Averaging Atmospheric Gas Concentration Data using Wasserstein Barycenters
Mathieu Barr\'e, Cl\'ement Giron, Matthieu Mazzolini, Alexandre, d'Aspremont
TL;DR
This paper introduces a novel method using Wasserstein barycenters and weather data to improve the averaging of atmospheric gas concentration data, aiming to better identify emission sources from satellite imagery.
Contribution
The paper presents a new approach that combines Wasserstein barycenters with weather data to enhance the analysis of greenhouse gas concentrations in satellite images.
Findings
Wasserstein barycenters better localize emission sources.
Method improves accuracy over simple averaging.
Coupling with weather data enhances results.
Abstract
Hyperspectral satellite images report greenhouse gas concentrations worldwide on a daily basis. While taking simple averages of these images over time produces a rough estimate of relative emission rates, atmospheric transport means that simple averages fail to pinpoint the source of these emissions. We propose using Wasserstein barycenters coupled with weather data to average gas concentration data sets and better concentrate the mass around significant sources.
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Taxonomy
TopicsAtmospheric and Environmental Gas Dynamics · Hydrocarbon exploration and reservoir analysis · Geochemistry and Geologic Mapping