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Daniel Varon
Massachusetts Institute of TechnologyMeeting roles in:
Machine learning-based analysis of oil and gas methane emissions in the Gulf of Mexico using publicly available satellite data
Integrating MethaneAIR Aircraft and TROPOMI Satellite Observations in the Integrated Methane Inversion (IMI) to Optimize Methane Emissions
Attributing 2019–2024 methane growth using TROPOMI satellite observations
A High-Temporal-Resolution Methane Budget for California from Multiscale Assimilation of Satellite, In Situ, and Plume Observations
Quantifying methane emission trends in the United States (2019-2024) through high resolution inversion of satellite observations
Benchmarking USA Methane Inventories using GOSAT based Methane Fluxes
Global fine-resolution analytical inversion of TROPOMI methane observations enabled by the stretched-grid high-performance GEOS-Chem model
Quantifying atmospheric methane emissions with satellite observations
Data-Driven Methods for Quantifying Atmospheric Composition: Advances in Computation and Statistical Learning I Oral
Predicting and Correcting the Influence of Boundary Conditions in Regional Inverse Analyses
Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions
Data-Driven Methods for Quantifying Atmospheric Composition: Advances in Computation and Statistical Learning II Poster
Deep Learning for Clouds, Cloud Shadow, and Plume Segmentation in Methane Satellite and Airborne Imaging Spectroscopy
GEO-ring for near-global monitoring of extreme methane point sources
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