Weather forecasts change frequently because they are predictions of an inherently chaotic global system. A forecast is a probabilistic prediction, a best estimate of future atmospheric conditions based on a snapshot of the atmosphere at a given moment. These predictions are generated by complex numerical weather models that process vast amounts of data from satellites, radar, and ground stations. Since the atmosphere is constantly in motion and new data is always being collected, prediction models are routinely re-run and updated, leading to the adjustments you notice daily.
The Time Horizon of Accuracy
The likelihood and magnitude of a forecast change are directly tied to how far into the future the prediction extends. Short-range forecasts, typically covering the next 0 to 48 hours, are the most stable and reliable because they are based on the most current, high-resolution data. A two-day forecast for temperature or precipitation is generally accurate about 90% of the time, meaning adjustments in this period are usually minor, like a slight shift in temperature or the timing of a shower.
Forecasting for the medium-range, which spans approximately three to seven days out, sees a noticeable drop in stability. Accuracy for a seven-day forecast is closer to 80%, making this period more susceptible to significant adjustments as new model runs digest updated atmospheric information. A predicted sunny day for the weekend can easily shift to rain if a high or low-pressure system moves just a few dozen miles off its initial projected course.
When looking at the long-range, which is anything beyond eight days, the forecast becomes primarily about spotting general trends rather than predicting specific conditions. A ten-day or longer forecast may only be accurate about 50% of the time, meaning it is highly volatile and prone to substantial changes with each new update. These distant predictions give an idea of whether temperatures will be above or below average, but they cannot reliably pinpoint the exact high temperature or the precise timing of a storm.
Why Forecasts Must Change
Constant forecast adjustment is necessary due to the atmosphere’s fundamentally chaotic nature, often called the butterfly effect. This principle describes how a tiny, immeasurable difference in initial atmospheric conditions can be amplified over time, leading to dramatically different weather outcomes days later. The complexity of the global weather system makes it impossible to measure every variable perfectly, ensuring every forecast starts with a small, inherent error.
This initial error is compounded by data sparsity across the globe. Weather models assimilate observations from weather balloons, satellites, and ground stations, but vast areas, particularly over oceans and sparsely populated regions, lack direct measurements. The model must estimate conditions between these sensor points, which introduces an initialization error that grows larger as the model projects further into the future.
Even the most powerful supercomputers cannot perfectly simulate every micro-process in the atmosphere. Numerical weather prediction models divide the Earth and atmosphere into a grid. While resolutions are improving, many smaller-scale atmospheric features occur between the grid points. Subtle shifts in the speed and placement of a front or a low-pressure center, if not fully resolved in a previous run, can have a huge ripple effect. This often necessitates a complete change in the predicted weather pattern downstream.
The Schedule of Updates
The frequency of forecast changes is governed by the scheduled run times of the sophisticated computer models that generate the data. The two most influential global models, the American Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF), provide the major data input cycles. The GFS model runs four times per day, refreshing data every six hours, while the ECMWF model runs twice a day, or every twelve hours.
These major model runs provide the foundational data that meteorologists and weather apps use to build their specific forecasts. Many localized and high-resolution regional models, such as the High-Resolution Rapid Refresh (HRRR) model used in the United States, run much more frequently, often every hour. These rapid-refresh models incorporate the latest radar and observational data, allowing meteorologists to make minor, real-time adjustments, especially during rapidly evolving severe weather events.
While the core prediction may only see a major shift every six to twelve hours, the forecast displayed on a mobile app can appear to change much more frequently. Many commercial weather providers blend and interpret data from multiple models. They update their consumer-facing prediction every one to three hours, ensuring the public sees the most recently processed and refined estimate.