Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast’s properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

Today's ensemble weather prediction systems provide reliable and sharp probabilistic forecasts—yet they are still rarely communicated to outside users because of two main worries: the difficulty of communicating probabilities to lay audiences and their presumed reluctance to use probabilistic forecasts. To bridge the gap between the forecasts available and their use in day‐to‐day decision making, we encourage scientists, developers, and end‐users to engage in interdisciplinary collaborations. Here, we discuss our experience with three different approaches of introducing probabilistic forecasts to different user groups and the theoretical and practical challenges that emerged. The approaches range from quantitative analyses of users' revealed preferences online to a participatory developer–user dialogue based on trial cases and interactive demonstration tools. The examples illustrate three key points. First, to make informed decisions, users need access to probabilistic forecasts. Second, forecast uncertainty can be understood if its visual representations follow validated best practices from risk communication and information design; we highlight five important recommendations from that literature for communicating probabilistic forecasts. Third, to appreciate the value of probabilistic forecasts for their decisions, users need the opportunity to experience them in their everyday practice. With these insights and practical pointers, we hope to support future efforts to integrate probabilistic forecasts into everyday decision making.

To improve the prediction of small-scale flooding events with high societal impacts that may result from heavy precipitation associated with severe convective storms, Deutscher Wetterdienst (DWD) is strengthening its collaboration with Germany´s flood forecasting authorities. With its focus on the seamless and probabilistic prediction of severe summertime convective events and associated heavy precipitation, DWD´s novel Seamless Integrated Forecasting System (SINFONY) is considered as a major step towards improving precipitation information for the forecast range from minutes to approx. 12 hours. To ensure a significant contribution of our newly developed SINFONY data and products for hydrological applications, in particular the prediction of flash floods, the SINFONY team is in a continuous dialogue with users from flood forecasting centres. One of the main objectives thereby is to identify user requirements and to utilize this information for targeted development of forecast products. In addition, users are supported in implementing those data and products in their models and decision processes. Starting from this year, the above-mentioned activities within SINFONY will be complemented by a research project for augmenting the hydrometeorological value chain through co-design. This project is jointly developed and conducted by DWD and operational German flood forecasting centres, and funded in the context of DWD´s new research Programme “Italia – Deutschland science-4-services network in weather and climate”. The activities within the project stretch across the value chain for the generation of hydrometeorological forecasts. They aim for i.) a user-oriented evaluation and optimization of DWD´s precipitation forecasts (SINFONY, ICON-D2-EPS,…); ii.) the implementation of functionalities and processes optimised to the needs of flood forecasting centres into the DWD´s new warning system; iii.) the evaluation of DWD (new) data and products within operational flood forecasting applications; vi.) the harmonisation of precipitation and flood warnings; v.) the improvement of the communication and perception of forecasts and warnings. With this poster presentation we will provide an overview of the ongoing and commencing activities described above. We are also interested in an exchange with participants of ECSS 2023, working on the intersection between meteorology and hydrology, to learn about their experiences.

We present ICON-waves, a new component of the ICOsahedral Non-hydrostatic (ICON) modeling system, designed to explicitly model ocean surface gravity waves and their feedbacks on the atmosphere and ocean within the Earth system. Until now, the ICON-NWP model, developed and routinely operated by the German Weather Service (DWD), has not fully captured the impact of wave-induced interactions. Waves influence the sea surface state, generate turbulence, modify ocean currents, and affect air-sea exchanges of heat, matter, and momentum. ICON-waves addresses these processes by providing a wave-spectrum-dependent interface within the ICON framework, enabling more realistic simulations of atmosphere-ocean interactions. The integration of ICON-waves represents a significant advancement in modeling the complexity of atmosphere-ocean feedbacks, offering potential benefits for weather and climate prediction. This presentation outlines the ICON-waves model, including its concept, wave physics, and its role in improving overall model physics. We demonstrate results from both stand-alone ICON-waves and coupled ICON-NWP-waves simulations, focusing on the effects of wave-dependent sea surface roughness in the coupled atmosphere-waves system.

Ensemble prediction systems have evolved as a standard in modern weather prediction. At the same time, with an increasing number of members and higher spatiotemporal resolution, the amount of data produced is growing fast. However, not all operational users are able to process the increasing amount of data, which calls for methods to reduce ensemble information.The SINFONY project at Deutscher Wetterdienst (DWD) focuses on the probabilistic forecasting and Nowcasting-NWP combination of precipitation, with the aim of better representing convective heavy rainfall events. Ensemble reduction of SINFONY output data is based on two concepts. Firstly, it aims to compact the information of an ensemble into a new, combined member (the “pseudo-deterministic” member). The second approach is to retain most of the information in a member-reduced ensemble that also contains the newly created pseudo-deterministic member. This postprocessing of an ensemble can be helpful to users that have limited computational power at their disposal but still want to benefit from the probabilistic information of the ensemble. Another application of the pseudo-deterministic product could be its visualisation as a map with locally the most probable precipitation scenario.The pseudo-deterministic member is defined as the locally most probable forecast of the ensemble identified by a k-means-based clustering of the members in limited areas and blending the resulting best local forecasts back together. This leads to a mixed forecast that is supposed to have higher statistical skill averaged over the model domain (here, Germany) compared to the weather model’s deterministic member. In addition, the clustering can also be used for the identification of precipitation scenarios that differ significantly from the locally most probable precipitation. As a result, a reduced ensemble can be derived that contains a large part of the forecast distribution. In this poster, we present and evaluate the developed algorithm for the ensemble reduction and explore how skill depends on the number of reduced ensemble members in convective weather situations. 

The energy sector has a wide range of requirements for weather and climate data and services. As in many other countries, the share of renewable energies has increased in Germany during the last years and the German government aims at a further increase within the next decades. As a consequence, there is an increasing need for meteorological information on several time scales. In this presentation, we will discuss the experience of Germany’s national meteorological service (Deutscher Wetterdienst, DWD) with the provision of user-oriented services for various stakeholders in the energy sector.DWD hosts several national and international climate data centres that provide observation-based climatological data suitable for assessments of the site-specific renewable energy potential, but also suitable for evaluating weather variability and the resulting risks of shortfall events or complementarity of energy resources (e.g. https://doi.org/10.1016/j.renene.2020.10.102). The datasets are derived from traditional weather observations, satellite data (e.g. https://www.cmsaf.eu) or model-based atmospheric reanalyses (e.g. https://doi.org/10.5194/asr-17-115-2020). Climate simulations can help to assess whether relevant changes in critical parameters are to be expected in the future. The debates in the recent winters about the risks of a shortage in gas availability have also led to an increased interest in seasonal forecasts.Weather forecasts are of particular interest for the daily operation of the energy system, the predictive balancing of production and consumption, the management of congestion and the weather dependent thermal rating of the overhead lines in the electrical grid. In close contact with our key user groups, DWD optimizes and co-designs forecast products and services tailored to the user needs. One special focus lies on the co-development of products that make use of ensemble forecast information and to foster its integration in the user’s decision process.DWD's open data policy (https://www.dwd.de/opendata) also supports the cooperation with this community. In addition to the national data, DWD’s international data centres also provide relevant datasets for Europe, or beyond (e.g. https://doi.org/10.3389/fclim.2021.815043).In the presentation we will discuss the results and lessons learned of the cooperation with our users and we will also provide information on products that may be of interest to other users.