Earth-o1 learns continuous atmospheric dynamics from ungridded observations and matches operational IFS forecast skill in hindcasts.
Nature619(7970), 533–538 (2023)
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
ToolRosella converts 122 scientific GitHub repositories into 1,580 agent-callable tools at 61.5% success and 4.4× human speed, supporting 84% downstream task success.
STARQ uses a SegFormer-based multi-scale transformer with Gaussian-kernel pseudo-label propagation from sparse OpenAQ stations to downscale CAMS PM2.5 forecasts from 0.4° to 0.01° (~1 km) across Europe, achieving MAE 5.87 and R² 0.24 on held-out stations.
Probabilistic bias correction doubles ECMWF AI subseasonal skill and improves debiased dynamical forecasts for most pressure, temperature, and precipitation targets, winning ECMWF’s 2025 real-time competition.
citing papers explorer
-
Earth-o1: A Grid-free Observation-native Atmospheric World Model
Earth-o1 learns continuous atmospheric dynamics from ungridded observations and matches operational IFS forecast skill in hindcasts.
-
ToolRosella: Translating Code Repositories into Standardized Tools for Scientific Agents
ToolRosella converts 122 scientific GitHub repositories into 1,580 agent-callable tools at 61.5% success and 4.4× human speed, supporting 84% downstream task success.
-
Air Quality Downscaling with Station-Guided Pseudo-Supervision
STARQ uses a SegFormer-based multi-scale transformer with Gaussian-kernel pseudo-label propagation from sparse OpenAQ stations to downscale CAMS PM2.5 forecasts from 0.4° to 0.01° (~1 km) across Europe, achieving MAE 5.87 and R² 0.24 on held-out stations.
-
Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction
Probabilistic bias correction doubles ECMWF AI subseasonal skill and improves debiased dynamical forecasts for most pressure, temperature, and precipitation targets, winning ECMWF’s 2025 real-time competition.