On 15 January 2022, a highly explosive eruption of the submarine Hunga volcano (Kingdom of Tonga) generated the largest stratospheric hydration event ever observed and the largest aerosol perturbation since the 1991 Pinatubo eruption. Here, we develop a novel method for satellite retrieval of stratospheric aerosol optical depth (AOD) and layer peak height (zp) using solar backscattered ultraviolet (BUV) radiation; this is made possible by the exceptional mid-stratospheric altitude of the Hunga aerosols. We analyze BUV observations of the Hunga stratospheric aerosol cloud on 17 January 2022 (47 h after the eruption), using BUV band 1 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on board the ESA/Copernicus Sentinel-5 precursor (S5P) satellite, and the Ozone Mapping and Profiling Suite- Nadir Profiler (OMPS-NP) on board the National Oceanic and Atmospheric Administration (NOAA)-20 satellite. We retrieve AOD and zp by fitting hyperspectral BUV radiance ratios in a narrow spectral window restricted to 289-296 nm, chosen in order to reduce interference from tropospheric clouds while highly sensitive to stratospheric aerosols located above ozone peak altitude. The retrieval employs radiative transfer calculations from the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) forward model. We assume a single Hunga aerosol layer composed of polydisperse sulfuric acid spherical particles embedded in a Rayleigh atmosphere with a known ozone profile. The ozone profile is supplied from a version of the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) Stratospheric Composition Reanalysis of the Microwave Limb Sounder (MLS) on board NASA Earth Observing System-chemistry (EOS Aura) satellite - produced by NASA's Global Modeling and Assimilation Office using a stratospheric chemistry model and MERRA-2 meteorology. We also include a sulfur dioxide SO2 layer, which coincides spatially with the retrieved aerosol vertical profile, and with the total loading normalized to the stratospheric SO2 vertical column density from the operational TROPOMI SO2 product. We validate our AOD retrievals against ground-based AErosol RObotic NETwork (AERONET) direct-sun AOD measurements, and zp retrievals against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) overpasses using Lagrangian trajectory modeling. We estimate the total Hunga stratospheric wet aerosol mass (sulfuric acid solution droplets, including water uptake) to be Maer similar to 0.5 +/- 0.05 Tg. This value is consistent with our previous BUV estimates of Hunga SO2 emissions (similar to 0.4-0.5 Tg SO2) and rapid conversion of SO2 to sulfate aerosol. Based on these BUV retrievals we can also estimate the sulfuric acid (H2SO4) mass fraction w similar to 0.4 and H2SO4/H2O solution density: rho similar to 1.34 g cm-3. These new values represent an extreme departure from the stratospheric background sulfate aerosol (Junge layer), which is typified by values of w similar to 0.75 and rho similar to 1.7 g cm-3 supported by decades of observations of the lower stratosphere during both quiescent and volcanically impacted periods. The new low values, inferred from BUV observations and backed up by microphysical modeling, are a result of the uniquely water-rich conditions in the early Hunga plume. Relative humidity in the plume, as modeled by the NASA Goddard Earth Observing System Chemistry-Climate Model with the Community Aerosol and Radiation Model for Atmospheres (CARMA), reached values as high as 60 %, compared to background values closer to 1 %. These findings are unique in the long-term observational record of the stratosphere; similar relative humidities only otherwise occur in overshooting clouds or cold winter hemisphere vortices.

Retrievals of ocean color (OC) properties from space are important for better understanding the ocean ecosystem and carbon cycle. The launch of atmospheric hyperspectral instruments such as the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) and GEMS, provide a unique opportunity to examine the diurnal variability in ocean ecology across various waters in North America and prepare for the future suite of hyperspectral OC sensors. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO OC instrument (OCX), it provides hourly coverage of US coastal regions and great lakes, such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. We will apply our newly developed machine learning (ML) based atmospheric correction approach for OC retrievals to TEMPO data. Our approach begins by decomposing measured hyperspectral radiances into spectral features that explain the variability in atmospheric scattering and absorption as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict OC properties such as chlorophyll concentration derived from collocated MODIS/VIIRS physically-based retrievals. This ML approach compliments the standard radiative transfer-based OC retrievals by providing gap-filling over cloudy regions where the standard algorithms are limited. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate OC properties in less-than-ideal conditions such as lightly to moderately cloudy conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it provides extended spectral measurements at green and red wavelengths which are important particularly for coastal waters. Additionally, our ML technique can be applied to provisional data early in the mission and has potential to demonstrate the value of near real time OC products that are important for monitoring of harmful algae blooms and transient oceanic phenomena.

Abstract This Algorithm Theoretical Basis Document (ATBD) describes the retrieval algorithm and sensitivities of the Version 3 cloud product derived from the spectra collected by the Tropospheric Emissions: Monitoring of POllution (TEMPO) instrument. The cloud product is primarily produced for supporting the retrievals of TEMPO trace gases that are important for understanding atmospheric chemistry and monitoring air pollution. The TEMPO cloud algorithm is adapted from NASA's Ozone Monitoring Instrument (OMI) oxygen collision complex (O2‐O2) cloud algorithm. The retrieval generates effective cloud fraction (ECF) from the normalized radiance at 466 nm and generates cloud optical centroid pressure (OCP) using the O2‐O2 column amount derived from the spectral absorption feature near 477 nm. The slant column of O2‐O2 is retrieved using Smithsonian Astrophysical Observatory's spectral fitting code with optimized retrieval parameters. ECF and OCP are used by TEMPO trace gas retrievals to calculate Air Mass Factors which convert slant columns to vertical columns. The sensitivities of the cloud retrieval to various input parameters are investigated.

Ultraviolet (UV) radiation effects on Earth’s ecosystems on a global scale can be assessed on a basis of satellite estimates of hyperspectral irradiance on the surface and in ocean waters and the spectral biological weighting functions. The satellite UV surface irradiance algorithms combine satellite retrievals of extraterrestrial solar irradiance, cloud/surface reflectivity, aerosol optical depth, and total column ozone with radiative transfer computations. The assessment of in-water irradiance requires additional information on inherent optical properties (IOPs) of ocean water. Our Ozone Monitoring Instrument (OMI) surface hyperspectral irradiance algorithm is updated by implementing a new absorbing aerosol correction based on OMI daily retrievals of UV aerosol absorption optical depth (AAOD). To provide insight into the temporal and spatial variability of absorbing aerosols, we consider a monthly global AAOD climatology derived from the OMI UV aerosol algorithm. Hyperspectral underwater irradiance is computed using Hydrolight radiative transfer calculations along with a Case I water model of IOPs extended into UV. Both planar and scalar irradiances are computed on the Earth’s surface and propagated underwater. The output surface products include the UV index. The output underwater products include the hyperspectral diffuse attenuation coefficients of the planar and scalar irradiances. Effects of the seasonal variability of AAOD on the UV index and the deoxyribonucleic acid (DNA) damage dose rates are considered. The reduction in the UV index and DNA damage dose rate due to the presence of absorbing aerosols can be as large as 30–40%.

For nearly 2 decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO2 column densities from both anthropogenic and volcanic activities. Here, we describe the version-1 NOAA-20 (N20)/OMPS SO2 product (https://doi.org/10.5067/OMPS/OMPS_N20_NMSO2_PCA_L2_Step1.1, Li et al., 2023), aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO2 products, to N20/OMPS. For volcanic SO2 retrievals, the algorithm is identical between N20 and SNPP/OMPS and produces consistent retrievals for eruptions such as Kilauea in 2018 and Raikoke in 2019. For anthropogenic SO2 retrievals, the algorithm has been customized for N20/OMPS, considering its greater spatial resolution and reduced signal-to-noise ratio as compared with SNPP/OMPS. Over background areas, N20/OMPS SO2 slant column densities (SCDs) show relatively small biases, comparable retrieval noise with SNPP/OMPS (after aggregation to the same spatial resolution), and remarkable stability with essentially no drift during 2018-2023. Over major anthropogenic source areas, the two OMPS retrievals are generally well-correlated, but N20/OMPS SO2 is biased low, especially for India and the Middle East, where the differences reach similar to 20 % on average. The reasons for these differences are not fully understood but are partly due to algorithmic differences. Better agreement (typical differences of similar to 10 %-15 %) is found over degassing volcanoes. SO2 emissions (https://doi.org/10.5067/MEASURES/SO2/DATA406, Fioletov et al., 2022) from large point sources, inferred from N20/OMPS retrievals, agree well with those based on OMI, SNPP/OMPS, and the TROPOspheric Monitoring Instrument (TROPOMI), with correlation coefficients >0.98 and overall differences <10 %. The ratios between the estimated emissions and their uncertainties offer insights into the ability of different satellite instruments to detect and quantify SO2 sources. While TROPOMI has the highest ratios of all four sensors, the ratios from N20/OMPS are slightly greater than OMI and substantially greater than SNPP/OMPS. Overall, our results suggest that the version-1 N20/OMPS SO2 product will successfully continue the long-term OMI and SNPP/OMPS SO2 data records. Efforts currently underway will further enhance the consistency of retrievals between different instruments, facilitating the development of multi-decade, coherent global SO2 datasets across multiple satellites.

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.

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 Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.5 km × 5 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 hyper-spectral 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 days. 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 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, as our simulations suggest that it will be of similar or slightly better quality as compared with TROPOMI NO2 data at TROPOMI spatial resolution on a daily basis and will be available simultaneously. OCI retrievals can also be temporally averaged over time-scales of the order months to reduce noise and provide higher spatial resolution maps that may be useful for downscaling information provided by lower spatial resolution instruments such as OMI and TROPOMI, for high resolution emissions estimates, and other applications. In addition, we explore the possibility of using an extended fitting window for NO2 retrievals as compared with traditional approaches. We demonstrate that the use of an extended spectral fitting window can reduce random errors in a current state-of-the-art OMI NO2 SCD product. Machine learning approaches with extended fitting windows, once trained, 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.

Space-based quantitative passive optical remote sensing of the Earth’s surface typically involves the detection and elimination of cloud-contaminated pixels as an initial processing step. We explore a fundamentally different approach; we use machine learning with cloud contaminated satellite hyper-spectral data to estimate underlying terrestrial surface reflectances at red, green, and blue (RGB) wavelengths. An artificial neural network (NN) reproduces land RGB reflectances with high fidelity, even in scenes with moderate to high cloud optical thicknesses. This implies that spectral features of the Earth’s surface can be detected and distinguished in the presence of clouds, even when they are partially and visibly obscured by clouds; the NN is able to separate the spectral fingerprint of the Earth’s surface from that of the clouds, aerosols, gaseous absorption, and Rayleigh scattering, provided that there are adequately different spectral features and that the clouds are not completely opaque. Once trained, the NN enables rapid estimates of RGB reflectances with little computational cost. Aside from the training data, there is no requirement of prior information regarding the land surface spectral reflectance, nor is there need for radiative transfer calculations. We test different wavelength windows and instrument configurations for reconstruction of surface reflectances. This work provides an initial example of a general approach that has many potential applications in land and ocean remote sensing as well as other practical uses such as in search and rescue, precision agriculture, and change detection.

We report numerical results for polarized light reflection from the top of a Rayleigh scattering spherical atmosphere with height-dependent single scattering albedo over a dark surface. Michael Mishchenko considered this scenario back in the 1990's, for a plane-parallel atmosphere of unit optical thickness (OT = 1), for which radiance errors arising from neglecting polarization reaches their highest values. To further extend Mishchenko's results, we consider a value of OT = 0.25, for which the effect of atmospheric curvature is pronounced. New results are generated using three state-of-the art radiative transfer (RT) codes. These are: the MYSTIC and MCSSA models, which simulate light scattering in a true-spherical atmosphere using Monte Carlo methods; and the discrete ordinate code VLIDORT, operating with a new multiple-scatter spherical correction designed to deliver reasonable approximations to spherical-medium scattering. In this work, we report results for both single and multiple scattering; this will help to support the validation of existing and future polarized spherical RT codes, especially those using approximative methods to deal with sphericity. (C) 2022 Elsevier Ltd. All rights reserved.

Retrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwelling radiance from the ocean. The limitations in the atmospheric correction under certain conditions lead to many gaps in daily spatial coverage of ocean color retrievals. To address these limitations, we introduce a new approach that uses machine learning to estimate ocean color from top of atmosphere radiances or reflectance measurements. In this approach, a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere and the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the ozone monitoring instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI), using measurements from 320–500 nm to show that it can be used to reproduce ocean color properties in less-than-ideal conditions. This machine learning approach complements the current atmospheric correction ocean color retrievals by filling in the gaps resulting from cloud, aerosol, and sunglint contamination. This method can be applied to the future hyperspectral Ocean Color Instrument (OCI), which will be onboard NASA’s Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite set to launch in 2024.

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 Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.5 ::: 3.6 km × 5 ::: 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 5 is possible to retrieve NO2 amounts with lower spectral resolution hyper-spectral 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 days. 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 10 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, as our simulations suggest that it will be of similar or slightly better quality as compared with TROPOMI data at TROPOMI spatial resolution on a daily basis and will be available simultaneously. : . OCI retrievals can also be temporally averaged over time-scales of the order :: of months to reduce noise and provide higher spatial resolution maps that 15 may be useful for downscaling information provided by lower spatial resolution ::: data :::::::: provided ::: by instruments such as OMI and TROPOMI, for high : ; ::: this ::::::::::: downscaling ::::: could ::::::::: potentially :::::: enable ::::: higher : resolution emissions estimates , and ::: and :: be :::::: useful :: for : other applications. In addition, we explore the possibility of using an extended fitting window for :::: show :::: that :::: NNs ::: that :::: use :: as ::::: inputs :::::::::: coefficients :: of :::::: leading :::::: modes :: of :: a ::::::: principal :::::::::: component ::::::: analysis :: of ::::::: radiance :::::: spectra :::::: appear :: to :::::: enable ::::: noise :::::::: reduction :: in NO2 retrievals as compared with traditional approaches. We demonstrate that the use of an extended spectral fitting window 20 can reduce random errors in a current state-of-the-art OMI SCD product. Machine learning approaches with extended fitting

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%).

The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) satellite observes the entire Sun-illuminated Earth from sunrise to sunset from the L1 Sun-Earth Lagrange point. The L1 location, however, confines the observed phase angles to ∼2°–12°, a nearly backscattering direction, precluding any information on the bidirectional surface reflectance factor (BRF) or cloud/aerosol phase function. Deploying an analog of EPIC on the Moon’s surface would offer a unique opportunity to image the full range of Earth phases, including observing ocean/cloud glint reflection for different phase angles; monitoring of transient volcanic clouds; detection of circum-polar mesospheric and stratospheric clouds; estimating the surface BRF and full phase-angle integrated albedo; and monitoring of vegetation characteristics for different phase angles.

We have developed a new trajectory tool to reconstruct the altitude and the position of SO2 in a volcanic plume. Starting with 2D map of satellite observed SO2, known volcano location, and reanalysis wind fields from the NASA Goddard Earth Observing System (GEOS) model, the Goddard trajectory tool allows us to estimate the altitude and concentration of SO2 in the volcanic plume at time of observation. We used this tool for the June 21, 2019 Mt. Raikoke eruption and the June 15, 1991 Mt. Pinatubo event. We used SO2 data from the Ozone Mapping and Profiler Suite/Nadir Mapper (OMPS/NM) onboard the NASA-NOAA Suomi satellite and obtained a distribution of SO2 altitudes between 1 and 19 kilometers in different parts of the Raikoke SO2 clouds, with the highest SO2 concentration between 11 and 16 km, in good agreement with data from independent SO2 layer height retrievals from the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft; the Tropospheric Monitoring Instrument (TROPOMI) onboard the European Copernicus Sentinel 5 precursor satellite and Infrared Atmospheric Sounding Interferometer (IASI) on the European Space Agency's (ESA) MetOp series of a polar orbiting satellites. We then applied this method to the Pinatubo eruption using SO2 column measurements from the NASA Total Ozone Mapping Spectrometer (TOMS) and using wind fields from the National Centers for Environmental Prediction Reanalysis version 2. We found that the southern part of the Pinatubo plume is located in the troposphere, and the northern part is in the stratosphere.

Various instrumental or geophysical artifacts, such as saturation, stray light or obstruction of light (either coming from the instrument or related to solar eclipses), negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic-ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sunglint over open water or thick clouds. When saturation occurs, additional photoelectric charge generated at the saturated pixel may overflow to pixels adjacent to a saturated area and be reflected as a distorted image in the final sensor output. When these effects cannot be corrected to an acceptable level for science-quality retrievals, flagging of the affected pixels is indicated. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectra over a 10 nm spectral range; our decorrelation index (DI for brevity) is simply defined as a DI of 1−r. DI increases with anomalous additive effects or excessive noise in either radiances, the most likely cause in data from the Ozone Monitoring Instrument (OMI), or irradiances. DI is relatively straightforward to use and interpret and can be computed for different wavelength intervals. We developed a set of DIs for two spectral channels of the OMI, a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350–498 nm) and six DIs in its ultraviolet 2 (UV2) channel (310–370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary saturation possibility warning (SPW) flags currently provided for each individual spectral or spatial pixel in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.

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.

We have developed a new trajectory tool to reconstruct the altitude and the position of SO2 in a volcanic plume. Starting with 2D map of satellite observed SO2, known volcano location, and reanalysis wind fields from the NASA Goddard Earth Observing System (GEOS) model, the Goddard trajectory tool allows us to estimate the altitude and concentration of SO2 in the volcanic plume at time of observation. We used this tool for the June 21, 2019 Mt. Raikoke eruption and the June 15, 1991 Mt. Pinatubo event. We used SO2 data from the Ozone Mapping and Profiler Suite/Nadir Mapper (OMPS/NM) onboard the NASA-NOAA Suomi satellite and obtained a distribution of SO2 altitudes between 1 and 19 kilometers in different parts of the Raikoke SO2 clouds, with the highest SO2 concentration between 11 and 16 km, in good agreement with data from independent SO2 layer height retrievals from the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft; the Tropospheric Monitoring Instrument (TROPOMI) onboard the European Copernicus Sentinel 5 precursor satellite and Infrared Atmospheric Sounding Interferometer (IASI) on the European Space Agency's (ESA) MetOp series of a polar orbiting satellites. We then applied this method to the Pinatubo eruption using SO2 column measurements from the NASA Total Ozone Mapping Spectrometer (TOMS) and using wind fields from the National Centers for Environmental Prediction Reanalysis version 2. We found that the southern part of the Pinatubo plume is located in the troposphere, and the northern part is in the stratosphere.

The 21 June 2019 eruption of the Raikoke volcano (Kuril Islands, Russia; 48∘ N, 153∘ E) produced significant amounts of volcanic aerosols (sulfate and ash) and sulfur dioxide (SO2) gas that penetrated into the lower stratosphere. The dispersed SO2 and sulfate aerosols in the stratosphere were still detectable by multiple satellite sensors for many months after the eruption. For this study of SO2 and aerosol clouds we use data obtained from two of the Ozone Mapping and Profiler Suite sensors on the Suomi National Polar-orbiting Partnership satellite: total column SO2 from the Nadir Mapper and aerosol extinction profiles from the Limb Profiler as well as other satellite data sets. We evaluated the limb viewing geometry effect (the “arch effect”) in the retrieval of the LP standard aerosol extinction product at 674 nm. It was shown that the amount of SO2 decreases with a characteristic period of 8–18 d and the peak of stratospheric aerosol optical depth recorded at a wavelength of 674 nm lags the initial peak of SO2 mass by 1.5 months. Using satellite observations and a trajectory model, we examined the dynamics of an unusual atmospheric feature that was observed, a stratospheric coherent circular cloud of SO2 and aerosol from 18 July to 22 September 2019.

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.

Abstract. Non-linear effects, such as from saturation, stray light, or obstruction of light, negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sun glint over water surfaces or thick clouds. Related residual electronic cross-talk or blooming effects may occur in spatial pixels adjacent to a saturated area. Obstruction of light can occur within the zones of solar eclipses as well as from material located outside of the satellite instrument. The latter may also produce unintended scattered light into a satellite instrument. When these effects cannot be corrected to an acceptable level for science quality retrievals, it is desirable to flag the affected pixels. Here, we introduce a new detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectra over a 10 nm-spectral range; our Decorrelation Index (DI for brevity) is simply defined as DI=1−r. DI increases with non-linear effects or excessive noise in either radiances (the most likely cause in OMI data) or irradiances. DI is relatively straight-forward to use and interpret and can be computed for different wavelength intervals. We developed a set of DIs for two spectral channels of the Ozone Monitoring Instrument (OMI), a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350–498 nm) and 6 DIs in its ultraviolet 2 (UV2) channel (310–370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary Saturation Possibility Warning (SPW) flags currently provided for each individual spectral/spatial pixels in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.