ENHANCING NUMERICAL WEATHER PREDICTION ACCURACY THROUGH MACHINE LEARNING-BASED MODEL OPTIMIZATION

Abstract

Numerical Weather Prediction (NWP) models form the backbone of modern meteorology, yet their performance is often constrained by complex atmospheric dynamics, parameterization errors, and limited spatial– temporal resolution. This research introduces a machine learning–based optimization framework designed to enhance the accuracy and efficiency of NWP systems. The proposed approach integrates data-driven learning algorithms with conventional physical models to correct biases, fine-tune parameters, and improve short- to medium-range forecasts. Machine learning methods such as Random Forests, Gradient Boosting, and Neural Networks are applied to analyze residual errors between observed and simulated data, enabling adaptive model correction. Experimental evaluation using historical meteorological datasets demonstrates that the optimized model achieves significant improvements in forecast precision for temperature, humidity, and precipitation compared with baseline NWP outputs. The framework highlights the potential of hybrid physics-AI systems in advancing reliable and high-resolution weather prediction.