WeatherForecastModeling

Weather forecasting is the science of predicting atmospheric conditions for a given location and time. Forecasts cover nowcasting, which deals with the immediate future (minutes to hours), and longer horizons that extend over days. The purpose is to inform decision-making in areas such as safety, travel, agriculture, energy, and daily life.

Forecasting relies on observations from a wide range of sources, including weather stations, Doppler radar, satellites, radiosondes, ships, and aircraft. These data feed numerical weather prediction models that simulate the atmosphere by solving physical equations on a computational grid. Data assimilation combines real-world observations with model output to create an accurate representation of the current state, which serves as the starting point for forecasts.

Because the atmosphere is chaotic, forecast accuracy decreases with lead time. Short-range forecasts (typically up to 48 hours) are generally more reliable than longer-range predictions. Uncertainty is communicated through probabilistic forecasts and ensemble prediction systems, which run multiple simulations with varied initial conditions to estimate the range of possible outcomes.

Forecast products are produced by national and regional meteorological services and are used by aviation, maritime, agriculture, energy, and emergency management sectors, among others. Warnings and advisories help prepare for severe weather events such as storms, heatwaves, or heavy precipitation. Ongoing advancements in high-resolution models, data assimilation, satellite technology, and machine learning aim to improve accuracy and quantify uncertainty more effectively.

Historically, weather forecasting evolved from basic observational methods to modern numerical prediction with the help of computing power. Today, forecasts are continuously refined through international collaboration and the integration of diverse data sources.