Weather prediction has always been challenging because the atmosphere is complex and constantly changing. Traditional methods use physics-based models that rely on mathematical equations to predict how the weather will evolve. However, these models take long time to run and have uncertainties, the results are not very efficient and accurate. Now, Artificial Intelligence (AI) is helping improve weather forecasting (AI-driven forecasting). AI can quickly analyse huge amounts of weather data from satellites, weather stations, and radars. It learns patterns from past weather events and makes predictions faster and more accurately than 7traditional models. Artificial Intelligence (AI) Machine learning (ML) (particularly) Deep learning (DL) These are really helpful in revolutionizing weather forecasting by improving accuracy, reducing computational costs, and enabling real-time predictions. Accurate weather prediction is essential for disaster preparedness, agriculture, and climate research. AI-based weather prediction offers a promising solution by integrating deep learning techniques with physics-based models to enhance forecast accuracy and efficiency. This paper explores hybrid approaches that combine machine learning with fundamental atmospheric equations to improve weather predictions. We discuss advancements in AI-driven parameterization, and uncertainty reduction, highlighting their potential to revolutionize meteorology. The findings indicate that AI-augmented climate models can significantly improve forecasting capabilities, paving the way for more accurate and efficient weather prediction systems.
A Wed, study studied this question.