Improvements to the OMI near UV aerosol algorithm using A-train CALIOP and AIRS observations

Abstract. The height of desert dust and carbonaceous aerosols layers and, to a lesser extent, the difficulty in determining the predominant size mode of these absorbing aerosol types, are sources of uncertainty in the retrieval of aerosol properties from near UV satellite observations. The availability of independent, near-simultaneous measurements of aerosol layer height, and aerosol-type related parameters derived from observations by other A-train sensors, makes possible the use of this information as input to the OMI (Ozone Monitoring Instrument) near UV aerosol retrieval algorithm (OMAERUV). A monthly climatology of aerosol layer height derived from observations by the CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) sensor, and real-time AIRS (Atmospheric Infrared Sounder) CO observations are used in an upgraded version of the OMAERUV algorithm. AIRS CO measurements are used as a reliable tracer of carbonaceous aerosols, which allows the identification of smoke layers in regions and seasons when the dust-smoke differentiation is difficult in the near-UV. The use of CO measurements also enables the identification of elevated levels of boundary layer pollution undetectable by near UV observations alone. In this paper we discuss the combined use of OMI, CALIOP and AIRS observations for the characterization of aerosol properties, and show an improvement in OMI aerosol retrieval capabilities.

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Improvements to the OMI near-UV aerosol algorithm using A-train CALIOP and AIRS observations

Atmospheric Measurement Techniques ◽

10.5194/amt-6-3257-2013 ◽

2013 ◽

Vol 6 (11) ◽

pp. 3257-3270 ◽

Cited By ~ 112

Author(s):

O. Torres ◽

C. Ahn ◽

Z. Chen

Keyword(s):

Retrieval Algorithm ◽

Orthogonal Polarization ◽

Aerosol Layer ◽

Carbonaceous Aerosols ◽

Ozone Monitoring Instrument ◽

Atmospheric Infrared Sounder ◽

Layer Height ◽

Aerosol Retrieval ◽

Polarization Sensor ◽

Aerosol Properties

Abstract. The height of desert dust and carbonaceous aerosols layers and, to a lesser extent, the difficulty in determining the predominant size mode of these absorbing aerosol types, are sources of uncertainty in the retrieval of aerosol properties from near-UV satellite observations. The availability of independent, near-simultaneous measurements of aerosol layer height, and aerosol-type related parameters derived from observations by other A-train sensors, makes possible the use of this information as input to the OMI (ozone monitoring instrument) near-UV aerosol retrieval algorithm (OMAERUV). A monthly climatology of aerosol layer height derived from observations by the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) sensor, and real-time AIRS (Atmospheric Infrared Sounder) carbon monoxide (CO) observations are used in an upgraded version of the OMAERUV algorithm. AIRS CO measurements are used as an adequate tracer of carbonaceous aerosols, which allows the identification of smoke layers in regions and seasons when the dust-smoke differentiation is difficult in the near-UV. The use of CO measurements also enables the identification of high levels of boundary layer pollution undetectable by near-UV observations alone. In this paper we discuss the combined use of OMI, CALIOP and AIRS observations for the characterization of aerosol properties, and show an improvement in OMI aerosol retrieval capabilities.

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The role of cloud contamination, aerosol layer height and aerosol model in the assessment of the OMI near-UV retrievals over the ocean

Atmospheric Measurement Techniques ◽

10.5194/amt-9-3031-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 3031-3052 ◽

Cited By ~ 11

Author(s):

Santiago Gassó ◽

Omar Torres

Keyword(s):

Particle Shape ◽

Spherical Shape ◽

Detailed Examination ◽

Index Of Refraction ◽

Retrieval Algorithm ◽

Orthogonal Polarization ◽

Aerosol Layer ◽

Ozone Monitoring Instrument ◽

Aerosol Model ◽

Modis Aod

Abstract. Retrievals of aerosol optical depth (AOD) at 388 nm over the ocean from the Ozone Monitoring Instrument (OMI) two-channel near-UV algorithm (OMAERUV) have been compared with independent AOD measurements. The analysis was carried out over the open ocean (OMI and MODerate-resolution Imaging Spectrometer (MODIS) AOD comparisons) and over coastal and island sites (OMI and AERONET, the AErosol RObotic NETwork). Additionally, a research version of the retrieval algorithm (using MODIS and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) information as constraints) was utilized to evaluate the sensitivity of the retrieval to different assumed aerosol properties. Overall, the comparison resulted in differences (OMI minus independent measurements) within the expected levels of uncertainty for the OMI AOD retrievals (0.1 for AOD < 0.3, 30 % for AOD > 0.3). Using examples from case studies with outliers, the reasons that led to the observed differences were examined with specific purpose to determine whether they are related to instrument limitations (i.e., pixel size, calibration) or algorithm assumptions (such as aerosol shape, aerosol height). The analysis confirms that OMAERUV does an adequate job at rejecting cloudy scenes within the instrument's capabilities. There is a residual cloud contamination in OMI pixels with quality flag 0 (the best conditions for aerosol retrieval according to the algorithm), resulting in a bias towards high AODs in OMAERUV. This bias is more pronounced at low concentrations of absorbing aerosols (AOD 388 nm  ∼  < 0.5). For higher aerosol loadings, the bias remains within OMI's AOD uncertainties. In pixels where OMAERUV assigned a dust aerosol model, a fraction of them (< 20 %) had retrieved AODs significantly lower than AERONET and MODIS AODs. In a case study, a detailed examination of the aerosol height from CALIOP and the AODs from MODIS, along with sensitivity tests, was carried out by varying the different assumed parameters in the retrieval (imaginary index of refraction, size distribution, aerosol height, particle shape). It was found that the spherical shape assumption for dust in the current retrieval is the main cause of the underestimate. In addition, it is demonstrated in an example how an incorrect assumption of the aerosol height can lead to an underestimate. Nevertheless, this is not as significant as the effect of particle shape. These findings will be incorporated in a future version of the retrieval algorithm.

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Retrievals of Tropospheric Ozone Profiles from the Synergic Observation of AIRS and OMI: Methodology and Validation

10.5194/amt-2018-138 ◽

2018 ◽

Cited By ~ 1

Author(s):

Dejian Fu ◽

Susan S. Kulawik ◽

Kazuyuki Miyazaki ◽

Kevin W. Bowman ◽

John R. Worden ◽

...

Keyword(s):

Standard Deviation ◽

Measurement Uncertainty ◽

Tropospheric Ozone ◽

Retrieval Algorithm ◽

Vertical Resolution ◽

Ozone Monitoring Instrument ◽

Atmospheric Infrared Sounder ◽

Global Coverage ◽

Ozone Monitoring ◽

Wide Swath

Abstract. The Tropospheric Emission Spectrometer (TES) on the A-Train Aura satellite was designed to profile tropospheric ozone and its precursors, taking measurements from 2004 to 2018. Starting in 2008, TES global sampling of tropospheric ozone was gradually reduced in latitude with global coverage stopping in 2011. To extend the record of TES, this work presents a multispectral approach that will provide O3 data products with vertical resolution and measurement uncertainty similar to TES by combining the single-footprint thermal infrared (TIR) hyperspectral radiances from the Aqua Atmospheric Infrared Sounder (AIRS) instrument and the ultraviolet (UV) channels from the Aura Ozone Monitoring Instrument (OMI). The joint AIR+OMI O3 retrievals are processed through the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) retrieval algorithm. Comparisons of collocated joint AIRS+OMI and TES to ozonesonde measurements show that both systems have similar errors, with mean and standard deviation of the differences well within the estimated measurement uncertainty. AIRS+OMI and TES have slightly different biases (within 5 parts per billion) versus the sondes. Both AIRS and OMI have wide swath widths (~ 1,650 km for AIRS; ~ 2,600 km for OMI) across satellite ground tracks. Consequently, the joint AIRS+OMI measurements have the potential to maintain TES vertical sensitivity while increasing coverage by two orders of magnitude, thus providing an unprecedented new dataset to quantify the evolution of tropospheric ozone.

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Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product

Remote Sensing ◽

10.3390/rs12233987 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3987

Author(s):

Sujung Go ◽

Jhoon Kim ◽

Sang Seo Park ◽

Mijin Kim ◽

Hyunkwang Lim ◽

...

Keyword(s):

Spatial Data ◽

Estimation Method ◽

Weighting Factor ◽

Data Availability ◽

Orthogonal Polarization ◽

Aerosol Layer ◽

Ozone Monitoring Instrument ◽

Uv Visible ◽

Type Classification ◽

Aerosol Type

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet (UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of >~0.4 and GEMS SSA values of <~0.95. Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV–visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy.

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Error sources in the retrieval of aerosol information over bright surfaces from satellite measurements in the oxygen A-band

10.5194/amt-2017-323 ◽

2017 ◽

Author(s):

Swadhin Nanda ◽

Martin de Graaf ◽

Maarten Sneep ◽

Johan F. de Haan ◽

Piet Stammes ◽

...

Keyword(s):

Optical Thickness ◽

Surface Albedo ◽

High Surface ◽

Aerosol Optical Thickness ◽

Radiative Transfer Model ◽

Retrieval Algorithm ◽

Transfer Model ◽

Aerosol Layer ◽

Critical Surface ◽

Layer Height

Abstract. Retrieving aerosol optical thickness and aerosol layer height over a bright surface from measured top of atmosphere reflectance spectrum in the oxygen A-band is known to be challenging, often resulting in large errors. In certain atmospheric conditions and viewing geometries, a loss of sensitivity to aerosol optical thickness has been reported in literature. This loss of sensitivity has been attributed to a phenomenon known as critical surface albedo regime, which is a range of surface albedos for which the top of atmosphere reflectance has minimal sensitivity to aerosol optical thickness. This paper extends the concept of critical surface albedo for aerosol layer height retrievals in the oxygen A-band, and discusses its implications. The underlying physics are introduced by analysing top of atmosphere reflectance spectra obtained using a radiative transfer model. Furthermore, error analysis of the aerosol layer height retrieval algorithm are conducted over dark and bright surfaces to show the dependency on surface reflectance. The analysis shows that the information on aerosol layer height from atmospheric path contribution and the surface contribution to the top of atmosphere are opposite in sign – an increase in surface brightness results in a decrease in information content. In the case of aerosol optical thickness, these contributions are anti-correlated, leading to large retrieval errors in high surface albedo regimes. The consequence of this anti-correlation is demonstrated with measured spectra in the oxygen A-band from GOME-2A instrument on board the Metop-A satellite over the 2010 Russian wildfires incident.

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Detecting layer height of smoke aerosols over vegetated land and water surfaces via oxygen absorption bands: Hourly results from EPIC/DSCOVR satellite in deep space

10.5194/amt-2018-414 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xiaoguang Xu ◽

Jun Wang ◽

Yi Wang ◽

Jing Zeng ◽

Omar Torres ◽

...

Keyword(s):

Land Surface ◽

Near Infrared ◽

Oxygen Absorption ◽

Orthogonal Polarization ◽

Aerosol Layer ◽

Deep Space ◽

Absorption Bands ◽

Layer Height ◽

Fitting Procedure ◽

Water Surfaces

Abstract. We present an algorithm for retrieving aerosol layer height (ALH) and aerosol optical depth (AOD) for smoke over vegetated land and water surfaces from measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). The algorithm uses earth-reflected radiances in six EPIC bands in visible and near-infrared and incorporates flexible spectral fittings that account for specifics of land and water surface reflectivity. The fitting procedure first determines AOD using EPIC atmospheric window bands (443 nm, 551 nm, 680 nm, and 780 nm), then uses oxygen (O2) A and B bands (688 nm and 764 nm) to derive the ALH that represents an optical centroid altitude. ALH retrieval over vegetated surface primarily takes advantage of the measurements in the O2 B band. We applied the algorithm to EPIC observations of several biomass burning events over United State and Canada in August 2017. We found the algorithm can well capture AOD and ALH multiple times daily over water and vegetated land surface. Validations are performed against aerosol extinction profile detected by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and AOD observed at nine Aerosol Robotic Network (AERONET) sites, showing, in average, an error of 0.58 km and a bias of −0.13 km in retrieved ALH and an error of 0.05 and a bias of 0.03 in retrieved AOD. Additionally, we show that the aerosol height information retrieved by the present algorithm can potentially benefit the retrieval of aerosol properties from the EPIC’s ultraviolet (UV) bands.

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An online aerosol retrieval algorithm using OMI near-UV observations based on the optimal estimation method

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-16615-2015 ◽

2015 ◽

Vol 15 (12) ◽

pp. 16615-16654 ◽

Cited By ~ 1

Author(s):

U. Jeong ◽

J. Kim ◽

C. Ahn ◽

O. Torres ◽

X. Liu ◽

...

Keyword(s):

Radiative Transfer ◽

Degrees Of Freedom ◽

Northeast Asia ◽

Estimation Method ◽

Optimal Estimation ◽

Single Scattering ◽

Retrieval Algorithm ◽

Ozone Monitoring Instrument ◽

Near Ultraviolet ◽

Aerosol Retrieval

Abstract. An online version of the OMI (Ozone Monitoring Instrument) near-ultraviolet (UV) aerosol retrieval algorithm was developed to retrieve aerosol optical thickness (AOT) and single scattering albedo (SSA) based on the optimal estimation (OE) method. Instead of using the traditional look-up tables for radiative transfer calculations, it performs online radiative transfer calculations with the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model to eliminate interpolation errors and improve stability. The OE-based algorithm has the merit of providing useful estimates of uncertainties simultaneously with the inversion products. The measurements and inversion products of the Distributed Regional Aerosol Gridded Observation Network campaign in Northeast Asia (DRAGON NE-Asia 2012) were used to validate the retrieved AOT and SSA. The retrieved AOT and SSA at 388 nm have a correlation with the Aerosol Robotic Network (AERONET) products that is comparable to or better than the correlation with the operational product during the campaign. The estimated retrieval noise and smoothing error perform well in representing the envelope curve of actual biases of AOT at 388 nm between the retrieved AOT and AERONET measurements. The forward model parameter errors were analyzed separately for both AOT and SSA retrievals. The surface albedo at 388 nm, the imaginary part of the refractive index at 354 nm, and the number fine mode fraction (FMF) were found to be the most important parameters affecting the retrieval accuracy of AOT, while FMF was the most important parameter for the SSA retrieval. The additional information provided with the retrievals, including the estimated error and degrees of freedom, is expected to be valuable for future studies.

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Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

Geoscientific Model Development ◽

10.5194/gmd-9-2377-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 2377-2389 ◽

Cited By ~ 4

Author(s):

Galina Wind ◽

Arlindo M. da Silva ◽

Peter M. Norris ◽

Steven Platnick ◽

Shana Mattoo ◽

...

Keyword(s):

Remote Sensing ◽

Weather Prediction ◽

Retrieval Algorithm ◽

Model Parameters ◽

Aerosol Retrieval ◽

Model Aerosol ◽

Numerical Experimentation ◽

Retrieval Algorithms ◽

Sensor Cloud ◽

Aerosol Properties

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land–ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

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Spaceborne observations of the lidar ratio of marine aerosols

Atmospheric Chemistry and Physics ◽

10.5194/acp-15-3241-2015 ◽

2015 ◽

Vol 15 (6) ◽

pp. 3241-3255 ◽

Cited By ~ 18

Author(s):

K. W. Dawson ◽

N. Meskhidze ◽

D. Josset ◽

S. Gassó

Keyword(s):

Optical Depth ◽

Surface Wind ◽

Spatiotemporal Variability ◽

Retrieval Algorithm ◽

Orthogonal Polarization ◽

Aerosol Layer ◽

Marine Aerosol ◽

Marine Aerosols ◽

Lidar Ratio ◽

The Mean

Abstract. Retrievals of aerosol optical depth (AOD) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite sensor require the assumption of the extinction-to-backscatter ratio, also known as the lidar ratio. This paper evaluates a new method to calculate the lidar ratio of marine aerosols using two independent sources: the AOD from the Synergized Optical Depth of Aerosols (SODA) project and the integrated attenuated backscatter from CALIOP. With this method, the particulate lidar ratio can be derived for individual CALIOP retrievals in single aerosol layer, cloud-free columns over the ocean. Global analyses are carried out using CALIOP level 2, 5 km marine aerosol layer products and the collocated SODA nighttime data from December 2007 to November 2010. The global mean lidar ratio for marine aerosols was found to be 26 sr, roughly 30% higher than the current value prescribed by the CALIOP standard retrieval algorithm. Data analysis also showed considerable spatiotemporal variability in the calculated lidar ratio over the remote oceans. The calculated marine aerosol lidar ratio is found to vary with the mean ocean surface wind speed (U10). An increase in U10 reduces the mean lidar ratio for marine regions from 32 ± 17 sr (for 0 < U10 < 4 m s−1) to 22 ± 7 sr (for U10 > 15 m s−1). Such changes in the lidar ratio are expected to have a corresponding effect on the marine AOD from CALIOP. The outcomes of this study are relevant for future improvements of the SODA and CALIOP operational product and could lead to more accurate retrievals of marine AOD.

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Volcanic SO<sub>2</sub> effective layer height retrieval for the Ozone Monitoring Instrument (OMI) using a machine-learning approach

Atmospheric Measurement Techniques ◽

10.5194/amt-14-3673-2021 ◽

2021 ◽

Vol 14 (5) ◽

pp. 3673-3691

Author(s):

Nikita M. Fedkin ◽

Can Li ◽

Nickolay A. Krotkov ◽

Pascal Hedelt ◽

Diego G. Loyola ◽

...

Keyword(s):

Volcanic Eruptions ◽

Volcanic Plume ◽

Aerosol Layer ◽

Ozone Monitoring Instrument ◽

Layer Height ◽

Monitoring Instrument ◽

Satellite Sensors ◽

Ozone Monitoring ◽

Computationally Intensive ◽

Learning Machine

Abstract. Information about the height and loading of sulfur dioxide (SO2) plumes from volcanic eruptions is crucial for aviation safety and for assessing the effect of sulfate aerosols on climate. While SO2 layer height has been successfully retrieved from backscattered Earthshine ultraviolet (UV) radiances measured by the Ozone Monitoring Instrument (OMI), previously demonstrated techniques are computationally intensive and not suitable for near-real-time applications. In this study, we introduce a new OMI algorithm for fast retrievals of effective volcanic SO2 layer height. We apply the Full-Physics Inverse Learning Machine (FP_ILM) algorithm to OMI radiances in the spectral range of 310–330 nm. This approach consists of a training phase that utilizes extensive radiative transfer calculations to generate a large dataset of synthetic radiance spectra for geophysical parameters representing the OMI measurement conditions. The principal components of the spectra from this dataset in addition to a few geophysical parameters are used to train a neural network to solve the inverse problem and predict the SO2 layer height. This is followed by applying the trained inverse model to real OMI measurements to retrieve the effective SO2 plume heights. The algorithm has been tested on several major eruptions during the OMI data record. The results for the 2008 Kasatochi, 2014 Kelud, 2015 Calbuco, and 2019 Raikoke eruption cases are presented here and compared with volcanic plume heights estimated with other satellite sensors. For the most part, OMI-retrieved effective SO2 heights agree well with the lidar measurements of aerosol layer height from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and thermal infrared retrievals of SO2 heights from the infrared atmospheric sounding interferometer (IASI). The errors in OMI-retrieved SO2 heights are estimated to be 1–1.5 km for plumes with relatively large SO2 signals (>40 DU). The algorithm is very fast and retrieves plume height in less than 10 min for an entire OMI orbit.

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