Datasets

AIFS ENS Ensemble mean and spread: 100 m wind speed

AIFS ENS: a deep learning-based system developed by ECMWF. It is initialised with ECMWF perturbed forecasts and operates at N320 (~0.25Deg) resolution

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AIFS ENS Ensemble mean and spread: 2 m temperature

AIFS ENS: a deep learning-based system developed by ECMWF. It is initialised with ECMWF perturbed forecasts and operates at N320 (~0.25Deg) resolution

calendar_today Interval/period: N/A

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AIFS ENS Ensemble mean and spread: 300 hPa geopotential height

AIFS ENS: a deep learning-based system developed by ECMWF. It is initialised with ECMWF perturbed forecasts and operates at N320 (~0.25Deg) resolution

calendar_today Interval/period: N/A

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AIFS ENS Ensemble mean and spread: 500 hPa geopotential height

AIFS ENS: a deep learning-based system developed by ECMWF. It is initialised with ECMWF perturbed forecasts and operates at N320 (~0.25Deg) resolution

calendar_today Interval/period: N/A

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AIFS ENS Ensemble mean and spread: 850hPa temperature

AIFS ENS: a deep learning-based system developed by ECMWF. It is initialised with ECMWF perturbed forecasts and operates at N320 (~0.25Deg) resolution

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AIFS ENS Meteograms

AIFS ENS Meteograms show a probabilistic interpretation of the AIFS ENS forecasts for specific locations using a box and whisker plot. It shows the time evolution of the distribution of several meteorological parameters on a single diagram...

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AIFS ENS Probabilities: 10m wind speed

AIFS ENS: a deep learning-based system developed by ECMWF. It is initialised with ECMWF perturbed forecasts and operates at N320 (~0.25Deg) resolution

calendar_today Interval/period: N/A

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AIFS ENS Probabilities: 2 m temperature < 0ºC (day 10-15)

AIFS ENS: a deep learning-based system developed by ECMWF. It is initialised with ECMWF perturbed forecasts and operates at N320 (~0.25Deg) resolution

calendar_today Interval/period: N/A

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AIFS ENS Probabilities: total precipitation (day 10-15)

AIFS ENS: a deep learning-based system developed by ECMWF. It is initialised with ECMWF perturbed forecasts and operates at N320 (~0.25Deg) resolution

calendar_today Interval/period: N/A

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AIFS ENS Probabilities: total precipitation, last 24 hours

AIFS ENS: a deep learning-based system developed by ECMWF. It is initialised with ECMWF perturbed forecasts and operates at N320 (~0.25Deg) resolution

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AIFS Ensemble, X-i-a: Surface level

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AIFS Ensemble, X-i-b: Pressure level

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AIFS Ensemble, X-i-e: Soil level

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AIFS Ensemble, X-iii-a: Probabilities - Instantaneous weather events - Single level

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AIFS Ensemble, X-iii-b: Probabilities - Averaged weather events - Single level

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AIFS Ensemble, X-vi-a: Ensemble means - Single level

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AIFS Ensemble, X-vi-b: Ensemble means - Pressure levels

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AIFS Ensemble, X-vii-a: Ensemble standard deviations - Single level

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AIFS Ensemble, X-vii-b: Ensemble standard deviations - Pressure levels

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AIFS forecast - AIFS Ensemble (Set X - AIFS ENS)

Ensemble forecast runs produced by the ECMWF Artificial Intelligence Forecasting System (AIFS) Ensemble model.

4 forecast runs per day (00/06/12/18) 6 hourly steps to 360 (15 days)

More information can be found on the implementation page.

X-i: AIFS ENS forecast

Product description

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AIFS forecast - AIFS Ensemble Wave (Set XIII - AIFS ENS Wave)

On 12 May 2026, a new forecast stream will be produced by ECMWF's operational Artificial Intelligence Forecasting System ensemble model (AIFS ENS).

The new forecast stream is for wave forecast runs, marking ECMWF's first operational data-driven wave forecasts.

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Pacific Ocean SST attribution experiment for winter 2015/16: PC16

4-month long 101-ensemble member seasonal attribution experiment (PC16) initialised on 01-November-2015 using the atmosphere-only version of SEAS5 (see Johnson et al., 2019) forced with daily ERA5 SST as in the reference experiment (R16) but with daily SST climatology over the tropical Pacific Ocean.

Examples

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Pacific Ocean SST attribution experiment for winter 2019/20: PC20

4-month long 101-ensemble member seasonal attribution experiment (PC20) initialised on 01-November-2019 using the atmosphere-only version of SEAS5 (see Johnson et al., 2019) forced with daily ERA5 SST as in the reference experiment (R20) but with daily SST climatology over the tropical Pacific Ocean.

Examples

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pan-Arctic regional reanalysis daily and monthly data from 1986 to present

The C3S Arctic Regional Reanalysis second generation (CARRA2) dataset contains daily and monthly meteorological variables at 2.5 km resolution. These variables are specified at single levels (including surface) and also at soil, height, pressure and model levels. These daily and monthly data are pre-calculated and have the following types depending on the variables: daily and monthly averages, extremes and totals.

calendar_today Interval/period: Wed, 01/01/1986 - Sun, 12/31/2023

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pan-Arctic regional reanalysis sub-daily data from 1986 to present

The C3S Arctic Regional Reanalysis second generation (CARRA2) dataset contains 3-hourly analyses at 2.5 km resolution. These variables are specified at single levels (including surface) and also at soil, height, pressure and model levels. Additionally, hourly forecasts are available between the analysis times and particularly forecasts up to 18 hours initialised from the analyses at 00 and 12 UTC.

calendar_today Interval/period: Wed, 01/01/1986 - Sun, 12/31/2023

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