Data

01 · Our Data

Our Data

WeatherMapping is powered by a combination of observational station data and global gridded datasets developed and maintained by WeatherDataAI, our data science and modeling partner. WeatherDataAI’s proprietary data pipelines unify inputs from government, commercial, and satellite sources into a single, high-resolution global framework. The result is a scientifically consistent, AI-enhanced dataset that provides the foundation for all of WeatherMapping’s forecasts, analyses, and visualizations.

With a global grid of roughly 28 km (17 mi) spacing, WeatherDataAI’s processed datasets provide the spatial foundation for WeatherMapping’s mapping, satellite imagery, and graphing modules. This unified data layer ensures that every map, forecast, and visualization operates from the same scientifically consistent baseline giving users the accuracy required for research, forecasting, and operational decision making.

Sources at a glance

02 · Coverage

Coverage & Specifications

WeatherMapping covers the whole globe, land and ocean, at a daily time step. The same data underpins every module, so a number on a map, a line on a graph and a row in a data order always agree.

Spatial coverage

Global, land and ocean

Grid spacing

~28 km (17 mi)

Station network

~16,000 curated physical stations

Time step

Daily

Observation history

Daily since 1940 (Data Orders & API)

Graphing history

Any year back to 1980

Forecast runs

00z, 06z, 12z and 18z

Forecast horizon

Up to seven months in the app; 15 days via the API

Units

Metric or Imperial, on every variable

Anomalies

Departures from climatology, updated daily

Regions

Predefined regions, countries, states/provinces and custom areas

Outputs

PNG, interactive HTML, CSV with full metadata, REST API

03 · Variables

Variable Families

32 weather and geophysical variables are available from the station network, with satellite and gridded layers extending coverage to vegetation, soil and ocean conditions. Every variable can be shown as a raw value or as a departure from normal.

04 · Observations

Observational Data

Mapping every one of the 250,000 global weather data points directly would be impractical for daily visualization. Instead, WeatherMapping uses a curated global network of approximately 16,000 high-quality physical weather stations, selected and maintained by WeatherDataAI for their reliability and data continuity. These observations form the core inputs to WeatherDataAI’s global modeling systems, which transform point measurements into continuous gridded datasets through interpolation and assimilation techniques.

Within the application, the Weather Mapping and Data Graphing modules rely on this network to generate filled, data-driven contour maps and historical analyses. Users can visualize precipitation, temperature, wind, pressure, radiation, and soil metrics with daily updates and selectable time ranges - built directly from this station-based observational backbone.

05 · Forecasts

Forecast Data

For forecasts, WeatherMapping’s primary data provider is the European Centre for Medium-Range Weather Forecasts (ECMWF), utilizing both AIFS and ensemble-based forecast systems to provide state-of-the-art accuracy and consistency. ECMWF’s global models are supplemented with additional forecast inputs from the Global Forecast System (GFS) and other regional providers to ensure redundancy and breadth of coverage.

All forecast data is processed through the WeatherDataAI modeling pipeline to harmonize formats, update frequencies, and variable structures, enabling users to seamlessly compare observations and forecasts across providers within the same unified interface.

06 · Satellite

Satellite & Remote Sensing Data

The Satellite Mapping module integrates global gridded data from WeatherDataAI alongside additional datasets from government and private Earth observation sources. These include NASA, NOAA, ESA, and commercial satellite networks that supply vegetation indices, soil moisture, sea surface temperature, and other geophysical variables. By merging these sources within a single platform, WeatherMapping delivers a complete daily-scale view of Earth’s atmosphere, land, and ocean conditions - combining observational integrity, model precision, and satellite coverage into one seamless system.

07 · Quality

Data Integrity & Updates

All datasets used within WeatherMapping undergo automated quality control and validation before being integrated into the platform. Updates are processed daily to ensure alignment between observational, forecast, and satellite sources, maintaining the reliability and consistency that professionals depend on. From raw collection to refined visualization, every layer within WeatherMapping is backed by the same scientific rigor that powers WeatherDataAI’s enterprise data systems.

The daily pipeline

08 · Schedule

Daily Data Update Schedule

WeatherMapping’s global datasets are updated automatically throughout the day based on source availability and processing times within the WeatherDataAI pipeline. The following schedule reflects typical completion times in Eastern Time (ET).

Update times represent the completion of WeatherDataAI’s internal processing and verification routines. Minor delays may occur during heavy processing periods or large-scale satellite ingest operations.

Access

Three ways to use the data