Deep Learning-Based Reconstruction of Near-Surface Wind Speed Fields from Meteorological Observations

Reconstruction of near-surface wind speed (NSWS; ~10 m above ground level) from local meteorological station measurements remains an open challenge in climate research. Traditional geostatistical interpolation techniques can provide partial solutions, but their reliability is often limited—especially in regions with complex topography, which are common across Spain. These methods are also highly sensitive to the number of available observations.

This work investigates the potential of state-of-the-art Deep Learning (DL) techniques for NSWS reconstruction. In particular, we employ the Climate Reconstruction AI (CRAI) model, an encoder-decoder architecture based on U-Net with partial convolutions, which we train on the Copernicus European Regional Reanalysis (CERRA) dataset, featuring a temporal resolution of three-hourly data and a spatial resolution of 5.5km. This model learns the spatiotemporal patterns of NSWS and is capable of infilling wind fields from grids with missing values.

To apply the model to real-world conditions, we focus on reconstructing daily-averaged wind speed fields from incomplete grids of observational data provided by AEMET (the State Meteorological Agency of Spain). We evaluate several model variants to assess the influence of auxiliary variables such as 2-m air temperature, surface air pressure, 2-m relative humidity and orography. In addition to the baseline U-Net approach, a convolutional attention mechanism is employed to capture complex interdependencies among variables — for example, compound events involving relative humidity, temperature, orography and wind such as the Foehn effect — as well as an LSTM-based recurrent module to leverage temporal information in the reconstruction process.

The resulting models are benchmarked on CERRA data and subsequently applied to reconstruct NSWS fields from AEMET observations across Spain.