Evaluation and Interpretability of AI-Driven Short-Term Wind Speed Forecasting.

The generation of accurate and reliable short-term forecasts (<12 hours) of near-surface (∼ 10m above ground level) wind speed fields, hereinafter called NSWS, are crucial for various socioeconomic and environmental applications. However, monitoring and forecasting NSWS is challenging due to its inherent space-time variability, especially in regions with complex orography such as the Iberian Peninsula in Spain.

Traditional NSWS forecasting methods relies on Numerical Weather Prediction (NWP) models, which require significant computational resources. In addition, these NWP models often yield inaccurate results, especially in regions with complex orography. As a more efficient alternative to this constraint, here we explore the ability to Artificial Intelligence (AI) methods to enhance the efficiency and accuracy of short-term NSWS predictions. We propose the use of two deep learning methods:

1) A U-Net architecture based on Partial Convolutions to generate high-resolution hourly NSWS maps from station-based observations[3].

2) 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[4].

This real-time AI-based product, designed as an early warning system, generates high-resolution (3/9 km) short-term (12 h; σ=1 h) NSWS forecasts in near real-time (seconds), achieving high correlation and low prediction errors.

Meteorological stations provide accurate, site-specific wind observations but have limited spatial coverage, especially in mountainous or remote areas. In contrast, reanalysis products offer full coverage at low resolution but fail to accurately reproduce local wind conditions. Our AI-based tool bridges these gaps by combining station and simulation data, though its inference relies solely on station data, making it a cost-effective alternative to NWP models. Observations come from Spanish Meteorological Agency (AEMET)[1], while the reanalysis used is ERA5-Land (9 km)[2].

Beyond performance evaluation, we apply well-established interpretability techniques to analyze the model’s decision-making process:

1. Feature Importance methods were used to evaluate the relevance of each input time step. Both Feature Permutation and Feature Ablation revealed an expected exponential decline in importance over time, but also highlighted that time steps around 7 hours in the past play a key role in accurate forecasting, underscoring the value of long-term information.

2. Pixel Attribution techniques were used to identify important spatial regions in the input wind speed maps. Despite differences, all methods consistently emphasized regions where unexpected wind patterns or extreme events occur, revealing the model’s primary focus. Guided Grad-CAM offered the most interpretable results by combining coarse and fine details.

These interpretability analyses enhance trust in AI-driven forecasts while guiding improvements in model development. While tested on the Iberian Peninsula, the approach is adaptable to other regions. This scalable AI-based method enhances short-term NSWS forecasting for AEMET and other meteorological services, showcasing AI’s potential to improve both forecast accuracy and operational efficiency in meteorology.