ECMWF

ECMWF exists to improve medium-range weather prediction through international cooperation and shared scientific and computing resources. Its convention gives it responsibilities that include producing medium-range forecasts, maintaining meteorological data, advancing numerical weather prediction, and assisting national meteorological services.¹ The centre is headquartered in Reading, United Kingdom, and operates as an intergovernmental organisation supported by member and cooperating states.

ECMWF does not replace national weather services. Instead, its global products provide a common scientific foundation that services adapt for local forecasting, warnings, aviation, hydrology, energy planning, and emergency management. Member states contribute expertise, observations, and funding, while the centre distributes forecast products and supports research partnerships. Its governing structure includes a Council representing participating countries and a Director-General responsible for operations and scientific programmes.²

ECMWF forecasts are generated by numerical models that solve equations describing the atmosphere, ocean, land surface, sea ice, and, increasingly, other parts of the Earth system. Observations from satellites, aircraft, ships, weather stations, radiosondes, and remote-sensing instruments are combined with a previous model forecast in a process called data assimilation. The resulting analysis provides the initial state from which a new forecast is computed.³

The Integrated Forecasting System produces both a high-resolution forecast and an ensemble prediction system. Ensemble members begin with slightly different initial conditions or model configurations, allowing forecasters to estimate uncertainty rather than relying on one deterministic outcome. ECMWF products cover the medium range to about 15 days, while extended and seasonal forecasts address longer timescales with lower day-to-day specificity.⁴ Open-data initiatives have also made selected forecast fields available to a wider research and applications community.⁵

ECMWF reanalysis reconstructs past atmospheric and surface conditions by repeatedly assimilating historical observations into a consistent numerical model. Unlike an ordinary forecast archive, a reanalysis can provide a physically coherent record where observations were sparse or measurement practices changed. The ERA5 dataset supplies hourly estimates of many weather and climate variables from 1940 onward and is widely used for climate monitoring, impact studies, renewable-energy assessment, and attribution research.

ECMWF also operates major components of the European Union’s Copernicus programme. The Copernicus Climate Change Service delivers climate indicators and information for adaptation and mitigation, while the Copernicus Atmosphere Monitoring Service tracks atmospheric composition, air quality, greenhouse gases, and aerosols. The Copernicus Emergency Management Service uses geospatial information for crises such as floods, fires, and earthquakes; these services broaden ECMWF’s role from weather prediction to operational Earth-system information.⁶

ECMWF’s influence is partly indirect: many users encounter its forecasts through national weather apps, aviation systems, private-sector products, or public agencies rather than through the centre itself. Forecast skill is measured statistically over many cases, and apparent precision at a particular location can decline rapidly when precipitation is localised or weather becomes highly sensitive to small initial errors. Ensemble probabilities are therefore often more informative than a single forecast value.

A less visible contribution is the centre’s role in observing-system evaluation. Forecast experiments help determine the value of satellite instruments, aircraft observations, ocean measurements, and other data streams, informing decisions about future observing networks.³ ECMWF research has also helped establish global reanalysis as an essential climate record, although reanalyses remain model-based estimates rather than direct measurements and can contain artificial changes when observing systems are introduced or revised.

Data assimilation

The process of combining observations with a previous model forecast to estimate the current state of the atmosphere and Earth system.

Ensemble prediction

A collection of forecasts made from varied initial conditions or model settings to represent forecast uncertainty.

Numerical weather prediction

Computer-based forecasting that uses physical equations to simulate the evolution of the atmosphere and related components.

Reanalysis

A long-term, model-assisted reconstruction of past environmental conditions produced by assimilating historical observations consistently.

ERA5

ECMWF’s fifth-generation global atmospheric reanalysis, providing hourly estimates of weather and climate variables.