Abstract
bstract: Predicting the atmosphere's state for a future date and specific place is the problem of weather forecasting. Traditionally, the atmosphere has been treated as a fluid in physical simulations to accomplish this. The equations for fluid dynamics and thermodynamics are numerically solved to determine the current state of the atmosphere and its future state. However, the system of ordinary differential equations that governs this physical model is unstable under perturbations, uncertainties in the initial measurements of the atmospheric conditions, and a lack of understanding of complex atmospheric processes govern the range of accurate weather forecasting to a 10 day period, beyond which weather forecasts are significantly unreliable. Contrarily, machine learning doesn't need a thorough understanding of the physical processes that govern the atmosphere and is more resilient to perturbations. As a result, machine learning could be a good substitute for physical models in weather forecasting
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