Datasets
This experiment is a prototype. It has several important limitations. It is not an ERA6 production. It has known missing components. It should not be used for any application or publication. It should be used to provide feedback to ECMWF.
Examples
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The diagram shows mean errors in position and intensity of tropical cyclones for HRES and ENS. ...
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Interval/period: Mon, 01/01/1979 - Wed, 04/01/2026
The data are bias adjusted using the Distribution Based Scaling (DBS) method versus the global reference dataset HydroGFD2.0, both bias adjustment method and global reference dataset developed by the Swedish Meteorological and Hydrological Institute (SMHI).
The DBS method is a parametric quantile-mapping variant.
Interval/period: Thu, 10/12/2000 - Thu, 10/18/2018
HIRETYCS is the High Resolution Ten Year Climate Simulation. This data set consist of 10-year climate simulations produced at three centres: Centre National de Recherches Météorologiques (CNRM), Max-Planck Institute (MPI) and United Kingdom Met Office.
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ECMWF is a participant in the Development of a European Land Data Assimilation System to predict Floods and Droughts (ELDAS) project funded by the European Union.
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European Reanalysis and Observations for Monitoring project is a EU funded project that provides timely and reliable information about the state and evolution of the European climate. It combines observations from satellites, ground-based stations and results from comprehensive model-based regional reanalyses. By closely monitoring European climate, climate variability and change can be better understood and predicted.
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This is a 4 month seasonal hindcast experiment with CY49R1 at TCo199 initialised from an open loop land-surface analysis (as in iwvp) except that over Eurasia values are replaced with climatological values for the initial day (calculated from the open loop the period 1993-2022)
Examples
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This is a 4 month seasonal hindcast experiment with CY49R1 at TCo199 initialised from an open loop land-surface analysis (as in iwvp) except that over North America values are replaced with climatological values for the initial day (calculated from the open loop the period 1993-2022)
Examples
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Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
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Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
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Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
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Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
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Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
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Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
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FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.
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FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.
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FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.
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FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.
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FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.
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FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.
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FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.
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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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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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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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