Abstract

Traditionally, climate assessment methods have approached the environment as a liquid, closely observing wind conditions to predict future states. However, estimating the underlying air states has proven challenging due to oscillating effects and uncertainties. As a result, short-term climate forecasts have become increasingly unreliable. In this study, we propose a promising alternative that harnesses the power of machine learning, specifically utilizing the Random Forest model, for weather prediction.

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