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Experimental: GraphCast ML model: Mean sea level pressure and 850 hPa wind speed

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: 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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open_in_newview in Open Charts

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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open_in_newview in Open Charts

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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open_in_newview in Open Charts

Extended-range ensemble reforecasts for 1 Jan starts 1981-2016

Coupled ensemble forecasts (60 days, 8+1 members), 1 Jan 1981-2016, IFS-CY43R3 TCo319L91, no initial perturbations, SPPT applied only in (50E-120E, 20N-20S). Twice-daily global pressure-level gridded F80 atmospheric fields.

Examples

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Extended-range ensemble reforecasts for 1 Jan starts 1981-2016 that include MJO in phases 2 or 3

Coupled ensemble forecasts (60 days, 50+1 members), 1 Jan 1987,1990,1995,2010,2013, IFS-CY43R3 TCo319L91, no initial perturbations, SPPT applied only in (50E-120E, 20N-20S). Twice-daily global pressure-level gridded F80 atmospheric fields.

Examples

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Extended-range ensemble reforecasts for 1 Nov starts 1981-2016

Coupled ensemble forecasts (60 days, 8+1 members), 1 Nov 1981-2016, IFS-CY43R3 TCo319L91, no initial perturbations, SPPT applied only in (50E-120E, 20N-20S). Twice-daily global pressure-level gridded F80 atmospheric fields.

Examples

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Extended-range ensemble reforecasts for 1 Nov starts 1981-2016 that include MJO in phases 2 or 3

Coupled ensemble forecasts (60 days, 50+1 members), 1 Nov 2002, IFS-CY43R3 TCo319L91, no initial perturbations, SPPT applied only in (50E-120E, 20N-20S). Twice-daily global pressure-level gridded F80 atmospheric fields.

Examples

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Extended-range reforecasts (43R1)

15-member coupled IFS (cycle 43R1) extended-range reforecast experiment covering the period 1989-2015. The atmosphere is configured with 91 vertical levels and uses the Tco399 cubic octahedral reduced Gaussian grid. The IFS is coupled hourly to the 75 level version of the NEMO v3.4 ocean model and the LIM2 sea-ice model, both of which use the ORCA025 tripolar grid. Coupling follows the implementation used in ECMWF operational forecasts.

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Extended-range reforecasts (43R1) with bias-corrected North Atlantic sea surface temperatures

15-member coupled IFS (cycle 43R1) extended-range reforecast experiment covering the period 1989-2015 with bias-corrected sea-surface temperatures (SSTs) in the North Atlantic region. This experiment can be compared with gkzp, which is the relevant control without bias-correction. The atmosphere is configured with 91 vertical levels and uses the Tco399 cubic octahedral reduced Gaussian grid.

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open_in_newview in ECMWF Catalogue|open_in_newview in IFS Experiments

Extreme precipitation risk indicators for Europe and European cities from 1950 to 2019

The dataset presents climate impact indicators related to extreme precipitation in Europe under current climate conditions. The suite of indicators include recent historic records, recurrence intervals, and other relevant statistical measures to evaluate the magnitude and frequency of extreme precipitation events. These are provided as gridded products, with one product covering the whole of Europe, and the other higher resolution product focused on 20 European cities that were identified as vulnerable to urban pluvial flooding based on stakeholder surveys.

calendar_today Interval/period: Sun, 01/01/1950 - Tue, 12/31/2019

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Fire activity analyses

CAMS fire activity analyses

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Fire burned area from 2001 to present derived from satellite observations

The Burned Area products provide global information of total burned area (BA) at pixel and grid scale. The BA is identified with the date of first detection of the burned signal in the case of the pixel product, and with the total BA per grid cell in the case of the grid product. The products were obtained through the analysis of reflectance changes from medium resolution sensors (Terra MODIS, Sentinel-3 OLCI), supported by the use of MODIS thermal information.

calendar_today Interval/period: Mon, 01/01/2001 - Fri, 04/01/2022

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Fire danger indicators for Europe from 1970 to 2098 derived from climate projections

The dataset presents projections of fire danger indicators for Europe based upon the Canadian Fire Weather Index System (FWI) under future climate conditions. The FWI is a meteorologically based index used worldwide to estimate the fire danger and is implemented in the Global ECMWF Fire Forecasting model (GEFF). In this dataset, daily FWI values, seasonal FWI values, and other FWI derived, threshold-specific, indicators were modelled using the GEFF model to estimate the fire danger in future climate scenarios.

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Fire danger indices historical data from the Copernicus Emergency Management Service

This data set provides complete historical reconstruction of meteorological conditions favourable to the start, spread and sustainability of fires. The fire danger metrics provided are part of a vast dataset produced by the Copernicus Emergency Management Service for the

calendar_today Interval/period: Wed, 01/03/1940 - Tue, 05/05/2026

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Fire radiative power and active fire pixels from 2020 to present derived from satellite observations

This dataset provides global information on the timing and location of Active Fires (AF) burning on Earth's surface during satellite overpasses, and also records their Fire Radiative Power (FRP) as a measure of strength. FRP relates to a fire's combustion rate, and the rate at which smoke containing greenhouse gases, reactive gases and particular matter is released into the atmosphere. The vast majority of detected hotspots are related to landscape fires, however other high temperature targets such as active volcanoes and gas flares are also present in the data.

calendar_today Interval/period: Wed, 01/01/2020 - Fri, 02/28/2025

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forecast experiment with ECMWF IFS for the EUREC4A period (25km), no dropsondes

Research forecast experiment produced with ECMWF IFS cycle 47R1, at TCo399 (25km), for the EUREC4A field campaign (15.01.2020-14.02.2020). Initialized from the hfa8 analysis, in which no dropsondes are assimilated, with fields saved on model levels (bit identical to hfa8 forecasts)

Examples

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forecast experiment with ECMWF IFS for the EUREC4A period (25km), no dropsondes and no radiosondes

Research forecast experiment produced with ECMWF IFS cycle 47R1, at TCo399 (25km), for the EUREC4A field campaign (15.01.2020-14.02.2020). Initialized from the hfff analysis, in which no dropsondes and no radiosondes are assimilated, with fields saved on model levels (bit identical to hfff forecasts)

Examples

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open_in_newview in IFS Experiments