An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO2 Retrievals. Issue 4 (2nd April 2021) - Roten, Dustin; Wu, Dien; Fasoli, Benjamin; Oda, Tomohiro; Lin, John C.
Abstract: A growing constellation of satellites is providing near‐global coverage of column‐averaged CO2 observations. Launched in 2019, NASA's OCO‐3 instrument is set to provide XCO2 observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time‐Inverted Lagrangian Transport (X‐STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO‐3 observations and future space‐based column‐observing missions. However, OCO‐3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X‐STILT model to generate upwind influence footprints with less computational expense. The method uses X‐STILT generated influence footprints from a key subset of OCO‐3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO‐3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO‐3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO2 missions are also discussed. Plain Language Summary: Several satellites are providing near‐global observations of Earth's atmospheric carbon dioxide (CO2 ). One example is NASA's new OCO‐3 instrument which is set to provide spatially dense CO2 measurements over targeted areas. Measurements may contain signals of emissions from cities and power plants. One method of finding the source(s) of observed CO2 is using a Lagrangian particle dispersion model such as X‐STILT. This model takes OCO‐3 measurements and runs atmospheric transport backwards in time to trace out the sources affecting these measurements. However, OCO‐3 and future satellite missions will yield many measurements, significantly increasing the computational cost for X‐STILT and other similar models. This paper presents an algorithm that will reduce the computational effort for X‐STILT by tracing the sources of only a subset of OCO‐3 measurements and then infers (interpolates) the rest. The following two questions are addressed: (1) How many OCO‐3 measurements does X‐STILT need for the interpolations to be accurate? (2) How do meteorology and topography affect the accuracy of the interpolations? Applying the algorithm on simulated OCO‐3 data at two test cities—Los Angeles and Salt Lake City—the time required to elucidate the CO2 sources was reduced by 62% and 78%, respectively. Key Points: Determining sources of spatially dense XCO2 observations with LPDM techniques can become time intensive and strain computational resources Presented in this work is an interpolation scheme that eases the computational burden of spatially dense XCO2 source determination studies Evaluating the efficiency of this interpolation scheme revealed reductions of >50% in computational time at two testing locations