Explicit and consistent aerosol correction for visible wavelength satellite cloud and nitrogen dioxide retrievals based on optical properties from a global aerosol analysis

Abstract

Abstract. We discuss an explicit and consistent aerosol correction for cloud and NO2 retrievals that are based on the mixed Lambertian-equivalent reflectivity (MLER) concept. We apply the approach to data from the Ozone Monitoring Instrument (OMI) for a case study over northeastern China. The cloud algorithm reports an effective cloud pressure, also known as cloud optical centroid pressure (OCP), from oxygen dimer (O2−O2) absorption at 477 nm after determining an effective cloud fraction (ECF) at 466 nm. The retrieved cloud products are then used as inputs to the standard OMI NO2 algorithm. A geometry-dependent Lambertian-equivalent reflectivity (GLER), which is a proxy of surface bidirectional reflectance, is used for the ground reflectivity in our implementation of the MLER approach. The current standard OMI cloud and NO2 algorithms implicitly account for aerosols by treating them as nonabsorbing particulate scatters within the cloud retrieval. To explicitly account for aerosol effects, we use a model of aerosol optical properties from a global aerosol assimilation system and radiative transfer computations. This approach allows us to account for aerosols within the OMI cloud and NO2 algorithms with relatively small changes. We compare the OMI cloud and NO2 retrievals with implicit and explicit aerosol corrections over our study area.

POMINO-GEMS: A Research Product for Tropospheric NO2 Columns from Geostationary Environment Monitoring Spectrometer

“…Finally, the tropospheric SCDs are converted to VCDs using calculated air mass factors (AMFs), which are highly sensitive to the observation geometry, cloud parameters, aerosols, surface conditions and the shape of the NO2 vertical distribution. Over polluted areas, errors in the retrieved tropospheric NO2 VCDs are dominated by the uncertainties in AMF calculations (Boersma et al, 2004;Lorente et al, 2016); errors are sensitive to assumptions on aerosol optical effects, surface reflectance, and a priori NO2 vertical profiles (Zhou et al, 2010;Lin et al, 2014;Lin et al, 2015;Vasilkov et al, 2016;Lorente et al, 2018;Liu et al, 2019;Liu et al, 2020;Vasilkov et al, 2021).…”

Space-Based Observations of Ozone Precursors within California Wildfire Plumes and the Impacts on Ozone-NOx-VOC Chemistry

“…104 Also, satellite retrievals of NO 2 and HCHO are subject to large uncertainty in cloudy areas, 42,43,45 and future work that explicitly accounts for the aerosol effects in cloud retrieval could help reduce such uncertainty. 42,65 Evaluation of TROPOMI retrievals and GEOS-Chem simulated a priori profiles with airborne measurements from field campaigns (e.g., WE-CAN, FIREX-AQ) and ground-based MAX-DOAS measurements is warranted to better understand and quantify the uncertainties of TROPOMI observations under fire smoke. Future work should conduct a more detailed evaluation of reactive nitrogen partitioning and VOC composition in GEOS-Chem to determine the mechanisms affecting the formation and loss of NO 2 and HCHO downwind of fires.…”

“…We use the a priori profiles from GEOS-Chem simulations at 0.25° × 0.3125° resolution, which significantly reduces the resolution-dependent uncertainties in the operational products that use global simulations at 1° resolution, , but improving the spatial resolution of a priori to a resolution of 0.1° or finer could help further resolve the spatial gradients of NO 2 and HCHO within fire plumes. , The fire injection height is assumed to be constant for all fires in GEOS-Chem and HYSPLIT, which is a major source of uncertainty that could lead to errors in simulating the smoke dispersion, especially for extreme fires . Also, satellite retrievals of NO 2 and HCHO are subject to large uncertainty in cloudy areas, ,, and future work that explicitly accounts for the aerosol effects in cloud retrieval could help reduce such uncertainty. , Evaluation of TROPOMI retrievals and GEOS-Chem simulated a priori profiles with airborne measurements from field campaigns (e.g., WE-CAN, FIREX-AQ) and ground-based MAX-DOAS measurements is warranted to better understand and quantify the uncertainties of TROPOMI observations under fire smoke. Future work should conduct a more detailed evaluation of reactive nitrogen partitioning and VOC composition in GEOS-Chem to determine the mechanisms affecting the formation and loss of NO 2 and HCHO downwind of fires.…”

“…We used the cloud pressure and fraction data included in the standard TROPOMI products, which are derived from the TROPOMI O 2 -O 2 cloud product . The retrieval of clouds implicitly accounts for the aerosol effects, and thus explicit correction of aerosol effects may result in a double-counting of the aerosol impacts on AMF. ,, Lin et al found that explicitly accounting for aerosol effects in cloud retrieval results in an overall 20% reduction in the cloud radiance fraction. Therefore, we reduced the radiance cloud fraction by 20% in our AMF calculation to reduce the double-counting aerosol effects on the retrievals.…”

Background nitrogen dioxide (NO2) over the United States and its implications for satellite observations and trends: effects of nitrate photolysis, aircraft, and open fires

“…On the west coast, high biases are found in fire-influenced areas but that may be due to OMI retrievals being too low. Most current operational NO2 retrieval algorithms including OMNO2 v4 treat aerosols implicitly (Liu et al, 2020;Vasilkov et al, 2021), resulting in low retrieved NO2 in fire plumes (Griffin et al, 2021). Travis et al (2016) previously found a 30% GEOS-Chem overestimate of the NASA OMI NO2 v2.1 retrieval over the Southeast US in summer, which they attributed to the NEI2011 emissions being too high, but that model bias is largely corrected in our simulation due both to downward revision of NOx emissions in NEI2016 and to 10-40% higher OMI NO2 retrievals in version 4 of OMNO2 relative to previous versions (Lamsal et al, 2021).…”