A weather forecasting model turns information about the atmosphere into predictions for variables such as temperature, rainfall, wind or pressure. Traditional systems numerically simulate physical equations, while learned systems can estimate future states from large collections of historical and simulated weather data.
Forecasts are uncertain because observations are incomplete and small differences in starting conditions can grow over time. Useful systems therefore provide location, time horizon and uncertainty context rather than presenting one number as certain.
When a forecast drives an automated decision, the surrounding application must define when the estimate is captured, how it is compared with alternatives and how errors affect risk. A strong weather model does not by itself prove that a trading strategy has an advantage.
ELI5
A weather forecasting model is a system that studies current and past weather information to estimate what may happen next. It looks for physical or learned patterns that connect today's conditions with tomorrow's weather.
For example, it might estimate the highest temperature in a city tomorrow. The estimate can be useful, but it still has uncertainty because the atmosphere is complicated and measurements are never perfect.
