19 Sep 2022
19 Sep 2022
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Use of machine learning to retrieve nitrogen dioxide with hyperspectral imagers in the ultraviolet and blue spectral range

Joanna Joiner1, Sergey Marchenko2, Zachary Fasnacht2, Lok Lamsal3, Can Li4, Alexander Vasilkov2, and Nickolay Krotkov1 Joanna Joiner et al.
  • 1National Aeronautics and Space Administration, Goddard Space Flight Center, Laboratory for Atmospheric Chemistry and Dynamics, Code 614, Greenbelt, MD USA
  • 2Science Systems and Applications, Inc., Lanham, MD USA
  • 3University of Maryland, Baltimore County, MD USA
  • 4University of Maryland, College Park, MD USA

Abstract. Nitrogen dioxide (NO2) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO2 column densities have been retrieved with satellite UV-Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.5 km × 5 km. These NO2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO2 amounts with lower spectral resolution hyper-spectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼1 km with global coverage in 1–2 days. At this spectral resolution, small scale spectral structure from NO2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO2 slant column densities (SCDs) with an artificial neural network trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO2 retrievals. Nevertheless, the NO2 information from OCI may be of value for ocean color retrievals, as our simulations suggest that it will be of similar or slightly better quality as compared with TROPOMI NO2 data at TROPOMI spatial resolution on a daily basis and will be available simultaneously. OCI retrievals can also be temporally averaged over time-scales of the order months to reduce noise and provide higher spatial resolution maps that may be useful for downscaling information provided by lower spatial resolution instruments such as OMI and TROPOMI, for high resolution emissions estimates, and other applications. In addition, we explore the possibility of using an extended fitting window for NO2 retrievals as compared with traditional approaches. We demonstrate that the use of an extended spectral fitting window can reduce random errors in a current state-of-the-art OMI NO2 SCD product. Machine learning approaches with extended fitting windows, once trained, can also substantially speed up NO2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

Joanna Joiner et al.

Status: open (until 24 Oct 2022)

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Joanna Joiner et al.

Joanna Joiner et al.


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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 we developed here, based on machine learning, can also be applied to instruments already in space to speed up algorithms and reduce effects of noise.