IMPROVING PREDICTIVE SKILL IN NUMERICAL WEATHER MODELS THROUGH MACHINE LEARNING INTEGRATION

Abstract

The increasing complexity of atmospheric systems presents significant challenges to the accuracy and timeliness of Numerical Weather Prediction (NWP) models. Traditional NWP approaches rely heavily on physical parameterizations and deterministic formulations, which often fail to capture nonlinear dependencies and uncertainties in meteorological processes. To address these limitations, this study proposes a machine learning (ML)-integrated framework aimed at enhancing the predictive skill and computational efficiency of NWP systems. The framework utilizes historical meteorological datasets, ensemble model outputs, and satellite observations to train ML models such as Long Short-Term Memory (LSTM) networks and Gradient Boosting Regressors. These models are designed to correct systematic biases, refine model parameterizations, and improve the assimilation of real-time data. Experimental results demonstrate that the ML-enhanced NWP framework achieves notable improvements in short- to medium-range forecasts, reducing root mean square error (RMSE) by up to 15% compared to conventional methods. Furthermore, the integration of ML modules enables adaptive learning from evolving climatic trends, ensuring model robustness under varying weather conditions. This approach highlights the potential of artificial intelligence in advancing meteorological forecasting and supports the transition toward intelligent, data-driven atmospheric prediction systems.