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.
Accurate spatial and temporal forecasts of fog and low cloud occurrence are still a challenging research topic due to the complexity of its physics. In the nowcasting range, observation-based techniques are often superior to numerical weather prediction (NWP) models and provide useful guidelines for operational forecasters. This article proposes a low stratus nowcasting scheme which merges station measurements, satellite data, a nowcasting technique and NWP data at a very high horizontal resolution (1 km). Case studies and a comprehensive validation over Austria reveal that the proposed approach of parameterizing sub-inversion cloudiness adds value especially in complex terrain and even for longer lead times due to the dynamic design of the method.
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 convection-permitting 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 is due to the improved simulation of the spatial structure of precipitation.
Abstract.A new approach to downscaling soil moisture forecasts from the seasonal ensemble prediction forecasting system of the ECMWF (European Centre for Medium-Range Weather Forecasts) is presented in this study. Soil moisture forecasts from this system are rarely used nowadays, although they could provide valuable information. Weaknesses of the model soil scheme in forecasting soil water content and the low spatial resolution of the seasonal forecasts are the main reason why soil water information has hardly been used so far. The basic idea to overcome some of these problems is the application of additional information provided by two satellite sensors (ASCAT and Envisat ASAR) to improve the forecast quality, mainly to reduce model bias and increase the spatial resolution. Seasonal forecasts from 2011 and 2012 have been compared to in situ measurement sites in Kenya to test this two-step approach. Results confirm that this downscaling is adding skill to the seasonal forecasts.
Klimaszenarien.AT is an initiative started by 8 scientific and climate service partners in Austria with the aim of developing new national climate scenarios for Austria until 2026 („ÖKS26“).  The main goal is to consider both, a sound scientific basis at the latest state of research and users’ specific requirements concerning climate data and information. Activities of the initiative are, to a large part, carried out in funded research projects. The workplan of Klimaszenarien.AT roughly defines two stages: The first phase (2021-2025) focuses on the generation of the regional climate scenario data , while the second phase (2023-2026) is dedicated to distillation and communication of climate information for stakeholders and users. Stage one uses multiple sources of climate projection and reference data from global to regional scales and addresses specific research topics, such as the understanding of atmospheric processes or the linkage of large- and regional-scale impacts of climate change, with a special focus on mountainous areas. Stage two focuses on the construction of climate information for various user contexts, in terms of main statements and on applicable formats for visualization and provision of the climate information. The initiative aims at serving the principle of use: The derived climate information shall not only be „useful“, i.e. reliable and relevant, but also „useable“, i.e. findable and accessible and, finally, „used“ by public, media, decision-makers and advanced users.  Hence, also the experiences of users with the predecessor, the Austrian „ÖKS15“ scenarios, are gathered within the framework of a comprehensive stakeholder process. The two phases are closely related to each other and overlap in time. The goal of this concept is that the final outputs, i.e. the generic scenario data as well as the information and products derived therefrom, are understood as the fruit of collaborative efforts by the various actors. The process of generating „ÖKS26“ is further related to a number of international activities, such as the (EURO-)CORDEX project and the D-A-CH-scenario project. The latter is a cooperation of the national weather services DWD (D), GeoSphere Austria (A) and MeteoSwiss (CH) with the aim to harmonize the new generations of national climate scenarios to the greatest possible extent and to avoid transborder inconsistencies.
Subseasonal predictions are gaining more and more attention and importance in many applications, e.g. agriculture or energy&consumption predictions. To bridge the gap between those two temporal horizons and their drivers is, however, a challenge. Several attempts have been made in recent years to improve the numerical weather predictions but they to come at a high computation cost resulting in coarse spatial resolutions.  In the past decade, significant advances were made in improving the S2S and seasonal prediction using mainly numerical weather prediction models (NWP) and in some cases climate models for generating the predictions. Recently, the application of these models in real time forecasting through the S2S Real-Time Pilot Initiative (Robbins et al., 2020) was evaluated and is ongoing. There are, however, drawbacks. Computational costs for performing one forecast cycle are high (RAM, storage, ensemble for uncertainty) and limit the spatial, and to some extent temporal, resolution which are currently roughly 1.5° in spatial and at most 6-hourly in temporal resolution. Both resolutions are not sufficient for small scale renewable production sites. To overcome this, post-processing can be applied using statistical and machine leraning methods. In this study, statistical (EPISODES, GMOS, SAMOS) and machine learning methods (U-net, random forest) are used to downscale and post-process the coarse subseasonal ensemble predictions for temperature and precipitation. The domain in centred on Austria with a spatial resolution of 1 km  using the INCA analysis as target fields. Evaluation against INCA and point observations show the skills of all methods and highlight the need for additional downscaling.
<p>Empirical-statistical downscaling (ESD) methods are sparing regarding computational costs compared to dynamical downscaling models. Due to this advantage ESD can be applied in a short time frame and in a demand-based manner. It enables, e.g., the creation of ensembles of downscaled climate projections, which can be assessed either as stand-alone data set or to enhance ensembles based on dynamical methods. This helps improve the robustness of climatological statements for the purpose of climate impact research.</p><p>EPISODES is an ESD method for the regionalisation of output of general circulation models (GSMs). The initial development of EPISODES has been done at Deutscher Wetterdienst (DWD) for the area of Germany. Results of EPISODES results of CMIP5 projections are available for public download at the Earth System Grid Federation (ESGF). In the meantime, EPISODES has been extended for the downscaling of climate predictions on different timescales (decadal, seasonal, sub-seasonal) to meet the needs of climate data users for high spatial resolution datasets. Furthermore, is has been applied to a number of CMIP6 global projections.</p><p>In co-operation with the Zentralanstalt f&#252;r Meteorologie und Geodynamik (ZAMG) EPISODES is currently further developed and adjusted for handling besides the German area also the Alpine region. In addition to the interest of ZAMG to carry out downscaling with EPISODES for Austria, the complete coverage of the catchment areas of the Rhine, Elbe and Danube is a common interest of this cooperation.</p><p>The presentation will give an overview of the current status of EPISODES, show results, and provide an insight into recent developments.</p>
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