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.

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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.

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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.

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Previous projects

Star forming galaxies in a galaxy supercluster
Temperature variability and climate