High-Fidelity Wind Field Prediction Using Deep Spatiotemporal Learning

Abstract

Introduction: Accurate short-term wind field prediction is essential for meteorology, aviation, and renewable energy applications. Traditional numerical weather prediction (NWP) models are limited by high computational costs and low temporal resolution, making them unsuitable for real-time forecasting. To overcome these limitations, deep learning offers a promising alternative by effectively modeling complex spatiotemporal dependencies in environmental data.

Objectives: This study aims to develop a deep learning-based approach for high-fidelity short-term wind field prediction. The key goals are to: (1) capture fine-scale wind structures with high spatial and temporal accuracy, (2) reduce computational complexity compared to NWP models, and (3) evaluate the model’s performance using standard metrics.

Methods: We propose a multi-layer Convolutional Long Short-Term Memory (ConvLSTM) network for wind nowcasting. ConvLSTM combines convolutional operations with temporal memory, making it ideal for spatially and temporally coherent predictions. The model is enhanced with batch normalization and dropout to prevent overfitting. It is trained on sequences of wind velocity fields (U and V components), learning intricate flow patterns across time.

Results: The ConvLSTM model delivers strong predictive performance. It achieves an MSE of 0.0429, RMSE of 0.02071, and MAE of 0.0603, indicating low error. High PSNR (66.2592) and SSIM (0.9978) scores confirm the model’s ability to preserve spatial detail and structural integrity in predicted wind fields. These results demonstrate its capability to accurately capture fine-scale wind dynamics.

Conclusions: The proposed ConvLSTM framework presents a reliable and efficient solution for short-term wind field prediction. It offers substantial improvements in spatial accuracy and computational speed over traditional NWP models. With high fidelity and structural consistency, this deep learning model shows strong potential for real-time wind nowcasting in critical applications.