Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 2
Articles | Volume 16, issue 2
https://doi.org/10.5194/amt-16-481-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-481-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 2
Research article
 | 
26 Jan 2023
Research article |  | 26 Jan 2023

Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO2) with hyperspectral imagers and reduce noise in spectral fitting

Joanna Joiner, Sergey Marchenko, Zachary Fasnacht, Lok Lamsal, Can Li, Alexander Vasilkov, and Nickolay Krotkov

Data sets

OMI/Aura Level 1B UV Global Geolocated Earthshine Radiances 1-orbit L2 Swath 13x24 km V003 Marcel Dobber https://doi.org/10.5067/AURA/OMI/DATA1002

OMI/Aura Level 1B VIS Global Geolocated Earthshine Radiances 1-orbit L2 Swath 13x24 km V003 Marcel Dobber https://doi.org/10.5067/AURA/OMI/DATA1004

OMI/Aura Nitrogen dioxide (NO2) total and Tropospheric Column 1-orbit L2 Swath 13x24 km V003 Nickolay A. Krotkov, Lok N. Lamsal, Sergey V. Marchenko, Eric J.Bucsela, William H. Swartz, Joanna Joiner and the OMI core team https://doi.org/10.5067/Aura/OMI/DATA2017

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Short summary
Nitrogen dioxide (NO2) is an important trace gas for both air quality and climate. NO2 affects satellite ocean color products. A new ocean color instrument – OCI (Ocean Color Instrument) – will be launched in 2024 on a NASA satellite. We show that it will be possible to measure NO2 from OCI even though it was not designed for this. The techniques developed here, based on machine learning, can also be applied to instruments already in space to speed up algorithms and reduce the effects of noise.
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