Microsoft has a new machine learning tool to improve weather forecasts

Physics-based and AI-based models run into similar problems when they start predicting the weather two weeks or more in advance. Traditional models attempt to simulate the complex physics of Earth’s atmosphere, while AI models look for patterns in historical data to make forecasts. In both approaches, models rely on approximations to fill data gaps, whether from regions with sparse weather stations or complex atmospheric phenomena that scientists are still working to understand. The ways in which these models make up for those gaps in data can become a type of systematic bias. The resulting errors compound when making forecasts further ahead in time, making these predictions less precise.