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.

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Abstract. The ability of radar-rain gauge merging algorithms to precisely analyse convective precipitation patterns is of high interest for many applications, e.g. hydrological modelling, thunderstorm warnings, and, as a reference, to spatially validate numerical weather prediction models. However, due to drawbacks of methods like crossvalidation and due to the limited availability of reference data sets on high temporal and spatial scales, an adequate validation is usually hardly possible, especially on an operational basis. The present study evaluates the skill of very high-resolution and frequently updated precipitation analyses (rapid-INCA) by means of a very dense weather station network (WegenerNet), operated in a limited domain of the southeastern parts of Austria (Styria). Based on case studies and a longer-term validation over the convective season 2011, a general underestimation of the rapid-INCA precipitation amounts is shown by both continuous and categorical verification measures, although the temporal and spatial variability of the errors is -by convective nature -high. The contribution of the rain gauge measurements to the analysis skill is crucial. However, the capability of the analyses to precisely assess the convective precipitation distribution predominantly depends on the representativeness of the stations under the prevalent convective condition.

The lack or inadequate representation of uncertainties in the surface initial conditions (ICs) affects the quality of ensemble forecast, in particular the near surface temperature and precipitation. In this paper, a strategy for perturbing surface ICs in limited area model ensemble prediction system, noncycling surface breeding (NCSB) is proposed. The strategy combines short-range surface forecasts driven by perturbed atmospheric forcing and the breeding method for generating the perturbation to surface ICs. NCSB is implemented and tested in Aire Limitée Adaptation dynamique Développement InterNational-limited area ensemble forecasting (ALADIN-LAEF). Statistical verification demonstrates that the application of NCSB improves the ALADIN-LAEF 2m temperature and precipitation forecast. Positive impacts are also obtained for temperature and specific humidity in the lower atmosphere.

Although the quality of numerical ensemble prediction systems (EPS) has greatly improved during the last few years, these systems still show systematic deficiencies. Specifically, they are underdispersive and lack both reliability and sharpness. A variety of statistical postprocessing methods allows for improving direct model output. Since 2007, Aire Limité e Adaptation Dynamique Dé veloppement International Limited Area Ensemble Forecasting (ALADIN-LAEF) has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG), and its 2-m temperature model output subject to calibration. This work follows the approach of nonhomogeneous Gaussian regression (NGR) that addresses a statistical correction of the first and second moment (mean bias and dispersion) for Gaussian-distributed continuous variables. It is based on the multiple linear regression technique and provides a predictive probability density function (PDF) in terms of a normal distribution. Fitting the regression coefficients, a minimum continuous ranked probability score (CRPS) estimation has been chosen instead of the more traditional maximum likelihood technique. The use of high-resolution analysis data on a 1 km 3 1 km grid as training data improves the forecast skill in terms of CRPS by about 35%, especially on the local scale. The percentage of outliers decreases significantly without loss of sharpness. Sensitivity studies confirm that about half of the total improvement can be attributed to the effect of a bias correction. The training length plays a minor role, at least for the chosen verification period. A rescaling of the predictive PDF is important in order to obtain sharp forecasts, especially in the short range. Applying the same method to the global ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF) gives improvements of similar magnitude. However, the calibrated 2-m temperature of ALADIN-LAEF still remains slightly better than the 2-m temperature from calibrated ECMWF-EPS, which leads to the conclusion that statistical downscaling of EPS cannot replace dynamical downscaling. Finally, an advanced version of NGR, the so-called NGR-TD, which uses timeweighted averaging within minimum CRPS estimation, is able to yield a further improvement of about 5% in terms of the CRPS.

Abstract. The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale -Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational -Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convectionpermitting ensemble with higher-resolution AROME-EPS outperforms its mesoscale counterpart ALADIN-LAEF for precipitation forecasts. The positive impact is larger for the mountainous areas than for the lowlands. In particular, the diurnal precipitation cycle is improved in AROME-EPS, which leads to a significant improvement of scores at the concerned times of day (up to approximately one-third of the scored verification measure). Moreover, there are advantages for higher precipitation thresholds at small spatial scales, which are due to the improved simulation of the spatial structure of precipitation.

Abstract. The SAL (Structure, Amplitude, Location) method is used for verification of precipitation forecasts at horizontal grid spacings ranging from 2.5 km to 25 km, using a high-resolution 1 km precipitation analysis as a reference. The verification focuses on a summertime period with predominantly convective precipitation. The verification domain contains lowland as well as alpine areas. Evaluation of the individual SAL components shows that with regard to area mean values (A) the benefit of high resolutions models becomes apparent only in high impact weather situations. For the summertime period studied, the subjective impression of better structured precipitation fields (S ) in higher resolution models can generally be confirmed. The most significant improvement appears to be associated with explicit simulation of deep convection.

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