Datasets

Multi-model seasonal reforecasts of river discharge for Europe

This dataset provides hydrological seasonal reforecasts of monthly mean river discharge across Europe for the period 1993 to 2016. The first is an E-HYPE multi-model system comprising eight model realisations using a catchment-based resolution. The second comprises the E-HYPEgrid, VIC-WUR and LISFLOOD-EFAS hydrological models at a 5km gridded resolution.

calendar_today Interval/period: Fri, 01/01/2021 - Sun, 08/09/2026

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Multi-parameter EFI during last 24 hours

These charts aim to point towards areas where anomalous weather is likely to occur. ...

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Multiparam: Weekly mean anomalies

This chart shows 7-day mean anomalies for a range of parameters from the ECMWF Sub-seasonal ...

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Near surface meteorological variables from 1979 to present derived from bias-corrected reanalysis

This dataset provides bias-corrected reconstruction of near-surface meteorological variables derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5). It is intended to be used as a meteorological forcing dataset for land surface and hydrological models.

calendar_today Interval/period: Mon, 01/01/1979 - Tue, 12/31/2024

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nextGEMS - Future Projection - IFS-FESOM - Generation-1 - Realization-2

Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections

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nextGEMS - Historical Simulation - IFS-FESOM - Generation-1 - Realization-1

Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections

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Nino Annual Plumes (Verification) - Seasonal Forecast - SEAS5

This chart provides a range of skill scores relating to forecasts of the evolution of the sea ...

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Nino Plumes (Verification) - Seasonal Forecast - SEAS5

This chart provides a range of skill scores relating to forecasts of the evolution of the sea ...

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Nitrogen Dioxide forecasts

CAMS nitrogen dioxide forecasts

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Nordic gridded temperature and precipitation data from 1961 to present derived from in-situ observations

The Nordic Gridded Climate Dataset (NGCD) is a high resolution, observational, gridded dataset of daily minimum, maximum and mean temperatures and daily precipitation totals, covering Finland, Sweden and Norway. The time period covered begins in January 1961 and continues to the present.
Spatial interpolation methods are applied to observational datasets to create gridded datasets.
In general, there are three types of such methods: deterministic (type 1), stochastic (type 2) and pure mathematical (type 3).

calendar_today Interval/period: Sun, 01/01/1961 - Thu, 09/18/2025

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Northern Hemisphere 500 hPa height anomaly

**Note:** In **June 2023** ECMWF implemented a **major upgrade ...**

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Experimental: GraphCast ML model: Rain and mean sea level pressure

GraphCast (Google DeepMind): a deep learning-based system developed by Google DeepMind.It is initialised with ECMWF analysis. GraphCast operates at 0.25° resolution.

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Experimental: GraphCast ML model: Temperature and geopotential at various pressure levels

GraphCast (Google DeepMind): a deep learning-based system developed by Google DeepMind.It is initialised with ECMWF analysis. GraphCast operates at 0.25° resolution.

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Experimental: GraphCast ML model: Total accumulated precipitation

GraphCast (Google DeepMind): a deep learning-based system developed by Google DeepMind.It is initialised with ECMWF analysis. GraphCast operates at 0.25° resolution.

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Experimental: GraphCast ML model: Wind and geopotential heights at various pressure levels

GraphCast (Google DeepMind): a deep learning-based system developed by Google DeepMind.It is initialised with ECMWF analysis. GraphCast operates at 0.25° resolution.

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Experimental: Pangu-Weather ML model: 2 m temperature and 10 m wind

Pangu-Weather: a deep learning-based system developed by Huawei. It is initialised with ECMWF analysis. Pangu-Weather operates at 0.25° resolution.

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Experimental: Pangu-Weather ML model: 500 hPa geopotential height and 850 hPa temperature

Pangu-Weather: a deep learning-based system developed by Huawei. It is initialised with ECMWF analysis. Pangu-Weather operates at 0.25° resolution.

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Experimental: Pangu-Weather ML model: Mean sea level pressure and 200 hPa wind

Pangu-Weather: a deep learning-based system developed by Huawei. It is initialised with ECMWF analysis. Pangu-Weather operates at 0.25° resolution.

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Experimental: Pangu-Weather ML model: Mean sea level pressure and 850 hPa wind speed

Pangu-Weather: a deep learning-based system developed by Huawei. It is initialised with ECMWF analysis. Pangu-Weather operates at 0.25° resolution.

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Experimental: Pangu-Weather ML model: Temperature and geopotential at various pressure levels

Pangu-Weather: a deep learning-based system developed by Huawei. It is initialised with ECMWF analysis. Pangu-Weather operates at 0.25° resolution.

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Experimental: Pangu-Weather ML model: Wind and geopotential heights at various pressure levels

Pangu-Weather: a deep learning-based system developed by Huawei. It is initialised with ECMWF analysis. Pangu-Weather operates at 0.25° resolution.

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Global land surface atmospheric variables from 1755 to present from comprehensive in-situ observations

This set of data holdings provides access to data collected from land surface meteorological observations across the globe. Data are available at the observational level and also at daily and monthly aggregations. Data have been collated and harmonised and quality control checks have been performed, but no attempt has been made to assess for potential biases. Data are provided for a range of commonly observed variables.

calendar_today Interval/period: Sun, 01/01/1978 - Wed, 10/17/2018

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Hourly and daily gridded air quality surface concentrations derived from in situ low-cost sensor measurements over Europe from 2018 to 2024

This dataset provides surface-level particulate matter (PM2.5 and PM10) concentrations derived from in situ low-cost sensor (LCS) measurements, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in the context of the Horizon Europe All Data for Green Deal project. This dataset differs from existing air quality datasets by combining crowdsourced observations with a robust correction framework, producing high-resolution, reference-aligned gridded products.

calendar_today Interval/period: Mon, 01/01/2018 - Tue, 12/31/2024

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Machine Learning model data

ECMWF is now running a series of data-driven forecasts as part of its experimental suite. These machine-learning based models are very fast, and they produce a 10-day forecast with 6-hourly time steps in approximately one minute. The outputs are available in graphical form.

Currently, three of these models are available:

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Near surface meteorological variables from 1979 to 2019 derived from bias-corrected reanalysis

This dataset provides bias-corrected reconstruction of near-surface meteorological variables derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5). It is intended to be used as a meteorological forcing dataset for land surface and hydrological models.

calendar_today Interval/period: Mon, 01/01/1979 - Mon, 12/31/2018

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