HYBRID DATA DRIVEN WEATHER PREDICTION USING NEAR SURFACE MEASUREMENTS AND ATMOSPHERIC NUMERICAL MODELS

Abstract

The purpose of this research is to present a hybrid data-driven weather prediction framework that blends atmospheric numerical weather prediction (NWP) models with near-surface observational datasets in order to improve the accuracy and reliability of forecasts. Common problems with traditional numerical models include inaccurate parameterization, poor spatial resolution, and a lack of localized meteorological data. The proposed solution applies state-of-the-art machine learning algorithms to integrate numerical atmospheric model outputs with real-time near-surface meteorological data (such as humidity, temperature, wind speed, and pressure). In order to more accurately portray intricate atmospheric patterns and localized weather variations, the hybrid system combines the strengths of data-driven learning with simulations based on physical principles. This system is able to adapt to new environmental conditions, enhance short-term forecasting, and decrease prediction errors by integrating historical data with model outputs. If you want reliable, practical, and widely applicable weather predictions, the proposed hybrid approach is a great bet. The reason behind this is that numerical atmospheric models, when combined with near-surface measurements, greatly improve prediction capacity, according to experiments.