MACHINE LEARNING TECHNIQUES FOR PERFORMANCE OPTIMIZATION OF NUMERICAL WEATHER PREDICTION MODELS

Abstract

Numerical Weather Prediction (NWP) models play a crucial role in forecasting atmospheric conditions, but their accuracy is often limited by computational constraints, uncertainty in initial conditions, and model biases. Recent advancements in machine learning (ML) techniques offer promising solutions to enhance the performance and precision of NWP models. This study explores the integration of ML-based optimization techniques, including neural networks, ensemble learning, and deep learning architectures, to improve weather prediction accuracy, computational efficiency, and bias correction. The proposed approach leverages data-driven pattern recognition, feature selection, and error correction models to refine NWP outputs, enabling more reliable short-term and long-term forecasts. Experimental evaluations demonstrate that ML-enhanced NWP models outperform traditional numerical methods in terms of forecast precision, reduced computational overhead, and adaptive learning capabilities. This research highlights the potential of AI-driven meteorology, paving the way for more accurate, scalable, and real-time weather forecasting solutions.