{"type": "FeatureCollection", "features": [{"id": "10.1016/j.renene.2019.03.134", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:16:50Z", "type": "Journal Article", "created": "2018-09-04", "title": "Scoping the potential usefulness of seasonal climate forecasts for solar power management", "description": "Solar photovoltaic energy is widespread worldwide and particularly in Europe, which became in 2016 the first region in the world to pass the 100\u202fGW of installed capacity. As with all the renewable energy sources, for an effective management of solar power, it is essential to have reliable and accurate information about weather/climate conditions that affect the production of electricity. Operations in the solar energy industry are normally based on daily (or intra-daily) forecasts. Nevertheless, information about the incoming months can be relevant to support and inform operational and maintenance activities. This paper discusses a methodology to assess whether a seasonal climate forecast can provide a useful prediction for a specific sector, in this paper the European solar power industry. After evaluating the quality of the forecasts in providing probabilistic information for solar radiation, we describe how to assess their potential usefulness for a generic user by proposing an approach that takes into account not only their accuracy but also other potentially relevant factors. This approach is called index of opportunity and is then illustrated by presenting an example for the European solar power sector. The index of opportunity provides indications about where and when seasonal climate forecasts can benefit the decision-making in the photovoltaic sector. Even more importantly, it suggests an approach on how to evaluate their usefulness for the user's decision-making. This approach has the advantage of not limiting the definition of the usefulness only to the quality of the forecasts but rather considering, in an explicit way, all the factors that must be combined with the forecast's quality to define what is useful or not for the user.", "keywords": ["bepress|Physical Sciences and Mathematics", "330", "EarthArXiv|Physical Sciences and Mathematics|Environmental Sciences|Sustainability", "EarthArXiv|Physical Sciences and Mathematics|Environmental Sciences", "bepress|Physical Sciences and Mathematics|Earth Sciences", "02 engineering and technology", "EarthArXiv|Physical Sciences and Mathematics|Earth Sciences", "7. Clean energy", "01 natural sciences", "EarthArXiv|Physical Sciences and Mathematics", "Sustainability", "13. Climate action", "Physical Sciences and Mathematics", "Earth Sciences", "0202 electrical engineering", " electronic engineering", " information engineering", "bepress|Physical Sciences and Mathematics|Environmental Sciences", "bepress|Physical Sciences and Mathematics|Environmental Sciences|Sustainability", "Environmental Sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/149508/1/usefulness-climate-fcsts-solar-power.revised_3rd.pdf"}, {"href": "https://ueaeprints.uea.ac.uk/id/eprint/70701/1/Accepted_Manuscript.pdf"}, {"href": "https://doi.org/10.1016/j.renene.2019.03.134"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Renewable%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.renene.2019.03.134", "name": "item", "description": "10.1016/j.renene.2019.03.134", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.renene.2019.03.134"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-04T00:00:00Z"}}, {"id": "10.1016/j.renene.2021.02.003", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:16:50Z", "type": "Journal Article", "created": "2020-11-05", "title": "Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression", "description": "

We propose a data-driven model to predict the short-term fatigue Damage Equivalent Loads (DEL) on a wake-affected wind turbine based on wind field inflow sensors and/or loads sensors deployed on an adjacent up-wind wind turbine. Gaussian Process Regression (GPR) with Bayesian hyperparameters calibration is proposed to obtain a surrogate from input random variables to output DELs in the blades and towers of the up-wind and wake-affected wind turbines. A sensitivity analysis based on the hyperparameters of the GPR and Kullback-Leibler divergence is conducted to assess the effect of different input on the obtained DELs. We provide qualitative recommendations for a minimal set of necessary and sufficient input random variables to minimize the error in the DEL predictions on the wake-affected wind turbine. Extensive simulations are performed comprising different random variables, including wind speed, turbulence intensity, shear exponent and inflow horizontal skewness. Furthermore, we include random variables related to the blades lift and drag coefficients with direct impact on the rotor aerodynamic induction, which governs the evolution and transport of the meandering wake. In addition, different spacing between the wind turbines and W\u00f6hler exponents for calculation of DELs are considered. The maximum prediction normalized mean squared error, obtained in the tower base DELs in the fore-aft direction of the wake affected wind turbine, is less than 4%. In the case of the blade root DELs, the overall prediction error is less than 1%. The proposed scheme promotes utilization of sparse structural monitoring (loads) measurements for improving diagnostics on wake-affected turbines.

", "keywords": ["bepress|Physical Sciences and Mathematics|Physics|Engineering Physics", "engrXiv|Engineering|Risk Analysis", "engrXiv|Engineering|Other Engineering", "bepress|Engineering", "engrXiv|Engineering|Mechanical Engineering|Fluid Mechanics", "bepress|Engineering|Mechanical Engineering", "engrXiv|Engineering|Mechanical Engineering", "bepress|Engineering|Mechanical Engineering|Applied Mechanics", "Gaussian Process Regression", "02 engineering and technology", "7. Clean energy", "Virtual sensing", "wind turbine", "bepress|Engineering|Computational Engineering", "engrXiv|Engineering|Civil and Environmental Engineering", "0202 electrical engineering", " electronic engineering", " information engineering", "uncertainty", "Fatigue", "wake", "engrXiv|Engineering|Civil and Environmental Engineering|Structural Engineering", "Uncertainty", "engrXiv|Engineering|Mechanical Engineering|Applied Mechanics", "Bayesian Calibration", "engrXiv|Engineering|Engineering Physics", "bepress|Engineering|Risk Analysis", "engrXiv|Engineering", "bepress|Engineering|Civil and Environmental Engineering", "engrXiv|Engineering|Computational Engineering", "Wake", "bepress|Engineering|Aerospace Engineering|Aerodynamics and Fluid Mechanics", "bepress|Engineering|Civil and Environmental Engineering|Structural Engineering", "fatigue", "bepress|Engineering|Other Engineering", "Sensitivity analysis", "Wind turbine", "Bayesian Gaussian process regression"]}, "links": [{"href": "https://doi.org/10.1016/j.renene.2021.02.003"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Renewable%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.renene.2021.02.003", "name": "item", "description": "10.1016/j.renene.2021.02.003", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.renene.2021.02.003"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-05T00:00:00Z"}}, {"id": "1893/33794", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:19Z", "type": "Journal Article", "created": "2021-12-30", "title": "Global maps of soil temperature", "description": "Abstract

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2\uffc2\uffa0m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1\uffe2\uff80\uff90km2resolution for 0\uffe2\uff80\uff935 and 5\uffe2\uff80\uff9315\uffc2\uffa0cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1\uffe2\uff80\uff90km2pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse\uffe2\uff80\uff90grained air temperature estimates from ERA5\uffe2\uff80\uff90Land (an atmospheric reanalysis by the European Centre for Medium\uffe2\uff80\uff90Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\uffc2\uffb0C (mean\uffc2\uffa0=\uffc2\uffa03.0\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.1\uffc2\uffb0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.3\uffc2\uffb0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (\uffe2\uff88\uff920.7\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.3\uffc2\uffb0C). The observed substantial and biome\uffe2\uff80\uff90specific offsets emphasize that the projected impacts of climate and climate change on near\uffe2\uff80\uff90surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil\uffe2\uff80\uff90related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.