FootNet v1.0: development of a machine learning emulator of atmospheric transport

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 the measurement “footprint”. Computing and storing footprints using full-physics models is becoming expensive due to the requirement to simulate atmospheric transport at high resolution. We present the development of FootNet, a deep-learning emulator of footprints at the kilometer scale. We train and evaluate the emulator using footprints simulated with a Lagrangian particle dispersion model (LPDM). FootNet predicts the magnitudes and extents of footprints in near real time with high fidelity. We identify the relative importance of input variables of FootNet for improving the interpretability of the model. Surface winds and a precomputed Gaussian plume from the receptor are identified as the most important variables for footprint emulation. The FootNet emulator developed here may help address the computational bottleneck of flux inversions using dense observations.