Assessing the feasibility of using a neural network to filter Orbiting Carbon Observatory 2 (OCO-2) retrievals at northern high latitudes

Mendonca, Joseph; Nassar, Ray; O'Dell, Christopher W.; Kivi, Rigel; Morino, Isamu; Notholt, Justus; Petri, Christof; Strong, Kimberly; Wunch, Debra

doi:https://doi.org/10.5194/amt-14-7511-2021

Articles | Volume 14, issue 12

https://doi.org/10.5194/amt-14-7511-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-14-7511-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 12

Research article

|

03 Dec 2021

Research article |

| 03 Dec 2021

Assessing the feasibility of using a neural network to filter Orbiting Carbon Observatory 2 (OCO-2) retrievals at northern high latitudes

Joseph Mendonca, Ray Nassar, Christopher W. O'Dell, Rigel Kivi, Isamu Morino, Justus Notholt, Christof Petri, Kimberly Strong, and Debra Wunch

Supplement

https://doi.org/10.5194/amt-14-7511-2021-supplement

Data sets

GES DISC data NASA https://oco2.gesdisc.eosdis.nasa.gov/data/OCO2_DATA/OCO2_L2_Lite_FP.10r/

TCCON data from Bremen (DE) J. Notholt, C. Petri, T. Warneke, N. M. Deutscher, M. Palm, M. Buschmann, C. Weinzierl, R. C. Macatangay, and P. Grupe https://doi.org/10.14291/TCCON.GGG2014.BREMEN01.R1

TCCON data from Bialystok (PL) N. M. Deutscher, J. Notholt, J. Messerschmidt, C. Weinzierl, T. Warneke, C. Petri, and P. Grupe https://doi.org/10.14291/TCCON.GGG2014.BIALYSTOK01.R2

TCCON data from Sodankylä (FI) R. Kivi, P. Heikkinen, and E. Kyrö https://doi.org/10.14291/TCCON.GGG2014.SODANKYLA01.R0/1149280

TCCON data from Ny Ålesund, Spitsbergen (NO) J. Notholt, T. Warneke, C. Petri, N. M. Deutscher, C. Weinzierl, M. Palm, and M. Buschmann https://doi.org/10.14291/TCCON.GGG2014.NYALESUND01.R1

TCCON data from East Trout Lake, SK (CA) D. Wunch, J. Mendonca, O. Colebatch, N. T. Allen, J.-F. Blavier, S. Roche, J. Hedelius, G. Neufeld, S. Springett, D. Worthy, R. Kessler, and K. Strong https://doi.org/10.14291/TCCON.GGG2014.EASTTROUTLAKE01.R1

TCCON data from Eureka (CA) K. Strong, S. Roche, J. E. Franklin, J. Mendonca, E. Lutsch, D. Weaver, P. F. Fogal, J. R. Drummond, R., Batchelor, and R. Lindenmaier https://doi.org/10.14291/TCCON.GGG2014.EUREKA01.R3

TCCON data from Park Falls (US) P. O. Wennberg, C. M. Roehl, D. Wunch, G. C. Toon, J.-F. Blavier, R. Washenfelder, G. Keppel-Aleks, N. T. Allen, and J. Ayers https://doi.org/10.14291/TCCON.GGG2014.PARKFALLS01.R1

TCCON data from Rikubetsu (JP) I. Morino, N. Yokozeki, T. Matsuzaki, and M. Horikawa https://doi.org/10.14291/TCCON.GGG2014.RIKUBETSU01.R2

Short summary

Machine learning has become an important tool for pattern recognition in many applications. In this study, we used a neural network to improve the data quality of OCO-2 measurements made at northern high latitudes. The neural network was trained and used as a binary classifier to filter out bad OCO-2 measurements in order to increase the accuracy and precision of OCO-2 XCO₂ measurements in the Boreal and Arctic regions.