- 1Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain (marcos.martinez.roig@csic.es)
- 2Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
- 3School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- 4Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC), Zaragoza, Spain
- 5CSIRO Environment, GPO Box 1700, Canberra, Australia
- 6Retired from the State Meteorological Agency (AEMET), Balearic Islands Office, Palma, Spain
- 7National Institute of Water & Atmospheric Research Ltd (NIWA), Wellington, New Zealand
The generation of accurate and reliable short-term forecasts (<12 hours) of near-surface (~10 m above ground level) gridded wind speed data, hereinafter called NSWS, are crucial for various socioeconomic and environmental applications. For instance, in the face of climate change, accurate wind speed predictions can contribute to the decarbonization of the electricity grid by optimizing the wind energy generation.
Traditional NSWS forecasting methods relies on Numerical Weather Prediction (NWP) models, which require significant computational resources, particularly when high spatial and temporal resolution are required. Moreover, these models often yield inaccurate results, especially in regions with complex topography. As a more efficient alternative to this pressing issue, the Climatoc-Lab, as part of the PTI+Clima, is exploring Artificial Intelligence (AI) methods to enhance the efficiency and accuracy of short-term NSWS predictions. We propose the use of two deep learning methods:
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A U-Net architecture based on Partial Convolutions to generate high-resolution hourly NSWS maps from station-based observations.
- An encoder-decoder architecture based on mixed convolutional and recurrent (ConvLSTM) layers to predict short-term NSWS maps using the generated infilled data as input.
This AI-based product, designed as an early warning system, generate high-resolution (~3/9-km) short-term (12 h; 1-h resolution) NSWS forecasts in near real-time (seconds) using a GPU.
Measurements from meteorological station networks provide accurate site-specific observations, capturing local wind effects, but with limited spatial coverage, being sparse and almost absent in mountainous and remote areas. Conversely, reanalysis and simulation products offer complete spatial coverage at low resolution but fail to accurately reproduce local NSWS. Our AI-based tool combine the strenghts of both worlds, as it is trained using both, observation and simulation data. The observations are provided by the Spanish Meteorological State Agency (AEMET), while the simulation data comes from reanalysis like ERA5-Land (9-km).
The AI-based tool achieves a high correlation of 0,96 for Infilling and 0,849 for Prediction for the year 2020 of ERA5-Land data used for validation, with potential for further improvements. This also shows a reasonably high correlation of 0,84 with the AEMET meteorological observations. This scalable AI-based approach promises to enhance short-term NSWS forecasting for AEMET and other meteorological services, highlighting the promising role of AI to improve both forecast precision and operational efficiency in meteorology applications.
How to cite: Martínez-Roig, M., Plaza-Martín, N. P., Azorín-Molina, C., Monsalvez-Pozo, K., Andrés-Martin, M., Chen, D., Zeng, Z., Vicente-Serrano, S. M., McVicar, T. R., Guijarro, J. A., and Safaei-Pirooz, A. A.: AI-based Short-Term Wind Speed Forecasting for Real-Time Applications., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19963, https://doi.org/10.5194/egusphere-egu25-19963, 2025.
