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GRAPHDOP — Towards Skillful Data-Driven Medium-Range Weather Forecasts
🎧 Abstract:
In this episode, we dive into GraphDOP, a novel data-driven forecasting system developed by ECMWF. Unlike traditional models, GraphDOP learns directly from Earth System observations—without relying on physics-based reanalysis. By capturing relationships between satellite and conventional observations, it builds a latent representation of Earth’s dynamic systems and delivers accurate weather forecasts up to five days ahead.
đź“Ś Bullet points summary:
GraphDOP is developed by ECMWF and operates purely on observational data, without physics-based (re)analysis or feedback.
Produces skillful forecasts for surface and upper-air parameters up to five days into the future.
Competes with ECMWF’s IFS for two-metre temperature (t2m), outperforming it in the Tropics at 5-day lead times.
Can generate forecasts at any time and location—even where observational data is sparse—without using gridded ERA5 fields for training.
Combines data from various instruments to create accurate joint forecasts of surface and tropospheric temperatures in the Tropics.
Learns observation relationships that generalize well to data-sparse regions, with upper-level wind forecasts aligning closely with ERA5 even in low-coverage areas.
đź’ˇ The Big Idea:
GraphDOP reimagines weather forecasting by proving that pure observational data—when paired with intelligent modeling—can rival and even surpass traditional, physics-based systems in both speed and accuracy.
đź“š Citation:
Alexe, Mihai, et al. "GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations." arXiv preprint arXiv:2412.15687 (2024). https://doi.org/10.48550/arXiv.2412.15687
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By Amirpasha🎧 Abstract:
In this episode, we dive into GraphDOP, a novel data-driven forecasting system developed by ECMWF. Unlike traditional models, GraphDOP learns directly from Earth System observations—without relying on physics-based reanalysis. By capturing relationships between satellite and conventional observations, it builds a latent representation of Earth’s dynamic systems and delivers accurate weather forecasts up to five days ahead.
đź“Ś Bullet points summary:
GraphDOP is developed by ECMWF and operates purely on observational data, without physics-based (re)analysis or feedback.
Produces skillful forecasts for surface and upper-air parameters up to five days into the future.
Competes with ECMWF’s IFS for two-metre temperature (t2m), outperforming it in the Tropics at 5-day lead times.
Can generate forecasts at any time and location—even where observational data is sparse—without using gridded ERA5 fields for training.
Combines data from various instruments to create accurate joint forecasts of surface and tropospheric temperatures in the Tropics.
Learns observation relationships that generalize well to data-sparse regions, with upper-level wind forecasts aligning closely with ERA5 even in low-coverage areas.
đź’ˇ The Big Idea:
GraphDOP reimagines weather forecasting by proving that pure observational data—when paired with intelligent modeling—can rival and even surpass traditional, physics-based systems in both speed and accuracy.
đź“š Citation:
Alexe, Mihai, et al. "GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations." arXiv preprint arXiv:2412.15687 (2024). https://doi.org/10.48550/arXiv.2412.15687