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

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 (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 to improve the interpretability of the model. Surface winds and a precomputed Gaussian plume from the receptor are identified to be the most important variables for footprint emulation. The FootNet emulator developed here may help address the computational bottleneck of flux inversions using dense observations.