertainty, and other sources of uncertainty are not accounted for. However, a number of analyses, including Relative Operating Characteristic diagrams, indicate that the ensemble spread is a useful indicator to assess potential forecast errors for lead times larger than 12 h.

Air temperature data from five enclosed limestone sinkholes of various sizes and shapes on the Hetzkogel Plateau near Lunz, Austria (1300 m MSL), have been analyzed to determine the effect of sinkhole geometry on temperature minima, diurnal temperature ranges, temperature inversion strengths, and vertical temperature gradients. Data were analyzed for a non-snow-covered October night and for a snow-covered December night when the temperature fell as low as Ϫ28.5ЊC. A surprising finding is that temperatures were similar in two sinkholes with very different drainage areas and depths. A three-layer model was used to show that the skyview factor is the most important topographic parameter controlling cooling for basins in this size range in nearcalm, clear-sky conditions and that the cooling slows when net longwave radiation at the floor of the sinkhole is nearly balanced by the ground heat flux.