arXiv:2606.09941v1 Announce Type: cross Abstract: Surface winds can vary substantially from one minute to the next, so there is scope for studying its variation on this fine time scale. Restricting to the month of June to minimize seasonality, this work develops a range of machine learning models for generating realistic time series of surface wind vectors at a site in Lamont, Oklahoma based on more than 30 years of high quality measurements at the minute time scale. Such a generator could be used as an input into models from a range of disciplines, notably for wind energy, but also wildfire s
The increasing availability of high-resolution meteorological data and advancements in machine learning techniques are enabling more granular climate modeling for practical applications.
Accurate high-frequency wind prediction is crucial for optimizing renewable energy systems, improving disaster preparedness, and enhancing various climate-sensitive operations.
The ability to generate realistic, high-frequency wind vector time series at specific locations improves the inputs for models across multiple disciplines, particularly wind energy.
- · Renewable energy sector
- · Climate modeling researchers
- · Companies developing forecasting tools
- · Traditional, less precise wind modeling techniques
Improved efficiency and reliability of wind power generation facilities due to better predictive models.
Reduced operational costs and increased investment in wind energy projects as risk factors diminish with more accurate forecasting.
Accelerated transition to renewable energy sources, contributing to global decarbonization efforts and potentially shifting energy market dynamics.
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