Datasets

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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.

CAMS fire activity analyses

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

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

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 hg1z analysis, with fields saved on model levels (bit identical to hg1z forecasts)

Examples

Coupled forecast with the new gravity-capillary model and nonlinear renormalisation of the growth rate in ecWAM. Tc1279 resolution (9km), from 2019-03-22 0 UTC, hourly output from step 1 to 72 hours code branch: wab_CY47R3.0_wam.IFS-1944

Examples

Coupled forecast WITHOUT the new gravity-capillary model and nonlinear renormalisation of the growth rate in ecWAM. Tc1279 resolution (9km), from 2019-03-22 0 UTC, hourly output from step 1 to 72 hours code branch: wab_CY47R3.0_wam.IFS-1944

Examples

CAMS formaldehyde forecasts

CAMS aerosol forecasts

The AI Weather Quest (AI WQ), organised by ECMWF, is an ambitious international competition designed to harness artificial intelligence (AI) and machine learning (ML) in advancing weather forecasting.