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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/185754
High-quality observations for improved seasonal predictions. Implications for the wind energy sector
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[eng] Multiple initiatives are being implemented to mitigate and, in the worst-case scenarios, adapt society to climate change. The vast majority consider renewable energies key to accomplishing a necessary transition from fossil fuels to clean energies. The electricity system, in particular, is facing a significant transformation, being it more dependent on renewable production and, subsequently, on meteorological factors like wind speed or solar radiation. The prediction of anomalies of meteorological variables is well- established and trusted from minutes to days ahead, and so is the amount of renewable generation. Beyond those time scales, seasonal predictions start to produce beneficial results in anticipating the amount of generation months in advance, but their quality is still far from that offered by weather forecasts. In this regard, the climate community is advancing towards better seasonal predictions, both from the perspective of climate modelling and its post-processing.
To further increase the value of seasonal predictions, climate services have recently appeared to make climate information ---sometimes deemed challenging to digest--- more understandable and practical for non-experienced users. Climate services facilitate the integration of seasonal predictions into the renewable industry. The wind power industry, for example, employs seasonal predictions not only to advance the future availability of the wind resource but also to schedule maintenance activities in wind farms. A better understanding of the opportunities of seasonal predictions allows wind energy users to identify gaps and report specific needs. This PhD thesis looks into those user needs to improve the quality of seasonal predictions for wind speed.
More specifically, the enhancement of seasonal predictions is achieved from the perspective of wind observations. We first focus on wind records measured at tall meteorological towers, a non-standard type of climate data widely used within the wind industry. We identify, retrieve and collect climate records from 222 tall tower locations distributed worldwide. After unifying the data format and performing an exhaustive quality control, specifically designed for this type of wind data, we release the dataset under the name of The Tall Tower Dataset. The data collection is made publicly accessible through a data web portal. We later explore reanalysis datasets to quantify how they differ from the true observed wind speeds. We consider five global reanalyses and describe their agreements and discrepancies in representing surface wind speeds. By comparing reanalysis data against winds from The Tall Tower Dataset, we conclude that representativeness errors in reanalyses can be large sometimes, to the extent not to trust gridded estimates in specific areas. We also conclude that ERA5 shows the closest wind speed estimates to those observed at the tall towers.
Once wind observations are characterised, and their quality is ensured to be sufficiently high to produce robust results, they are used to enhance seasonal predictions. The hybrid seasonal forecasts provided in this work allow predicting near-surface wind speeds at a point scale ---e.g. wind farm location. Those forecasts rely on the information of the large-scale atmospheric circulation, summarised in the state of the four main Euro- Atlantic Teleconnections. In general, hybrid predictions show skill at lead times two and three, while dynamical predictions do not. Another aspect that is improved is the skill
assessment of seasonal predictions. We illustrate the strong dependency of the score estimates, namely the Brier Score, on the choice of the observational reference. This has implications in, for example, the selection of the best prediction system among a set of possible candidates. To solve this issue, we consider two methodologies already proposed in the literature and apply them to seasonal predictions for wind speed. We evaluate their strengths and weaknesses to end up recommending the use of the observation-error-corrected scoring rules.
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RAMON GAMON, Jaume. High-quality observations for improved seasonal predictions. Implications for the wind energy sector. [consulta: 29 de novembre de 2025]. [Disponible a: https://hdl.handle.net/2445/185754]