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In this presentation we detail highlight results obtained from the research work within the European Horizon 2020 project Smart4RES (http://www.smart4res.eu). The project, which started in 2019 and runs until 2023, aims at a better modelling and forecasting of weather variables necessary to optimise the integration of weather-dependent renewable energy (RES) production (i.e. wind, solar, run-of-the-river hydro) into power systems and electricity markets. Smart4RES gathers experts from several disciplines, from meteorology and renewable generation to market- and grid-integration. It aims to contribute to the pathway towards energy systems with very high RES penetrations by 2030 and beyond, through thematic objectives including:Improvement of weather and RES forecasting, Streamlined extraction of optimal value through new forecasting products, data market places, and novel business models; New data-driven optimization and decision-aid tools for market and grid management applications; Validation of new models in living labs and assessment of forecasting value vs costly remedies to hedge uncertainties (i.e. storage). In this presentation we will focus on our results on models that permit to improve forecasting of weather variables with focus on extreme situations and also through innovative measuring settings (i.e. a network of sky cameras). Also results will be presented on the development of seamless approach able to couple outputs from different ensemble numerical weather prediction (NWP) models with different temporal resolutions. Advances on the contribution of ultra-high resolution NWPs based on Large Eddy Simulation will be presented with evaluation results on real case studies like the Rhodes island in Greece.When it comes to forecasting the power output of RES plants, mainly wind and solar, the focus is on improving predictability using multiple sources of data. The proposed modelling approaches aim to efficiently combine highly dimensionally input (various types of satellite images, numerical weather predictions, spatially distributed measurements etc.). A priority has been to propose models that permit to generate probabilistic forecasts for multiple time frames in a seamless way. Thus, the objective is not only to improve accuracy and uncertainty estimations, but also to simplify complex forecasting modelling chains for applications that use forecasts at different time frames (i.e. a virtual power plant - VPP- with or without storage that participates in multiple markets). Our results show that the proposed seamless models permit to reach these performance objectives. Results will be presented also on how these approaches can be extended to aggregations of RES plants which is relevant for forecasting VPP production.

Les maladies et ravageurs des cultures dépendent fortement des conditions météorologiques. Différents outils d’aide à la décision sont proposés pour prendre en compte ces conditions météorologiques dans la modélisation des dynamiques épidémiques et accompagner les agriculteurs dans leurs prises de décision. Néanmoins, l’écoulement atmosphérique est un phénomène chaotique et les prévisions météorologiques restent incertaines. Dans ce contexte, les centres de prévision météorologique, dont Météo-France, mettent en œuvre des systèmes de prévision probabiliste qui permettent d’accéder à une estimation de l’incertitude des conditions météorologiques prévues. Nous proposons de montrer l’intérêt d’utiliser ces prévisions probabilistes au travers d’outils d’aide à la décision et d’une évaluation des risques associés à cette décision pour la protection des cultures, avec des applications sur la vigne et le blé., Crop diseases and pests are highly dependent on weather conditions. Various decision support systems a re proposed to take into account these meteorological conditions in the modeling of epidemic dynamics and to assist farmers in their decision-making. Nevertheless, the atmospheric flow is a chaotic phenomenon and weather forecasts remain uncertain. In recent years, weather prediction centers, including Météo-France, have implemented probabilistic prediction systems that provide an estimate of the uncertainty of the weather forecasts. We propose to demonstrate the potential of using these probabilistic forecasts through decision support tools used in an operational way for the protection of crops with applications to the wine and wheat.

International audience; Background-error variances estimated from a small-size ensemble of data assimilations need to be filtered because of the associated sampling noise. Previous studies showed that objective spectral filtering is efficient in reducing this noise, while preserving relevant features to a large extent. However, since such filters are homogeneous, they tend to smooth small-scale structures of interest. In many applications, nonlinear thresholding of wavelet coefficients has proved to be an efficient technique for denoising signals. This algorithm proceeds by thresholding the wavelet coefficients of the noisy signal using an estimated threshold. This is equivalent to applying an adaptive local spatial filtering. A quasi-optimal value for the threshold can be computed from the noise variance. We show that the statistical properties of the sampling noise associated with the estimation of background-error variances can be used to calculate the noise level and the appropriate threshold value. This method is first applied to 1D academic examples, with emphasis on correlated and heterogeneous noises. This approach is shown to outperform the commonly used homogeneous filters, since it automatically adapts to the local structure of the signal. We also show that this technique compares favourably to a heterogeneous diffusion-based filter, with the advantage of requiring less trial-and-error tuning. These results are next confirmed in a more realistic 2D problem, using the Arome-France convective-scale model.