Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gas algorithms make use of climatological surface reflectivity databases. For example, cloud and NO₂ retrievals for the Ozone Monitoring Instrument (OMI) use monthly gridded surface reflectivity climatologies that do not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun–sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (LER). Implementation within the existing OMI cloud and NO₂ retrieval infrastructure requires changes only to the input surface reflectivity database. The geometry-dependent LER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from the Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the Cox–Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare the geometry-dependent and climatological LERs for two wavelengths, 354 and 466 nm, that are used in OMI cloud algorithms to derive cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and geometry-dependent LERs is carried out. Geometry-dependent LER and corresponding retrieved cloud products are then used as inputs to our OMI NO₂ algorithm. We find that replacing the climatological OMI-based LERs with geometry-dependent LERs can increase NO₂ vertical columns by up to 50 % in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO₂ columns (within 5 %) are found over unpolluted and overcast areas.

We discuss a new cloud algorithm that retrieves 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, a wavelength not significantly affected by trace-gas absorption and rotational Raman scattering. The retrieved cloud products are intended for use as inputs to the operational nitrogen dioxide (NO₂) retrieval algorithm for the Ozone Monitoring Instrument (OMI) flying on the Aura satellite. The cloud algorithm uses temperature-dependent O₂-O₂ cross sections and incorporates flexible spectral fitting techniques that account for specifics of the surface reflectivity. The fitting procedure derives O₂-O₂ slant column densities (SCDs) from radiances after O₃, NO₂, and H₂O absorption features have been removed based on estimates of the amounts of these species from independent OMI algorithms. The cloud algorithm is based on the frequently used mixed Lambertian-equivalent reflectivity (MLER) concept. 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 OCP is derived from a match of the measured O₂-O₂ SCD to that calculated with the MLER method. Temperature profiles needed for computation of vertical column densities are taken from the Global Modeling Initiative (GMI) model. We investigate the effect of using GLER instead of climatological LER on the retrieved ECF and OCP. For evaluation purposes, the retrieved ECFs and OCPs are compared with those from the operational OMI cloud product, which is also based on the same O₂-O₂ absorption band. Impacts of the application of the newly developed cloud algorithm to the OMI NO₂ retrieval are discussed.

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.

Quantifying the impact of rotational-Raman scattering (RRS) on the O₂ A- and B-bands is important as these bands can be used for cloud and aerosol characterization for trace-gas retrievals including CO₂ and CH₄. In this paper, we simulate the spectral effects of RRS for various viewing geometries and instruments with different spectral resolutions. We also examine how aerosols affect the amount of RRS filling-in. We show that the filling-in effects of RRS are relatively small, but not negligible, in these O₂ absorption bands, particularly for high-spectral-resolution instruments. For comparison, we also compare and contrast the spectral signatures of RRS with those of terrestrial chlorophyll fluorescence.

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor (TSIS-1), satellite estimates of cloud/surface reflectivity, ozone from the Ozone Monitoring Instrument (OMI) and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) with radiative transfer computations in the ocean-atmosphere system. A comparison of the estimates of collocated OMI-derived surface irradiance with Marine Optical Buoy (MOBY) measurements shows a good agreement within 5% for different seasons. To estimate scalar irradiance at the ocean surface and in water, we propose scaling the planar irradiance, calculated from satellite observation, on the basis of Hydrolight computations. Hydrolight calculations show that the diffuse attenuation coefficients of scalar and planar irradiance with depth are quite close to each other. That is why the differences between the planar penetration and scalar penetration depths are small and do not exceed a couple of meters. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in water that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in Case I waters using an inherent optical properties (IOP) model. Other input parameters are less significant. The DNA damage penetration depths vary from a few meters in areas of productive waters to about 30–35 m in the clearest waters. A machine learning approach (an artificial neural network, NN) was developed based on the full physical algorithm for computational efficiency. The NN shows a very good performance in predicting the penetration depths (within 2%).

Ultraviolet (UV) data collected over the ocean by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory ( DSCOVR ) are used. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm adapted for EPIC processing performs cloud detection, aerosol retrievals, and atmospheric correction providing the water-leaving reflectance of the ocean at 340 and 388 nm. The water-leaving reflectance is an indicator of the presence of absorbing and scattering constituents in seawater. The retrieved water-leaving reflectance is compared with full radiative transfer calculations based on a model of inherent optical properties (IOP) of ocean water in UV. The model is verified with data collected on the Aerosol Characterization Experiments (ACE) Asia cruise supported by the NASA Sensor Intercomparison for Marine Biological and Interdisciplinary Ocean Studies (SIMBIOS) project. The model assumes that the ocean water IOPs are parameterized through a chlorophyll concentration. The radiative transfer simulations were carried out using the climatological chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite. The EPIC-derived water-leaving reflectance is also compared with climatological Lambertian-equivalent reflectivity (LER) of the ocean derived from measurements of the Ozone Monitoring Instrument (OMI) on board the NASA polar-orbiting Aura satellite. The EPIC reflectance agrees well (within 0.01) with the model reflectance except for oligotrophic oceanic areas. For those areas, the model reflectance is biased low by about 0.01 at 340 nm and up to 0.03 at 388 nm. The OMI-derived climatological LER is significantly higher than the EPIC water-leaving reflectance, largely due to the surface glint contribution. The globally averaged difference is about 0.04.