AI-based approach for short-term forecasting of wind speed from a weather station network: A Case study in Valencia
- 1Centro de Investigaciones sobre Desertificación (CIDE), CSIC-UV-GVA, Climate, Atmosphere and Ocean Laboratory (Climatoc-Lab) Moncada (Valencia), Spain
- 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
- 8Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand
The generation of accurate and reliable forecasts of near-surface (~10 m above ground level) gridded wind speed data, hereinafter called NSWS, is crucial since it influences numerous socioeconomic and environmental fields. For instance, in the face of climate change, wind energy can contribute to the decarbonization of the electricity grid. NSWS, however, is a complex meteorological variable due to its inherent space-time variability, particularly in regions with complex topography like Valencia (Spain).
The traditional approach to forecasting NSWS relies on Numerical Weather Prediction (NWP) models, which demand substantial computational resources, specially when high spatial and temporal resolution are required, often necessitating hundred to thousands of CPU hours. As an innovative solution to this pressing issue, the ThinkInAzul project, under Climatoc-Lab, is exploring the use of deep learning for accurate NSWS predictions. We propose an architecturebased on encoder-decoder neural networks composing mixed convolutional and recurrent (ConvLSTM) layers. This AI-based product, designed as an early warning system, generate high-resolution (3- or 9-km) short-term (i.e., <24 hours) NSWS forecasts in near real-time (a few seconds) using a GPU.
Meteorological station networks provide realistic observations, being able to detect local wind effects, but with limited spatial coverage. Conversely, reanalysis and simulation products offer complete spatial coverage at low resolution but fail to accurately reproduce local NSWS. To address this, our AI-based tool is trained with the ERA5-Land (9-km) and NEWA (New European Wind Atlas, 3-km) NSWS datasets but its inference is performed using the observations from the Spain/Valencian Association of Meteorology (AEMET/AVAMET), a citizen weather station network of around ~600 stations. Consequently, the AI-based tool merges the advantages of both, offering a gridded product with high spatio-temporal resolution that can reproduce local NSWS effects.
The AI-based tool achieves a reasonably high correlation of 0.7 with the AEMET meteorological observations, with expectation of further improvement. This tool is applied to the western Mediterranean coast and has the potential for use in other regions following retraining of the neural network. Our ultimate goal is to develop an AI-based tool that enhance short-term forecasting of NSWS.
How to cite: Martinez-Roig, M., P. Plaza, N., Azorin-Molina, C., Andres-Martin, M., Chen, D., Zeng, Z., Vicente Serrano, S. M., R. McVicar, T., Guijarro, J. A., and Ali Safaei Pirooz, A.: AI-based approach for short-term forecasting of wind speed from a weather station network: A Case study in Valencia, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-536, https://doi.org/10.5194/ems2024-536, 2024.
