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This repository contains the necessary data and notebooks to replicate the results and analyses carried out in the paper "PyPVRoof: a Python package for the modular extraction of minimal metadata of rooftop PV installations". This repository is self-sufficient to replicate the models and results. The dataset is organized as follows: characteristics/ root folder for the replication of the results /CSV: contains the `.csv` files necessary to train and evaluate the methods /Data: main folder for the data /BDAPPV: folder containing the BDAPPV dataset /DSFrance: folder containing auxiliary data to run the methods on the outputs of DeepPVMapper /LiDAR: folder containing a sample of LiDAR images for evaluation /Yann: folder containing auxiliary data and annotations. /models: folder containing the weights of the models /notebooks: folder containing the Jupyter notebooks in which the models are defined and evaluated. Methods_Static_Analysis.ipynb is the main notebook from which the results can be replicated Characteristics_Extraction_Methods.ipynb is the notebook in which the methods are gathered and defined. The other notebooks can be used to generate the models using the data located in the /Data folder. /scalers: folder containing the scaler for the random forest defined for tilt and azimuth estimation env.yml: use this file to defined a virtual environment from which the notebooks can be launched. hands-on/ Root folder to run the hands-on notebook from the repository, accessible here :https://github.com/gabrielkasmi/characteristics/blob/main/hands-on.ipynb metadata.csv: a file containing ground truth information for installations located in France. This dataset is filtered from the BDAPPVdataset. Localizations were added based on the information on the departement. arrays_69.geojson. A raw file coming from DeepPVMapper' first step (detection and segmentation). This file is the output of the hands-on notebook of DeepPVMapper. You can access the repository here:https://github.com/gabrielkasmi/deeppvmapper README.md : the Readme file. &nbsp

This repository contains the necessary data and notebooks to replicate the results and analyses carried out in the paper "PyPVRoof: a Python package for the modular extraction of minimal metadata of rooftop PV installations". The project repository is accessible here: https://github.com/gabrielkasmi/pypvroof The paper is accessible here: This repository is self-sufficient to replicate theresults. This repository also hosts the data necessary to run the notebook hands-on.ipynbfrom the public repository of the project. The dataset is organized as follows: characteristics/ root folder for the replication of the results /CSV: contains the `.csv` files necessary to train and evaluate the methods /Data: main folder for the data /BDAPPV: folder containing the data stemming from BDAPPV. /DSFrance: folder containing auxiliary data to run the methods on the outputs of DeepPVMapper /LiDAR: folder containing a sample of LiDAR images for evaluation /Yann: folder containing auxiliary data and annotations. /models: folder containing the weights of the models /notebooks: folder containing the Jupyter notebookin which the methods are evaluated. Characteristics_Extraction_Methods.ipynb is the notebook in which the methods are gathered and defined. The other notebooks can be used to generate the models using the data located in the /Data folder. /scalers: folder containing the scaler for the random forest defined for tilt and azimuth estimation env.yml: use this file to defined a virtual environment from which the notebooks can be launched. README.md: the readme file hands-on/ Root folder to run the hands-on notebook from the repository, accessible here: : https://github.com/gabrielkasmi/pypvroof/blob/master/hands-on.ipynb bdappv-metadata.csv: a file containing ground truth information for installations located in France. This dataset is filtered from the BDAPPVdataset. Localizations were added based on the information on the departement. arrays_69.geojson. A raw file coming from DeepPVMapper' first step (detection and segmentation). This file is the output of the hands-on notebook of DeepPVMapper. You can access the repository here:https://github.com/gabrielkasmi/deeppvmapper lookup-table.json. The lookup table of DeepPVMapper tile.tif: a LiDAR raster as an example for the tilt and azimuth estimation using Theil-Sen algorithm&nbsp

The ASoRA method consists in estimating the solar resource in tower concentrated solar plants by exploiting global data sets. The solar resource parameters are not only the Direct Normal Irradiance (DNI) but also the slant path transmittance (Tsp). The column to surface-level conversion process was validated by comparing the retrieved aerosol extinction coefficient (AEC) to independent measurements performed by a ground-based nephelometer. The Aerosol Robotic NETwork (AERONET) provided column-integrated aerosol extinction properties. The European Centre for Medium-Range Weather Forecasts Reanalysis Interim boundary layer height was used to reproduce the seasonal dependence of the aerosol vertical profile. At the daily resolution, ASoRA reproduced observed AEC with a relative root mean square difference (rRMSD) of 33% and a correlation coefficient of 0.90. The spectral extrapolation from monochromatic measurements to the solar broadband was validated by comparing computations and observations of DNI. ASoRA was also able to reproduce the hourly variability of DNI observed by a pyrheliometer with a RMSD of 38 W/m2, a rRMSD of 4.6%, and a correlation coefficient larger than 0.98. Despite larger uncertainties caused by using Modern-Era Retrospective analysis for Research and Applications (version 2) instead of AERONET, the annual average of Tsp was underestimated by only 0.01, and the annual average of the irradiance loss in the slant path by only 4 W/m2. Largest impact on Tsp comes from the parameterisation of the aerosol vertical profile, and the annual average of Tsp was 0.945 at Ouarzazate (Morocco) in 2014, with an estimated uncertainty of 0.030.

The EU Copernicus Climate Change Service (C3S) has produced an operational climate service, called C3S Energy, designed to enable the energy industry and policy makers to assess the impacts of climate variability and climate change on the energy sector in Europe. The C3S Energy service covers different time horizons, for the past forty years and the future. It provides time series of electricity demand and supply from wind, solar photovoltaic and hydro power, and can be used for recent trends analysis, seasonal outlooks or the assessment of climate change impacts on energy mixes in the long-term.This paper introduces this dataset, with a focus on the design and validation of the energy conversion models, based on ENTSO-E energy data and the ERA5 climate reanalysis. Flexibility and coherence across all countries have been privileged upon models’ accuracy. However, the comparison with ENTSO-E data shows that the models provide plausible energy indicators and, in particular, allow to compare climate variability effects on power demand and generation in an homogenous approach all over Europe.

Climate simulations consistently show an increase in European near-surface air temperature by the late 21st century, although projections for near-surface wind speeds and irradiance differ between models, and are accompanied by large natural variability. These factors make it difficult to estimate the effects of physical climate change on power system planning. Here, the impact of climate change on future European power systems is estimated. We show for the first time how a set of divergent future power system scenarios lead to marked differences in Europe’s total energy balance (demand-net-renewable supply) by 2050, which dominate over the uncertainty associated with climate change (∼50% and ∼5% respectively). However, within any given power system scenario, national power systems may be subject to considerable impacts from climate change, particularly for seasonal differences between renewable resources (e.g., wind power may be impacted by ∼20% or more). There is little agreement between climate models in terms of the spatio-temporal pattern of these impacts, and even in the direction of change for wind and solar. More thorough consideration of climate uncertainty is therefore needed, as it is likely to be of great importance for robust future power system planning and design.

A wind turbine's power curve relates its power production to the wind speed it experiences. The typical shape of a power curve is well known and has been studied extensively; however, the power curves of individual turbine models can vary widely from one another. This is due to both the technical features of the turbine (power density, cut-in and cut-out speeds, limits on rotational speed and aerodynamic efficiency), and environmental factors (turbulence intensity, air density, wind shear and wind veer). Data on individual power curves are often proprietary and only available through commercial databases. We therefore develop an open-source model which can generate the power curve of any turbine, adapted to the specific conditions of any site. This can employ one of six parametric models advanced in the literature, and accounts for the eleven variables mentioned above. The model is described, the impact of each technical and environmental feature is examined, and it is then validated against the manufacturer power curves of 91 turbine models. Versions of the model are made available in MATLAB, R and Python code for the community., Comment: preprint submitted to Renewable Energy

Climate information is necessary for the energy sector. However, the use of climate projections has remained limited so far for a number of reasons such us the lack of consistency among climate projections, the inadequate temporal and spatial resolution, the climate model biases, the lack of guidance for users, and the size of data sets. In this work, we develop and assess a consistent ensemble of high time and space resolution climate projections that address these problems. First, a methodology for sub-ensemble selection is developed and proposed. Our ensemble dataset includes eleven 12 km-resolution EURO-CORDEX simulations of temperature, precipitation, wind speed and surface solar radiation on 3-hourly and daily time scales. These variables are bias-corrected for a more effective use into impact studies. The assessment of bias-corrected model simulations against observational data indicates reduced biases and increased coherence in projected changes among models compared to the raw climate projections. We provide a well-documented dataset for energy practitioners and decision-makers to facilitate the access and use of energy-relevant high-quality climate information in operation and planning. The new dataset is freely available via the Earth System Grid Federation (ESGF) platform. Keywords: EURO-CORDEX, Sub-ensemble selection, Bias-correction, Climate data, Energy sector

The development of successful climate services faces a number of challenges, including the identification of the target audience and their needs and requirements, and the effective communication of complex climate information, through engagement with a range of stakeholders. This paper describes how these challenges were tackled during the European Climatic Energy Mixes (ECEM) project, part of the Copernicus Climate Change Service (C3S), in order to deliver a pre-operational, proof-of-concept climate service for the European renewable energy sector. The process of iterative user engagement adopted in ECEM is described, from the initial presentation of the team’s first vision for such a service to support external stakeholders, through to evaluation of the final interactive tool for visualisation, data download and supporting documentation (the C3S ECEM Demonstrator). The outcomes of this evaluation are outlined, together with a retrospective reflection on the engagement and development process. The extent to which co-production and co-design were achieved in practice is assessed. The paper also highlights the distance travelled from the start to end of ECEM in terms of building capacity, developing a community of practice, and raising the Technology Readiness Level. The relevance of ECEM for the European climate services market is briefly considered, including the development of downstream commercial services which build upon the public C3S services. Keywords: Climate services, Sectoral information system, Renewable energy, User needs and requirements, Stakeholder engagement, Co-production

Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments.

In this paper we analyse and quantify the impact of North Atlantic Oscillation (NAO) on the daily hydropower generation in the European countries. We create a consistent dataset of hydropower generation using a machine- learning methodology, this activity is an output of the Copernicus Climate Change Service ECEM contract. The model is set up using generation data on 2015-2016, and then an artificial dataset was built over the period 1979- 2017, as if the installed capacity, that of 2017, had been the same all along the period, using reanalysis data for the meteorological variables. This synthetic dataset thus generated has been used to analyse the changes in national hydro-power generation (both for run-of-river and reservoir-based plants) be- tween years when NAO is positive and when it is negative, over Europe. The results show how the majority of countries in southern Europe experience a reduction in generation during NAO positive phases. In particular the Iberian peninsula shows a clear sensitivity with change of both the hydro-power generation typologies in the range -8% – -4% in terms of capacity factor. This work also shows the benefit of having a pan-European dataset of energy vari- ables spanning multiple decades to analyse the impact of climate phenomena on the European energy sector.

The interplay between energy and meteorology (based on its broad meaning of weather, water and climate) has been steadily growing. For this relationship to continue flourishing, a formal structure for stakeholders to interact effectively is required. The process of formation of the World Energy & Meteorology Council (WEMC), an organisation aimed at promoting and strengthening such a relationship, is discussed in this chapter. Such a process involves building many diverse relationships, something which has been happening over several years, alongside the adoption of more formal practices such as stakeholder surveys. While the focus of this chapter is clearly on WEMC, this process could be used as a stimulus for analogous activities in the broader energy and meteorology area, specifically those at the national and regional levels, as well as similar activities straddling diverse disciplines, such as those promoted by the Global Framework for Climate Services (GFCS).

Work at the nexus between energy and meteorology aims at integrating meteorological information into operational risk management and strategic planning for the energy sector, at all timescales, from long-term climate change and climate variability to shorter term local weather. Weather and climate risk management can be a powerful instrument for development—not only for building energy system resilience and thus mitigating the effects of adverse events but also for ensuring that opportunities for enhanced system efficiency are exploited. The collaboration between energy and meteorology has a long history but has recently been strengthening, particularly in response to the new challenges posed by climate change and the necessary development of low-carbon energy systems. An efficient integration of high-quality weather and climate information into energy sector policy formulation, strategic planning, risk management and operational activities now, more than ever, requires improved understanding and communication between energy and meteorology specialists and decision makers.

The energy sector significantly depends on weather and climate variability, which impacts both demand and supply, at all timescales. Over the next decades, climate change mitigation and adaptation will lead to an overhaul in energy systems, to reduce greenhouse gases emissions. Low carbon energy generation is key to facing this challenge, but its renewable part—mainly from wind, solar and hydro power—will even increase the exposure of the sector to weather and climate factors. Energy companies can assess their preparation to tackle the impact of weather volatility on their operations by running a weather-readiness assessment. This chapter provides an overview of the energy sector today, together with future scenarios and challenges. The weather-readiness concept is then presented in detail and demonstrates that stronger collaboration between the energy industry and the meteorological community is key to reducing the risks posed by climate variability and change, and allow a more effective integration of high-quality weather and climate information into energy sector activities, to better manage power systems on all timescales from a few days to several decades.

International audience; European Climatic Energy Mixes (ECEM) is a Copernicus Climate Change Service (C3S) activity which isdeveloping, in close collaboration with the energy sector, a proof-of-concept model – or demonstrator. Its purposeis to enable the energy industry and policy makers to assess how well energy supply will meet demand in Europeover different time horizons, focusing on the role climate has on energy supply and demand.This presentation sums up how energy time series were modelled and computed at country level and dailytime step for the whole Europe.Bias-adjusted Essential Climate Variables from ERA-Interim over a European domain for 1979-2016 were used tocalculate electricity demand and generation from wind, solar and hydro power. Different strategies were adopteddepending on the target variable, and the availability of measured data to calibrate and validate the energy models.For demand, individual models were set up for each country, using a general approach based on GeneralizedAdditive Models. Hydro power generation could not be modelled precisely using a physical model at plant level,due to the lack of a plants database; hence a simplified statistical model approach was chosen, and gives goodresults. Wind and solar power generation were modelled with both statistical and physical models.Results of these different models and approaches will be presnted, and compared to actual measurements orestimations of electricity demand and supply. Comparison will also be made with other projects.We will then present how the produced time series are integrated into the ECEM demonstrator, and howsuch information can be exploited to analyze and understand the supply/demand balance problem at the Europeanscale. We will also discuss some critical needs for the further development of such services, including theavailability of energy data.

International audience; ECEM is a Copernicus Climate Change Services project focused on the role climate has on the European energymix (supply/demand).Climate data is converted into energy variables (energy demand and production for renewable energy sources) at country-level using statistical and physical models. Energy dataset have been gathered from a wide range of sources (e.g. ENTSO-E data and individual countries TSOs’ data) to build a common database to be used to analyse the relationship between climate information and the energy sector. For the climate side, ERA-INTERIM reanalysis has been used for the essential climate variables after a bias-correction procedure. Modelling results reveal the uniqueness of each national power system, underlining the need of a diverse set of modelling methodologies. In fact, both physical and statistical models have been used, calibrated with all the available observed climate and energy data. Results demonstrate the good performance of the climate data as predictors for the target energy variables: cross-validation error for daily national electricity demand is below 2.5% while for RES generation it is commonly below 10%. Furthermore, the work has highlighted the need of high-quality and complete metadata to maximise the impact of the produced data on the targeted user community.

By scattering and absorbing solar radiation, aerosols generate production losses in solar plants. Due to the specific design of solar tower plants, solar radiation is attenuated not only in the atmospheric column but also in the slant path between the heliostats and the receiver. Broadband attenuation by aerosols is estimated in both the column and the slant path for Ouarzazate, Morocco, using spectral measurements of aerosol optical thickness (AOT) collected by AERONET. The proportion of AOT below the tower’s height is computed assuming a single uniform aerosol layer of height equal to the boundary layer height computed by ECMWF for the Operational Analysis. The monthly average of the broadband attenuation by aerosols in the slant path was 6.9±3.0% in August 2012 at Ouarzazate, for 1-km distance between the heliostat and the receiver. The slant path attenuation should be added to almost 40% attenuation along the atmospheric column, with aerosols in an approximate 4.7-km aerosol layer. Also, around 1.5% attenuation is caused by Rayleigh and water vapour in the slant path. The monochromatic-broadband extrapolation is validated by comparing computed and observed direct normal irradiance (DNI). DNI observed around noon varied from more than 1000 W/m2 to around 400 W/m2 at Ouarzazate in 2012 because of desert dust plumes transported from North African desert areas.By scattering and absorbing solar radiation, aerosols generate production losses in solar plants. Due to the specific design of solar tower plants, solar radiation is attenuated not only in the atmospheric column but also in the slant path between the heliostats and the receiver. Broadband attenuation by aerosols is estimated in both the column and the slant path for Ouarzazate, Morocco, using spectral measurements of aerosol optical thickness (AOT) collected by AERONET. The proportion of AOT below the tower’s height is computed assuming a single uniform aerosol layer of height equal to the boundary layer height computed by ECMWF for the Operational Analysis. The monthly average of the broadband attenuation by aerosols in the slant path was 6.9±3.0% in August 2012 at Ouarzazate, for 1-km distance between the heliostat and the receiver. The slant path attenuation should be added to almost 40% attenuation along the atmospheric column, with aerosols in an approximate 4.7-km aerosol layer. Also, around 1.5% a...

T he growing body of knowledge and experience in weather and climate risk management in the energy industry has spurred a rapidly growing research interest at the nexus between weather, climate, and energy (Troccoli 2010; Troccoli et al. 2010; Ebinger and Vergara 2011; Marquis 2011). Although this increased attention has been stimulated by a renewed and fervent interest in renewable energy sources, weather and climate information is also critical to managing the energy supply from other energy sectors (e.g., offshore oil operations) as well as understanding and estimating energy demand. Until recently, such applied research was discussed in specialized sessions during conferences organized by industry-specific organizations related to wind energy (e.g., American Wind Energy Association), solar energy [e.g., Solar Power and Chemical Energy Systems (SolarPACES)], and meteorology (e.g., European Meteorological Society). To take advantage of the substantial overlap between these energy activities and their use of weather and climate information, the International Conference Energy and Meteorology (ICEM) 2011 (www.icem2011.org/) was convened in Queensland, Australia. The objective was to provide a dedicated forum where scientists, engineers, economists, policy makers, and other specialists and practitioners involved in research or implementation activities at the intersection between weather, climate, and energy could discuss recent research findings and emerging practices ranging from operational activities to longterm investment planning and to policy making. Such discussions were framed within the context of all energy sector uses, including climate change mitigation and weather/climate risk management practices. The four expected outcomes of this conference were as follows

International audience; The Copernicus Earth Observation Programme is a €4 billion project funded by the European Union providing free data and tools to understand environmental change. This abstract outlines how the European Climatic Energy Mixes (ECEM) project, a pilot project of the Copernicus Climate Change Service (C3S), one of six Copernicus services, is using climate data from the programme to support the wind industry. Approach The Copernicus Programme is a global network of thousands of land, air and marine based sensors, as well as a family of dedicated satellites, making millions of observations a day to build a comprehensive picture of the Earth’s climate. C3S will combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. Proof of concepts such as the ECEM are using this data to deve lop demonstrators, as part of the C3S pilot projects, to enable the wind industry to plan for and adapt to Europe’s changing climate. Main body of abstract The ability to use data from thousands of global sensors has a wide range of applications, from identifying prime locations for new wind turbine developments to mitigating climate risk by understanding how windstorms could impact the industry. C3S will help provide innovative solutions to challenges within the industry; in particular how to forecast wind yield and identify viable opportunities for wind development and storage investment within Europe’s generation mix. ECEM is a proof of concept climate service that is working to enable the energy industry and policy makers to assess how different energy supply mixes in Europe will meet demand over different time horizons, from seasonal to long-term planning spanning decades. By assessing the uncertainty of seasonal climate forecasts and projections, ECEM will inform climate-sensitive energy projects, such as wind, about their production under varying climate conditions and how they would meet demand at the European scale under differing energy generation scenarios. The presentation presents an assessment of wind speed and power as produced by essential historical datasets called reanalyses taking many wind speed measurements, including tower data, as reference. Two scaling methods are compared to calibrate the reanalysis, for both on-shore and offshore locations. This calibration is a key first step to providing a benchmark for climate model data such as climate forecasts and projections. An assessment of the physical coherence/co-variability between calibrated wind speed and other climate variables, such as solar radiation, is also carried out to achieve an optimal spatial and temporal co-variability, which is critical when attempting to achieve a balanced energy supply. Conclusion Harnessing data is integral to the future of the wind industry, from identifying the best locations to place turbines and target investment to ensuring that projects and infrastructure are sustainable as Europe’s climate continues to change. The calibration of wind speed as produced by a widely used re-analysis product, ERA-Interim, as the basis wind resource assessments, is discussed with this presentation. Many measurement stations, both on-shore and offshore, are used for this calibration. This work is also important in pointing to the dominant climate drivers influencing overall supply-demand balance at both national and continental scales, such as in the case of winter-time North Atlantic Oscillation, which can have a profound influence on wind power. The outcomes of ECEM’s work can help provide methods for the wind industry to plan its long-term future. Learning objectives • The ways in which energy supply and demand over Europe are affected by the spatial and temporal variations of their climate drivers; • How climate modelling can enable the wind industry to target development and investment.

Aerosols attenuate the solar radiation collected by solar tower plants (STP), along two pathways: 1) the atmospheric column pathway, between the top of the atmosphere and the heliostats, resulting in Direct Normal Irradiance (DNI) changes; 2) the grazing pathway close to surface level, between the heliostats and the optical receiver. The attenuation along the surface-level grazing pathway has been less studied than the aerosol impact on changes of DNI, while it becomes significant in STP of 100 MW or more. Indeed aerosols mostly lay within the surface atmospheric layer, called the boundary layer, and the attenuation increases with the distance covered by the solar radiation in the boundary layer. In STP of 100 MW or more, the distance between the heliostats and the optical receiver becomes large enough to produce a significant attenuation by aerosols. We used measured aerosol optical thickness and computed boundary layer height to estimate the attenuation of the solar radiation at surface level at Ouarzaz...

Les modeles climatiques sont adaptes a la representation de la circulation atmospherique de grande echelle et du climat moyen de grandes regions du monde. Le lien entre la circulation atmospherique de grande echelle, et plus precisement ses structures stables et recurrentes que sont les regimes de temps, et les conditions locales est utilise dans cette etude, d’une part dans un but de prevision saisonniere et d’autre part pour analyser l’impact du changement climatique. En ete, 4 regimes de temps sont identifies sur la zone Atlantique-Europe, et 2 d’entre eux sont associes a l’occurrence preferentielle de conditions chaudes et seches sur la France. Ces regimes ont ete particulierement frequents lors des etes caniculaires et un lien a pu etre mis en evidence entre des conditions tropicales anormales et l’occurrence de ces regimes chauds et secs, qui pourrait constituer un espoir de previsibilite des canicules. Par ailleurs, le changement climatique conduit a une augmentation de la frequence d’un des regimes chaud et sec et a la diminution d’occurrence du regime le plus froid et humide. Il se traduirait alors globalement par une frequence plus importante de canicules dans le futur, avec cependant des incertitudes importantes, notamment sur leur intensite.

The EDF group is the biggest French electric power producer and distributor. Its activities are greatly related to weather and climate. In particular, optimal management of the hydroelectric power production system requires a good forecast of water resources, from several days to several months in advance. Currently, only climatology at the seasonal timescale is used for operational production management. Seasonal probabilistic forecasts would improve watershed management at some months' lead-time if they are skilful enough. For this, two main problems have to be addressed: first, direct precipitation forecasts at this timescale have little, but positive, skill over Europe; second, the spatial scales of seasonal forecasting models are not adequate to predict local precipitation at the river basin scale. This study aims to evaluate the quality of seasonal forecasts of precipitation for 48 catchments in southern France. These are obtained by spatially downscaling global scale seasonal forecasts of geopotential height at 850 hPa. The method used is based on singular value decomposition and multiple linear regression. The statistical downscaling model is calculated from 45 years of observed local precipitation in the watersheds and geopotential fields from ERA40 re-analysis data. The statistical model is then applied to the seasonal hindcasts from the DEMETER project. Two main results arise from this work. First, we show that it is possible to obtain useful and valuable information for EDF at the local scale from global seasonal averaged information. Second, we find that only a probabilistic multi-model ensemble forecast approach provides useful information for EDF catchments, even with quite low skill, and that a deterministic approach, using only the ensemble mean of the forecasts, is not better than a forecast based on climatology. It has, nevertheless, to be pointed out that for operational purposes, being able to know that a forecast for a given location or date is not reliable is, in itself, valuable information.

Part I: Why Should the Energy Industry be Concerned About Weather Patterns?.- A new era for Energy.- Vulnerability of Energy Systems in a Changing Climate.- Climatic Changes: Looking Back, Looking Forward.- Renewable Energy and Climate Change Mitigation - an Overview of the IPCC Special Report.- Part II: How is the Energy Industry Meteorology-proofing Itself?.- Managing Weather and Climate Risks: a View from the Energy Sector.- Improving Resilience Challenges and Linkages of the Energy Industry in a Changing Climate.- Combining Meteorological and Electrical Engineering Expertise to Solve Energy Management Problems.- Weather and Climate Impacts on Australia's National Electricity Market.- Bioenergy, Weather and Climate Change in Africa: Leading Issues and Policy Options.- Part III: What can Meteorology offer to the Energy Industry?.- Weather and climate information delivery within national and international frameworks.- Meteorology and the energy sector.- Earth observations in support of the Energy sector.- Emerging Meteorological Requirements to Support High Penetrations of Variable Renewable Energy Sources: Solar Energy.- Current status and challenges in wind energy assessment.- Wind Power Forecasting.- Regional Climate Modelling for the Energy Sector.- In Search of the Best Possible Weather Forecast for the Energy Industry.- Part IV: How is the Energy Industry applying State-of-the-Science Meteorology?.- A probabilistic view of weather, climate and the energy industry.- Weather & climate and the power sector: needs, recent developments and challenges.- Unlocking the potential of renewable energy with storage.- Improving NWP Forecasts for the Wind Energy Sector.- Overview On Irradiance And Photovoltaic Power Prediction.- Spatial and temporal variability in the UK wind resource: Scales, controlling factors and implications for wind power output.- Reducing the energy consumption of existing, residential buildings, for climate change and scarce resource scenarios in 2050.- Energy & Meteorology: Partnership for the future.- Index.

Weather and climate information is essential to the energy sector. The power sector in particular has been using both observations and forecasts of many meteorological and hydrological parameters for several decades. In the last 10 years, a clear upward trend has been observed in the number, complexity, and value of data provided by National Meteorological and Hydrological Services (NMHSs) or produced by the energy sector itself. Much progress has been made, especially in the medium-term and longer time ranges; the development of reliable probabilistic forecasting systems has allowed many improvements in demand and production forecasts, although there is still a lot to do because of the difficulty in integrating probabilistic weather forecasts in management tools. In addition, the rise of renewable energy (RE) production systems, in particular wind and solar energy, has emphasized new needs for more accurate and reliable short-term forecasts, from real-time to a few days ahead. Rapid fluctuations in wind and solar radiation at local scale certainly raise a serious problem for the management of power grids. Significant and swift improvements in local forecasts, at hourly or even sub-hourly time step, become increasingly important and will be among the drivers for the large-scale development of RE systems. In this paper, we present some important results concerning monthly ensemble forecasts of temperature and river streamflows in France. We then point to the principal needs in weather forecasting associated with the development of RE. We also discuss the importance of collaboration and relationships between providers and users of weather, water, and climate information.

The energy sector is highly dependent on climate conditions, whatever the particular field of activity, the production means, and the time scale. In developed countries, it is probably one of the most important users of Earth observation products and weather and climate forecasts. Extreme events such as heat waves or cold waves, wind storms or floods can of course have dramatic consequences on the production means or the electrical grid of a country. But “normal” day to day weather variations also have an impact on load level and energy production, transport and distribution management, as well as energy prices. In addition to short-term and medium-term management processes, long-term supply planning and production units dimensioning require climate archive data and future climate scenarios. In order to manage the risks associated with weather and climate conditions on all time scales from a few minutes to a century, reliable weather forecasts and climate information — past, present and future — are therefore crucial to reduce the uncertainty in supply and demand forecasts, as well as market dynamics. Examples based on the French electricity sector are presented, and suggested paths of progress are put forward.

International audience; The energy sector is undergoing a major transformation. This transformation in the energy sector is taking place against a variable and changing climate. Given the weather-and climate-dependency of both renewable energy and demand (even in the case of large storage uptake), it is important to develop robust climate-based tools to advise energy planners and policy makers.The EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) is producing, in close collaboration with prospective users, a proof-of-concept climate service, or demonstrator, whose purpose is to enable the energy industry and policy makers to assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes.This presentation will provide details about the calibration of climate variables over the historic period (last ca. 40 years) derived from reanalyses that was necessary in order to reduce the bias in variables relevant to the energy sector, such as wind speed and solar radiation. The conversion of climate variables into energy variables for wind power, solar power and hydropower, by means of both statistical methods and physical methods wherepossible, and for various European countries is also presented. An assessment of seasonal climate forecasts for use by the energy sector over the European domain will also be discussed.

International audience; The energy sector is undergoing a major transformation. This transformation in the energy sector is taking place against a variable and changing climate. Given the weather-and climate-dependency of both renewable energy and demand (even in the case of large storage uptake), it is important to develop robust climate-based tools to advise energy planners and policy makers. This talk will describe how the EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) project can assist in this energy transformation. ECEM is producing, in close collaboration with prospective users, a proof-of-concept climate service, or demonstrator, whose purpose is to enable the energy industry and policy makers to assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes. Energy time series were modelled and computed at country level and daily time step for the whole of Eu-rope. Bias-adjusted Essential Climate Variables from ERA-Interim over a European domain for 1979-2016 produced by ECEM were used to calculate energy variables, namely electricity demand and generation from wind, solar and hydro power. Different approaches were adopted depending on the target variable, and the availability of measured data to calibrate and validate the energy models. Examples from the C3S ECEM Demonstrator – an interactive and visual tool which allows users to view and explore energy supply and demand profiles for each European country (and for ca 100 clusters) and generation type, as well as climate variables, in map and/or time series format – will be used to illustrate the power of climate services in supporting the energy industry. The C3S ECEM Demonstrator is being developed according to user needs and integrates the energy and climate variables being produced in C3S ECEM on historic, seasonal forecast and climate projection timescales. Data can also be downloaded directly from the Demonstrator. The presentation will conclude with an outlook for future developments of climate services for the energy sector, and what benefits they could provide to the energy industry and decision makers at different levels.