papers
Publications (2)
physics.ao-ph2026
OCELOT: Direct Atmospheric Forecasting from Heterogeneous Earth Observations Using a Graph-Transformer Hybrid Model
Azadeh Gholoubi, Ronald McLaren, Mu-Chieh Ko +7
The paper introduces OCELOT, a machine‑learning system that directly forecasts atmospheric observations up to 12 hours ahead by processing heterogeneous satellite and in‑situ data…
#weather forecasting#machine learning#graph neural networks#satellite observations
physics.ao-ph2020
Global to local impacts on atmospheric CO2 caused by COVID-19 lockdown
Ning Zeng, Pengfei Han, Di Liu +10
The world-wide lockdown in response to the COVID-19 pandemic in year 2020 led to economic slowdown and large reduction of fossil fuel CO2 emissions, but it is unclear how much it w…