An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO2 Retrievals.

Dustin Roten,Dien Wu,John C Lin,Benjamin Fasoli,Tomohiro Oda

doi:10.1029/2020ea001343

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.

Highlights

As global concentrations of carbon dioxide pass the 400 ppm milestone (Keeling & Keeling, 2017), the focus on its mitigation has increased at the local, national, and international levels, guiding the implementation of several policies
X-Stochastic Time-Inverted Lagrangian Transport (STILT) footprints are used in tandem with an emission inventory to determine a priori column-averaged CO2 enhancements (Equation 2); the evaluation of this interpolation method first considered only the summed values of footprint elements to remove any potential influences from Open-source Data Inventory for Anthropogenic CO2 (ODIAC)
The traditional X-STILT approach requires rigorous computation to determine the upwind influences associated with each OCO-3 sounding

Summary

Introduction

As global concentrations of carbon dioxide pass the 400 ppm milestone (Keeling & Keeling, 2017), the focus on its mitigation has increased at the local, national, and international levels, guiding the implementation of several policies. Examples of such policies include the Kyoto Protocol (2005) and Paris Climate Agreement (2016), developed and implemented at the international level. Several local-level policies have been implemented to address the CO2 emissions from cities, one notable example being the C40 city network (Davidson et al, 2019). Since monitoring the CO2 fluxes of these urban areas is of scientific and political importance, research efforts have equipped several cities with mobile and stationary ground-based measurement networks

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