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Data assimilation

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Research



ECMWF is at the forefront of research in numerical weather prediction.

We use the scientific advances made in areas such as data assimilation, Earth system modelling, predictability and reanalysis to improve our forecasts.

We work collaboratively with scientists across the world on all aspects of prediction systems as well as the many areas that support forecast production.





























Data assimilation



We optimise methods that combine observations with the forecast model to produce an initial state of the Earth system.






Assimilation methods

Observations














Modelling



We develop models for different components of the Earth system and their interactions.






Atmosphere

Ocean

Land














Predictability



We assess predictability at all forecast ranges and investigate methods to represent forecast uncertainty.






Ensemble forecasts

Forecast evaluation














Reanalysis



We create global datasets of the recent history of the atmosphere, land surface, and oceans.






Coupled Earth-system reanalysis

Reanalysis for climate monitoring

Ocean Reanalysis




















Collaboration



ECMWF is part of a vibrant meteorological community, leading and taking part in projects, making resources available to others, and sharing knowledge.

Key results are documented in ECMWF publications and in many cases in peer-reviewed journals.



View our knowledge-sharing eventschevron_rightView our staff profile directorychevron_right











Projects and collaborations



We are funded by our Member and Co-operating States but receive some extra funding from various bodies to carry out cutting-edge research, often in collaboration with other organisations.













Publications



We produce a range of publications documenting our latest research.













Member State Special Projects



Member States can use ECMWF’s supercomputer for projects likely to be of interest to the general scientific community.


















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