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

Roten, Dustin and Wu, Dien and Fasoli, Benjamin and Oda, Tomohiro and Lin, John C. (2021) An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO₂ Retrievals. Earth and Space Science, 8 (4). Art. No. e2020EA001343. ISSN 2333-5084. doi:10.1029/2020ea001343. https://resolver.caltech.edu/CaltechAUTHORS:20210315-100620182

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1029/2020ea001343DOIArticle
https://doi.org/10.5067/W8QGIYNKS3JCDOIOCO-2 data
https://www.ready.noaa.gov/archives.phpRelated ItemHRRR data
https://db.cger.nies.go.jp/dataset/ODIAC/Related ItemODIAC dataset
https://doi.org/10.5281/zenodo.2556989DOICode
ORCID:
AuthorORCID
Roten, Dustin0000-0001-7697-7588
Wu, Dien0000-0002-2915-5335
Fasoli, Benjamin0000-0001-7372-2176
Oda, Tomohiro0000-0002-8328-3020
Lin, John C.0000-0003-2794-184X
Alternate Title:An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO2 Retrievals
Additional Information:© 2021. The Authors. Earth and Space Science published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Issue Online: 02 April 2021; Version of Record online: 02 April 2021; Accepted manuscript online: 12 March 2021; Manuscript accepted: 27 February 2021; Manuscript revised: 24 February 2021; Manuscript received: 09 July 2020. This work was completed under National Aeronautics and Space Administration (NASA) based Grant 80NSSC19K0196. Support from the Environmental Defense Fund is also acknowledged. Additionally, author T. Oda is supported by Grants 80NSSC18K1307 and 80NSSC18K1313. The authors would like to give special thanks to Derek V. Mallia for making his WRF data available for the Salt Lake City area. These data are described in citations Mallia et al. (2015) and Kunik et al. (2019). Support from the University of Utah’s Center for High Performance Computing (CHPC) is also acknowledged. Data Availability Statement: Relevant OCO‐2 data were publicly provided by NASA’s OCO‐2 project and retrieved from the Goddard Earth Science Data and information Services Center (https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_9r/summary, DOI: 10.5067/W8QGIYNKS3JC). The HRRR data used in this work were downloaded from the National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Laboratory (ARL) and is also publicly available at https://www.ready.noaa.gov/archives.php. Additionally, current and previous versions of the ODIAC dataset are maintained by the Center for Global Environmental Research and can be accessed via: http://db.cger.nies.go.jp/dataset/ODIAC/. Details are provided in Oda et al. (2018). Source code for X‐STILT can be found at https://doi.org/10.5281/zenodo.2556989. Lastly, the authors of this work declare no conflicts of interest.
Funders:
Funding AgencyGrant Number
NASA80NSSC19K0196
NASA80NSSC18K1307
NASA80NSSC18K1313
Subject Keywords:interpolation; Lagrangian particle dispersion modeling; land‐atmosphere Interactions; orbiting carbon observatory; space‐based CO2 observations; X‐STILT
Issue or Number:4
DOI:10.1029/2020ea001343
Record Number:CaltechAUTHORS:20210315-100620182
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210315-100620182
Official Citation:Roten, D., Wu, D., Fasoli, B., Oda, T., & Lin, J. C. (2021). An interpolation method to reduce the computational time in the Stochastic Lagrangian particle dispersion modeling of spatially dense XCO2 retrievals. Earth and Space Science, 8, e2020EA001343. https://doi.org/10.1029/2020EA001343
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108425
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:19 Mar 2021 02:08
Last Modified:06 Apr 2021 17:21

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