Comment on egusphere-2022-806

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> 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 &times; 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 &sim;1 km with global coverage in 1&ndash;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.