By facilitating the execution of agriculture-related models in cloud and HPC this works demonstrated new features that allow the improvement of the involved processes in terms of technology combination. The technology pillars of this work follow the paradigm of European Union's initiative, Destine Earth (DE). Lesson learned in all three components (HPC, NWP, Cloud) of the developed data driven service are described and pathways for the creation of operational services are discussed.

Agrometeorological services often do not cover the last mile – not reaching, not being understood, nor being trusted by smallholder farmers living in remote areas. To help bridge this gap across the last mile, the workshop on effective communication of agrometeorological services took place during the EMS2022. This paper presents the outcomes and recommendations on how to bridge the gap between information providers and information users.

This research presents new maps of return values of maximum and minimum shade air temperatures at mean sea level, lowest 10 cm soil temperature and snow loading at 100 m above mean sea level for 50, 100 and 120-year return periods. These maps will be crucial to inform the design of buildings and civil engineering works such as roof patterns or bridges.

We have compared hourly precipitation totals measured by rain gauges installed and maintained by citizens against professional weather stations managed by the national weather services of Finland, Norway and Sweden. The manufacturer of the citizen rain gauges is Netatmo. Despite the heterogeneity of citizens' measurements, our results show that the two data sources are comparable with each other, though with some limitations. The results also show how to improve the accuracy of citizens' data.

Combining high-fidelity official meteorological observations with low-fidelity crowd-sourced data in a single climate or weather map is challenging because of the significant bias and noise in the low-fidelity data. In this work, we present a method to treat this bias and noise in a statistical framework. In addition, we show that we can make an additional improvement in the quality of the map when we add high-resolution land use information.

Atmospheric boundary layers are inherently unsteady and exhibit processes on multiple scales. A stochastic one-dimensional turbulence model is applied here to periodically forced Ekman flows. These flows are hard to model due to competing laminar and turbulent response mechanisms. It is shown that the model is able to capture parametric dependencies of the near-surface turbulence. The results consolidate improvements seen in a subgrid-scale application of the model within large-eddy simulations.

Sensitivity analyses for wind direction effects upon an ENVI-met microclimate model were performed for a heterogeneous urban study area. Significant temperature differences were found when forcing the model with constant N/E/S/W wind direction data. Best model performance was observed using measured wind direction forcing data. The results demonstrate that cooling effects of park areas are largely directional which is important for urban planning and design of climate change adaptation measures.

The weather information value chain is a framework that describes how information is produced, communicated, and used in an end-to-end warning system for weather and hazard monitoring. A project under the WMO aims to explore value chain approaches to describe and evaluate high-impact weather events. The project developed a template for high-impact weather event case study collection, which allows scientists and practitioners to assess the effectiveness of warning value chains.

We present a wind power forecasting procedure, that consists of a physics-based component generated at a central server, and a data-based component generated on-site the wind power plant (WPP). It provides blackout-robust data transmission to grid operators and high forecast reliability, especially in the very-short term horizon. This endeavor aims at employing WPPs for support in exceptional or critical grid situations, where short term decision making is most relevant.

In 2021, several weather disasters occurred in which conditions surpassed recorded extremes. Comparative analysis of the warnings issued for these disasters shows that the conditions were generally forecast but that lack of preparedness and/or communication failures led to loss of life in particularly vulnerable groups.

In this work, we present the most recent updates in the E-OBS gridded data set for daily mean wind speed over Europe. The data set is provided as an ensemble of equally likely realisations. In addition, we make a preliminary study into possible causes of the observed terrestrial wind stilling effect, such as local changes in surface roughness length. As one of the results, we do observe a terrestrial wind stilling effect, however, the trend varies locally over Europe.

The importance of the ABL studies is essential because we live and work in it. The presented study is based on long-term acoustic soundings data for the coastal atmosphere. Such data is of great importance for the study and the theoretical description of the atmospheric processes in similar complex terrain regions and can be used for validation of different NWP and prognostic air pollution models. Such data is critically needed for the development of improved ABL parametrization schemes.

The quality of the global reanalysis ERA5, the regional reanalysis COSMO-REA6 and a successor version (R6G2), the new Copernicus European Regional Re-Analysis (CERRA) and a regional downscaling simulation with COSMO-CLM (HoKliSim-De) is assessed for offshore wind farm planning in the German Exclusive Economic Zone (EEZ) of the North Sea. The quality is assessed using in-situ wind measurements at the research platform FINO1 and satellite-based data of the near-surface wind speed as reference.

This method generates probabilistic solar power forecasts of up to 30 min using artificial intelligence and fish eye images of the sky. We investigated the impact of parameters describing the variability of the solar power on the forecast performance and divided the solar power to its direct and diffuse components. The method achieves overall good performance, while variability parameters and dividing the solar power don't show any performance gain. However, training time can be decreased.