FootNet: Development of a machine learning emulator of atmospheric transport

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Authors

Tai-Long He , Nikhil Dadheech, Tammy M Thompson, Alexander J. Turner

Abstract

There has been a proliferation of dense observing systems to monitor greenhouse gas (GHG) concentrations over the past decade. Estimating emissions with these observations is often done using an atmospheric transport model to characterize the source-receptor relationship, which is commonly termed measurement ``footprint''. Computing and storing footprints using full-physics models is becoming expensive due to the requirement of simulating atmospheric transport at high resolution. We present the development of FootNet, a deep learning emulator of footprints at kilometer scale. We train and evaluate the emulator using footprints simulated using a Lagrangian particle dispersion model. FootNet predicts the magnitudes and extents of footprints in near-real-time with high fidelity. We identify the relative importance of inputs to FootNet. Surface winds and a precomputed Gaussian plume from the receptor are identified to be the most important variables for footprint emulation. The emulator helps address the computational bottleneck of flux inversions using dense observations.

DOI

https://doi.org/10.31223/X5197G

Subjects

Atmospheric Sciences, Environmental Monitoring

Keywords

Atmospheric inversion, machine learning, Lagrangian particle dispersion model, Measurement footprints

Dates

Published: 2023-12-09 12:42

Last Updated: 2023-12-09 20:42

License

CC BY Attribution 4.0 International