ECMWF
A European intergovernmental organization that operates a leading global numerical weather prediction model and provides medium-range weather forecasting services.
Timeline (last 90 days)
Physics-based precipitation forecasting models struggle to resolve mesoscale convective systems (MCS), because traditional global models are limited by grid resolutions of tens of kilometers and can only rely on parameterization schemes for approximation.
Google's GraphCast is a global medium-range weather forecasting model based on graph neural networks that can generate a ten-day forecast in less than a minute on a single TPU and has surpassed ECMWF's traditional high-resolution model on multiple metrics.
CAMS (Copernicus Atmosphere Monitoring Service) is a global atmospheric composition forecasting system under the EU's Copernicus programme operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), with a spatial resolution of approximately 40 kilometers and freely accessible.
Mainstream AI meteorological models trained on ECMWF ERA5 reanalysis data have approached or even surpassed traditional numerical weather prediction (NWP) on multiple metrics.
ERA5 reanalysis data is a global atmospheric reanalysis dataset released by the European Centre for Medium-Range Weather Forecasts (ECMWF), providing hourly three-dimensional atmospheric state estimates from 1940 to the present.
The most authoritative physics-based forecasting systems currently include ECMWF's IFS model and the U.S. National Weather Service's GFS model.
The European Centre for Medium-Range Weather Forecasts (ECMWF) IFS model generates forecasts by solving systems of partial differential equations for atmospheric motion, with an update cycle typically of 6 to 12 hours.
The IFS of the European Centre for Medium-Range Weather Forecasts (ECMWF) typically runs on a global grid of around 9 kilometers, with each global forecast taking several hours to run on a supercomputer.
DeepMind launched the GraphCast model in 2023, which is based on a graph neural network architecture and surpassed the then state-of-the-art HRES system on more than 90% of metrics in 10-day forecast accuracy on ECMWF's WeatherBench 2 benchmark.
All Facts (10)
GenCast的精度超越欧洲中期天气预报中心（ECMWF）的传统数值模型
85%UnverifiedPhysics-based precipitation forecasting models struggle to resolve mesoscale convective systems (MCS), because traditional global models are limited by grid resolutions of tens of kilometers and can only rely on parameterization schemes for approximation.
50%UnverifiedGoogle's GraphCast is a global medium-range weather forecasting model based on graph neural networks that can generate a ten-day forecast in less than a minute on a single TPU and has surpassed ECMWF's traditional high-resolution model on multiple metrics.
50%UnverifiedCAMS (Copernicus Atmosphere Monitoring Service) is a global atmospheric composition forecasting system under the EU's Copernicus programme operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), with a spatial resolution of approximately 40 kilometers and freely accessible.
50%UnverifiedMainstream AI meteorological models trained on ECMWF ERA5 reanalysis data have approached or even surpassed traditional numerical weather prediction (NWP) on multiple metrics.
50%UnverifiedERA5 reanalysis data is a global atmospheric reanalysis dataset released by the European Centre for Medium-Range Weather Forecasts (ECMWF), providing hourly three-dimensional atmospheric state estimates from 1940 to the present.
50%UnverifiedThe most authoritative physics-based forecasting systems currently include ECMWF's IFS model and the U.S. National Weather Service's GFS model.
50%UnverifiedThe European Centre for Medium-Range Weather Forecasts (ECMWF) IFS model generates forecasts by solving systems of partial differential equations for atmospheric motion, with an update cycle typically of 6 to 12 hours.
50%UnverifiedThe IFS of the European Centre for Medium-Range Weather Forecasts (ECMWF) typically runs on a global grid of around 9 kilometers, with each global forecast taking several hours to run on a supercomputer.
50%UnverifiedDeepMind launched the GraphCast model in 2023, which is based on a graph neural network architecture and surpassed the then state-of-the-art HRES system on more than 90% of metrics in 10-day forecast accuracy on ECMWF's WeatherBench 2 benchmark.
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