Wind Nowcast On Radar Imagery Employing Deep Learning Technique

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The research introduces an innovative wind forecasting system that combines radar imagery with a deep learning approach using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The motivation behind this research stems from the limitations of traditional wind forecasting methods, which often fail to accurately capture complex spatial and temporal patterns in radar data. The aim is to develop a more precise and reliable forecasting model that can effectively handle these challenges. To achieve this, the study set several objectives: designing a robust deep learning framework that integrates CNNs for extracting spatial features from radar images and LSTMs for analyzing temporal sequences, optimizing the model to improve prediction accuracy, and evaluating its performance against existing methods. The results achieved include significantly low Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), reflecting enhanced forecasting precision. Additionally, the system demonstrates high Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) scores, indicating effective preservation of critical details in radar imagery. The successful implementation of this system highlights its potential applications in various fields, such as renewable energy management, weather forecasting, and environmental monitoring, where accurate and reliable wind predictions are essential for operational efficiency and decision-making.