Learned 1D Passive Scalar Advection to Accelerate Chemical Transport Modeling: A Case Study with GEOS-FP Horizontal Wind Fields

Abstract

Abstract We developed and applied a machine-learned discretization for one-dimensional (1D) horizontal passive scalar advection, which is an operator component common to all chemical transport models (CTMs). Our learned advection scheme resembles a second-order accurate, three-stencil numerical solver but differs from a traditional solver in that coefficients for each equation term are output by a neural network rather than being theoretically derived constants. We subsampled higher-resolution simulation results—resulting in up to 16× larger grid size and 64× larger time step—and trained our neural-network-based scheme to match the subsampled integration data. In this way, we created an operator that has low resolution (in time or space) but can reproduce the behavior of a high-resolution traditional solver. Our model shows high fidelity in reproducing its training dataset (a single 10-day 1D simulation) and is similarly accurate in simulations with unseen initial conditions, wind fields, and grid spacing. In many cases, our learned solver is more accurate than a low-resolution version of the reference solver, but the low-resolution reference solver achieves greater computational speedup (500× acceleration) over the high-resolution simulation than the learned solver is able to (18× acceleration). Surprisingly, our learned 1D scheme—when combined with a splitting technique—can be used to predict 2D advection and is in some cases more stable and accurate than the low-resolution reference solver in 2D. Overall, our results suggest that learned advection operators may offer a higher-accuracy method for accelerating CTM simulations as compared to simply running a traditional integrator at low resolution. Significance Statement Chemical transport modeling (CTM) is an essential tool for studying air pollution. CTM simulations take a long computing time. Modeling pollutant transport (advection) is the second most computationally intensive part of the model. Decreasing the resolution not only reduces the advection computing time but also decreases accuracy. We employed machine learning to reduce the resolution of advection while keeping the accuracy. We verified the robustness of our solver with several generalization testing scenarios. In our 2D simulation, our solver showed up to 100 times faster simulation with fair accuracy. Integrating our approach to existing CTMs will allow broadened participation in the study of air pollution and related solutions.