Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments

Abstract

Abstract. We present a new and improved version (V4.0) of the NASA standard nitrogen dioxide (NO2) product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This version incorporates the most salient improvements for OMI NO2 products suggested by expert users and enhances the NO2 data quality in several ways through improvements to the air mass factors (AMFs) used in the retrieval algorithm. The algorithm is based on the geometry-dependent surface Lambertian equivalent reflectivity (GLER) operational product that is available on an OMI pixel basis. GLER is calculated using the vector linearized discrete ordinate radiative transfer (VLIDORT) model, which uses as input high-resolution bidirectional reflectance distribution function (BRDF) information from NASA's Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instruments over land and the wind-dependent Cox–Munk wave-facet slope distribution over water, the latter with a contribution from the water-leaving radiance. The GLER combined with consistently retrieved oxygen dimer (O2–O2) absorption-based effective cloud fraction (ECF) and optical centroid pressure (OCP) provide improved information to the new NO2 AMF calculations. The new AMFs increase the retrieved tropospheric NO2 by up to 50 % in highly polluted areas; these differences arise from both cloud and surface BRDF effects as well as biases between the new MODIS-based and previously used OMI-based climatological surface reflectance data sets. We quantitatively evaluate the new NO2 product using independent observations from ground-based and airborne instruments. The new V4.0 data and relevant explanatory documentation are publicly available from the NASA Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary/, last access: 8 November 2020), and we encourage their use over previous versions of OMI NO2 products.

Description of the NASA GEOS Composition Forecast Modeling System GEOS‐CF v1.0

“…40%) higher than the OMI observations (Figure 8b), suggesting a potential overestimation of NOx emissions or a longer NOx lifetime in the model (Shah et al, 2020). However, the OMI retrieval algorithm v4.0 tends to underestimate tropospheric NO2 over polluted areas (Lamsal et al, 2021), which complicates the analysis. As further discussed in the next section, the comparison against surface observations does not support the view of a broad-based overestimation of surface NO2 over Asia.…”

“…The data sets used for model validation are summarized in Table 2. Briefly, we evaluate the global tropospheric distribution of O3 against ozonesonde observations obtained from the World Ozone and Ultraviolet Data Center (WOUDC, http://www.woudc.org) and the NOAA Earth System Research Laboratory -Global Monitoring Division (ftp://ftp.cmdl.noaa.gov/ozww/Ozonesonde/), NO2 against the OMI NASA standard tropospheric column NO2 product v4.0 (Lamsal et al, 2021) and CO total columns against satellite retrievals from the Measurements Of Pollution In The Troposphere (MOPITT) V8 (Deeter et al, 2019). All satellite comparisons use model output sampled at satellite overpass time and with averaging kernels applied to it.…”

COVID-19 lockdown-induced changes in NO<sub>2</sub> levels across India observed by multi-satellite and surface observations

“…Nitrogen oxides, NO x (NO + NO 2 ), are one of the major air pollutants, as defined by various national environmental agencies across the world, due to their adverse impact on human health . Furthermore, tropospheric levels of NO x can affect tropospheric ozone formation (Monks et al, 2015), contribute to secondary aerosol formation (Lane et al, 2008) and acid deposition, and impact climatic cycles (Lin et al, 2015). The major anthropogenic sources of NO x emissions include the combustion of fossil fuels in road transport, aviation, shipping, industries, and thermal power plants (e.g.…”

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

“…While the trace-gas algorithms have matured greatly over the past few decades and have been scrutinized by comparisons with independent measurements from ground-and aircraftbased platforms, there is still room for further improvement. For example, it has been long recognized that the effects of aerosols on trace-gas retrievals are significant, particularly in polluted regions, and affect both the trace-gas retrieval itself and cloud retrievals that supply inputs to it (e.g., Martin et al, 2002;Boersma et al, 2004;Leitão et al, 2010;Castellanos et al, 2015;Lorente et al, 2017). Even for clear-sky conditions, aerosols impact trace-gas retrievals in complicated ways due to different optical properties of various aerosol 2858 A.…”

“…Most of these studies focused on the effects of aerosol in clear-sky retrievals. The effects of aerosol in the presence of overlaying cloud layers is important, and Bousserez (2014) and Leitão et al (2010) suggest that explicit account of aerosols in this case may improve NO 2 retrievals in such cases.…”