Machine learning tools for detecting methane plumes in noisy satellite data

I’m training convolutional neural networks to localize methane plumes in noisy GHGSat satellite observations.


Estimating methane emissions from coal mine vents using GHGSat-D satellite observations

I’m estimating methane emissions from coal mines in the United States, China, and Australia by aggregating GHGSat-D observations in time. You can see some preliminary results here.


Source rate retrieval algorithms for quantifying methane point sources from space

I developed algorithms for retrieving emission rates from fine-resolution satellite observations of atmospheric methane plumes. You can read more about them in this paper.


Previous projects

Star forming galaxies in a galaxy supercluster
Temperature variability and climate