The importance of surface reflectance anisotropy for cloud and NO<sub>2</sub> retrievals from GOME-2 and OMI

Abstract

The angular distribution of the light reflected by the Earth's surface influences top-of-atmosphere (TOA) reflectance values. This surface reflectance anisotropy has implications for UV/Vis satellite retrievals of albedo, clouds, and trace gases such as nitrogen dioxide (NO 2 ). These retrievals routinely assume the surface to reflect light isotropically. Here we show that cloud fractions retrieved from GOME-2A and OMI with the FRESCO and OMCLDO2 algorithms have an east-west bias of 10 % to 50 %, which are highest over vegetation and forested areas, and that this bias originates from the assumption of isotropic surface reflection. To interpret the across-track bias with the DAK radiative transfer model, we implement the bidirectional reflectance distribution function (BRDF) from the Ross-Li semi-empirical model. Testing our implementation against state-of-the-art RTMs LIDORT and SCIATRAN, we find that simulated TOA reflectance generally agrees to within 1 %. We replace the assumption of isotropic surface reflection in the equations used to retrieve cloud fractions over forested scenes with scattering kernels and corresponding BRDF parameters from a daily high-resolution database derived from 16 years' worth of MODIS measurements. By doing this, the east-west bias in the simulated cloud fractions largely vanishes. We conclude that across-track biases in cloud fractions can be explained by cloud algorithms that do not adequately account for the effects of surface reflectance anisotropy. The implications for NO 2 air mass factor (AMF) calculations are substantial. Under moderately polluted NO 2 and backwardscattering conditions, clear-sky AMFs are up to 20 % higher and cloud radiance fractions up to 40 % lower if surface anisotropic reflection is accounted for. The combined effect of these changes is that NO 2 total AMFs increase by up to 30 % for backward-scattering geometries (and decrease by up to 35 % for forward-scattering geometries), which is stronger than the effect of either contribution alone. In an unpolluted troposphere, surface BRDF effects on cloud fraction counteract (and largely cancel) the effect on the clearsky AMF. Our results emphasise that surface reflectance anisotropy needs to be taken into account in a coherent manner for more realistic and accurate retrievals of clouds and NO 2 from UV/Vis satellite sensors. These improvements will be beneficial for current sensors, in particular for the recently launched TROPOMI instrument with a high spatial resolution.

In-orbit Earth reflectance validation of TROPOMI on board the Sentinel-5 Precursor satellite

“…In Fig. 4 The deviations found over land are in line with conclusions from a recent study by Lorente et al (2018), who showed that traditional Lambertian surface albedo databases, like the ones described in Sect. 3.4, seriously underestimate the surface reflectivity for certain geometries due to the fact that surface reflectance anisotropy is not accounted for in these databases.…”

“…Adopting Lambertian surface reflection can cause errors especially for the longer wavelengths. For example, neglect of the directional dependence can lead to errors of a factor of two or more in the surface reflectance of vegetated surfaces (Lorente et al, 2018).…”

Underestimation of column NO<sub>2</sub> amounts from the OMI satellite compared to diurnally varying ground-based retrievals from multiple PANDORA spectrometer instruments

“…An improved V2.0 DOMINO retrieval (Boersma et al, 2011) algorithm reduced the retrieval errors while increasing the estimated air mass factor, which reduces the retrieved TCNO 2 by up to 20 % in winter and 10 % in summer. The current versions of OMNO2-NASA (Krotkov et al, 2017) and v2.0 DOMINO (Boersma et al, 2011) are generally in good agreement (Marchenko et al, 2015;Zara et al, 2018). However, the OMNO2-NASA TCNO 2 retrievals are 10 % to 15 % lower than the v2.0 DOMINO retrievals and with Quality Assurance for Essential Climate Variables (QA4ECV) retrievals.…”

Assessment of NO<sub>2</sub> observations during DISCOVER-AQ and KORUS-AQ field campaigns

“…Enhanced agreement with OMI retrievals revised using observed NO 2 profiles is indicative of retrieval errors from model-based a priori vertical NO 2 profile shapes (Figure 8(d-f), Table A3), and highlights the need of approaches to address the issue. Moreover, improved accuracy in other retrieval parameters, both surface and atmospheric, help enhance the quality of satellite NO 2 retrievals (Laughner et al, 2019;Vasilkov et al, 2017Vasilkov et al, , 2018Lorente et al, 2018;Lin et al, 2014Lin et al, , 2015Liu et al, 2019;Noguchi et al, 2014;Zhou et al, 2011) 4 Conclusions…”