Meteorological forecasting uncertainty increases over longer durations

Weather forecasting accuracy decreases as the time horizon extends. While short-term predictions are generally reliable, ten-day forecasts often represent possible developments rather than precise sequences. This uncertainty stems from the chaotic nature of the atmosphere, incomplete measurement data, and necessary simplifications within computational models.

Forecasting relies on integrating millions of observations from weather stations, radiosondes, aircraft, ships, buoys, radar, and satellites. These data points cover variables such as temperature, air pressure, humidity, and wind speed. However, spatial distribution is uneven, with fewer direct measurements available over oceans, mountains, or sparsely populated regions.

Because even minor deviations in the initial state can amplify over time due to atmospheric chaos, meteorologists utilize data assimilation to combine observations with previous model forecasts. This process creates a consistent three-dimensional image of the atmosphere to serve as a starting point for subsequent predictions.