The weather forecast on a phone screen is the final stage of a complex global data pipeline involving thousands of instruments and powerful supercomputers. This localized prediction results from a coordinated effort that begins with physical atmospheric measurement, transitions to massive mathematical simulations, and concludes with commercial refinement and high-speed data delivery. Understanding where weather apps source their information requires tracing this data’s journey from raw atmospheric observation to the hyper-local forecast seen on a mobile device.
Gathering the Raw Observational Data
The foundation of all weather forecasting is the collection of raw, real-time measurements from instruments located across the planet and in space. This physical data acquisition is a continuous process involving a diverse network of publicly funded systems. These measurements of the atmosphere’s current state serve as the initial conditions for all subsequent prediction efforts.
Space-based satellites provide a global view of the atmosphere, operating in two primary orbits. Geostationary satellites, such as the US GOES series, orbit approximately 35,785 kilometers above the equator, synchronizing their speed with the Earth’s rotation to maintain a fixed view of a large coverage area. This position allows them to provide continuous imagery, tracking cloud formation and movement for short-term forecasts.
In contrast, polar-orbiting satellites fly at a much lower altitude of around 850 kilometers, circling the Earth from pole to pole. These satellites offer a higher resolution view and collect detailed atmospheric soundings of temperature and moisture profiles over the entire globe. They observe every point on Earth twice daily, complementing the coverage of geostationary systems, and are particularly valuable for initializing global prediction models.
Closer to the ground, a network of instruments captures surface and lower-atmosphere conditions. Automated Surface Observing Systems (ASOS), typically situated at airports, continuously measure temperature, wind speed and direction, visibility, and precipitation type. These automated stations provide the steady stream of surface variables that anchor forecasts to specific locations.
Another essential data source is the radiosonde, an instrument package carried aloft by weather balloons launched twice daily at 00:00 and 12:00 Coordinated Universal Time (UTC) from approximately 900 locations worldwide. As the balloon ascends, the radiosonde measures and transmits vertical profiles of atmospheric pressure, temperature, humidity, and wind speed/direction. This upper-air data provides a three-dimensional snapshot of the atmosphere’s structure, which is necessary for sophisticated computer models.
Ground-based Doppler radar systems, like the US NEXRAD network, provide high-resolution data on precipitation and wind. These radars emit microwave pulses and measure the energy reflected back by precipitation particles, known as reflectivity, to determine the location and intensity of rain, snow, or hail. They also utilize the Doppler effect to measure the motion of these particles, providing wind velocity data necessary for detecting severe weather rotation.
The Role of Numerical Weather Prediction Models
The massive volume of raw observational data collected globally is processed by complex computational systems known as Numerical Weather Prediction (NWP) models. These models are mathematical representations of the atmosphere, utilizing supercomputers to solve millions of complex physics equations that describe how atmospheric variables change over time. The process begins with data assimilation, where raw observations are ingested and blended with the previous forecast to create the most accurate starting point for the new prediction.
Two of the most prominent global NWP models are the Global Forecast System (GFS), developed by the US National Weather Service, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The GFS model provides freely accessible data and has a resolution, or grid spacing, of around 13 kilometers, offering a reliable global outlook. The ECMWF model, often regarded as having better accuracy for medium-range forecasts (three to ten days out), operates at a higher resolution, down to approximately 9 kilometers in some configurations.
The difference in output between these models stems from their underlying physics schemes, data assimilation techniques, and computational power, which is why forecasts can vary between applications. The highest level of modeling accuracy is achieved through ensemble forecasting, a technique that addresses the inherent uncertainty in the atmosphere. This method involves running the same NWP model multiple times, introducing slight variations to the initial conditions of each run.
By generating dozens of slightly different forecasts, often 50 or more, the ensemble system creates a range of possible outcomes rather than a single deterministic prediction. Meteorologists and commercial providers use the collective spread and clustering of these ensemble members to determine the probability and confidence level of a specific weather event. If 90% of the ensemble members predict rain, the confidence in the forecast is much higher than if the ensemble is widely scattered.
Commercial Aggregators and App Delivery
The final stage of the pipeline involves transforming the raw output from government NWP centers into the precise, hyper-local forecast delivered to a consumer’s mobile device. Most weather apps, including popular choices like Apple Weather, do not run their own global NWP models; instead, they license the resulting data from commercial aggregators. Companies such as IBM’s The Weather Company or AccuWeather act as intermediaries, acquiring the output of major government models like the GFS, ECMWF, and others.
These commercial aggregators apply proprietary post-processing algorithms to the licensed model data, which is the primary reason forecasts differ between applications. A key part of this process is model blending, where the aggregator’s algorithms combine the strengths of multiple NWP models, often integrating over a dozen different sources, to create a single, optimized forecast. Blending helps smooth out individual model biases and leverages the best predictions from each source for various time frames.
Another step is downscaling, a computational technique that takes a forecast from a coarse grid resolution, such as the 9-13 kilometer spacing of a global model, and refines it to a much smaller scale, sometimes down to a few hundred meters. This process incorporates local geographic factors like terrain, coastlines, and urban heat islands, which the coarser global models cannot resolve. The result is a hyper-local prediction tailored to the user’s exact GPS coordinates.
The refined, hyper-local data is then packaged and delivered to the end-user applications via an Application Programming Interface (API). The app requests the forecast data for a specific location from the aggregator’s servers, and the API instantly transmits the processed information. This allows the app to display a continuously updated forecast on the user’s screen. This framework allows billions of up-to-the-minute, location-specific forecasts to be generated and delivered globally every day.