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Towards emulated Lagrangian particle dispersion model footprints for satellite observations

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Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships (“footprints”) for use in greenhouse gas (GHG) flux inversions. They do not scale well to very large datasets, which makes them unsuitable for use in GHG inversions using high-resolution satellite instruments such as TROPOMI . In this work, we demonstrate how Machine Learning (ML) can be used to accelerate footprint production, by first presenting a proof-of-concept emulator for ground-based site observations, and then discussing a new emulator architecture for LPD Ms applied to satellite observations. We propose a Encode-Process-Decode Graph Network architecture and show some preliminary results for footprints predicted over Brazil, for a reduced domain.

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Topic: CAS Seminar: Dr Elena Fillola Mayoral Time: May 9, 2023 02:00 PM London

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Meeting ID: 878 2425 3668 Passcode: 961020

This talk is part of the Centre for Atmospheric Science seminars, Chemistry Dept. series.

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