Datasets
This dataset is from an ensemble forecast initiated on 3 October 2023 at 0 UTC. Key details: IFS cycle 48r1,TCo1279 (9km), 137 levels, 450s (7.5 min) timestep, 48h run, 10+1 members, EDA initial conditions (no singular vector perturbations), no model uncertainty, 1 hourly archiving 0-24h, 3 hourly archiving 24-48h.
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Ensemble Mean Sea Level Pressure (MSLP) is the mean MSLP of the ensemble members (in hPa) and detail is smoothed out. These charts show surface pressure patterns...
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Spread usually, but not always, increases with forecast range. Spread refers to the uncertainty of the values of a parameter but it does not necessarily refer to the flow patterns...
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The ensemble mean is the mean value derived from all the 50 ensemble members plus the control member. This value attempts to capture the general picture while smoothing out spurious detail...
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The ensemble spread is a measure of the differences between the 50 ensemble members plus the control member and is represented by the standard deviation with respect to the ensemble mean...
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These charts show the mean and variation in the latest ECMWF ensemble forecast (ENS). ...
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These charts show the mean and variation in the latest ECMWF ensemble forecast (ENS). ...
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These charts show the mean and variation in the latest ECMWF ensemble forecast (ENS). ...
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Spread refers to the uncertainty of the values of a parameter but it does not necessarily refer to the flow patterns...
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These charts show the mean and variation in the latest ECMWF ensemble forecast (ENS). ...
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One model cycle of ECMWF ensemble prediction system set up to explore data information content in the scope of a ESoWC project. Perturbed and control forecast of temperature on 91 model levels are archived. The data is encoded as IEEE single-precision (32-bit floats) circumventing any other lossy compression. This is in contrast to the default 16- or 24-bit linear packing used in the ECMWF data archive. The volume of the full experiment is 1 TB.
Examples
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These diagrams aim to show the time evolution of regimes which are indicative of the likely weather in and around Europe.The winds across the Atlantic can usefully be classified into four regimes ...
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Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis.
Interval/period: Mon, 01/01/1940 - Fri, 12/06/2024
Fifth Generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5).
Produced by replaying only the land component of the ECMWF ERA5 climate reanalysis, it benefits from the same physical data-assimilation framework but runs offline at
higher spatial detail (9 km grid) to deliver richer land-surface information.
Interval/period: Sun, 01/01/1950 - Thu, 12/31/2026
SNAPSI case study of Northern Hemisphere strong polar vortex case in the stratosphere, initialised on 2022-01-01. TCo319L137 resolution 51-member ensemble, integrated for 70 days. Nudging to the zonal-mean.
Examples
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Scores of forecasts of surface parameters by experimental machine learning models
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These plots compare recent IFS and experimental AIFS verification scores for 500 hPa ...
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These plots compare recent IFS and experimental AIFS verification scores for 500 hPa ...
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This dataset provides daily gridded data of sea ice concentration for both hemispheres derived from satellite passive microwave brightness temperatures. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is listed as an Essential Climate Variable by the Global Climate Observing System.
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Interval/period: Sun, 10/01/1978 - Mon, 09/29/2025
Interval/period: Tue, 01/01/1991 - Thu, 12/31/2020
This dataset provides daily gridded data of sea ice edge and sea ice type derived from brightness temperatures measured by satellite passive microwave radiometers. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is recognized by the Global Climate Observing System as an Essential Climate Variable. Sea ice edge and type are some of the parameters used to characterise sea ice.
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Interval/period: Wed, 10/25/1978 - Wed, 09/24/2025
Interval/period: Mon, 01/01/1979 - Tue, 09/30/2025
This dataset provides monthly gridded data of sea ice thickness for the Arctic region based on satellite radar altimetry observations. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is recognized by the Global Climate Observing System as an Essential Climate Variable.
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