Comparison of space high-detailed experimental and model data on tropospheric NO2 distribution

Postylyakov, O. V.; Borovski, Alexander; Elansky, N. F.; Давыдова, М. А.; Zakharova, S. A.; Makarenkov, Aleksandr A.

doi:10.1117/12.2540770

Cited by 5 publications

(1 citation statement)

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“…Therefore, it has been a stated and challenging goal of these ocean color missions to quantify NO 2 spatiotemporal variations with their lowerspectral-resolution measurements. NO 2 retrievals have been demonstrated with hyperspectral sensors on aircraft (Tack et al, 2017;Kuhlmann et al, 2022) and the Russian Resurs-P satellite (Postylyakov et al, 2017(Postylyakov et al, , 2019Zakharova et al, 2021); these sensors have somewhat higher spectral resolution than PACE and GLIMR.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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 the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 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 hyperspectral 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 d. 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 (NN) 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. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO2 retrievals. Once trained, NNs 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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Comparison of measured and simulated NO2 integral content in the lower troposphere in Moscow region

Postylyakov

Borovski

Kirsanov

et al. 2020

IOP Conf. Ser.: Earth Environ. Sci.

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We presented preliminary results of a comparison of the NO2 integral contents (IC) measured by the DOAS technique and simulated by the COSMO-Ru7-ART chemical transport model at Zvenigorod Scientific Station (ZSS) located in 38 km west from Moscow. The comparison covers January and July of 2014 when background and polluted by Moscow air masses were observed at ZSS. The measured NO2 IC in the atmospheric boundary layer observed at ZSS does not exceed 0.5 × 1016 molec × cm−2 in background conditions of the atmosphere when non-east wind direction dominated. It grows up to 5.4 × 1016 molec × cm−2 when polluted air masses come from Moscow megacity. Simulated NO2 IC has similar behaviour. As a whole, a good agreement between measured and simulated datasets is observed. Some overestimation of the NO2 emission presents for sources located to the south and north-east from ZSS. Underestimation of the NO2 emission presents for sources located inside of Moscow megacity and located to south-west from ZSS.

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On validation high-detail mapping of tropospheric NO2 using GSA/Resurs-P observations with simulated data

Postylyakov

Borovski

Shukurov

et al. 2020

Remote Sensing of Clouds and the Atmosphere XXV

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Comparison of space high-detailed experimental and model data on tropospheric NO2 distribution

Cited by 5 publications

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

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

Comparison of measured and simulated NO2 integral content in the lower troposphere in Moscow region

On validation high-detail mapping of tropospheric NO2 using GSA/Resurs-P observations with simulated data

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Comparison of space high-detailed experimental and model data on tropospheric NO2 distribution

Cited by 5 publications

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

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

Comparison of measured and simulated NO2 integral content in the lower troposphere in Moscow region

On validation high-detail mapping of tropospheric NO2 using GSA/Resurs-P observations with simulated data

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

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