Ozone Monitoring Instrument SO2 Research Articles

Remote sensing of eruptions and transport of Taal volcano in January 2020

The eruption and transport of the Taal volcano (14 N, 121 E) in January 2020 have been investigated using remote sensing measurements of several satellites, including the Moderate Resolution Imaging Spectroradiometer, Ozone Monitoring Instrument (OMI), Ozone Mapping and Profiler Suite, and Himawari-8. The aerosol optical thickness (AOT), angstrom exponent (AE), and column densities of sulfur dioxide (SO2) derived from satellites are analyzed in this study. The ground-based AERONET data of Manila city and OMI SO2 are studied to validate high AOT events. Our findings indicate that ash and gases followed opposite transport paths. This differential transport is consistent with various wind data, HYSPLIT back and forward trajectory calculations, and emission dispersion models. The potential influences of deposition on ocean biology and climate are also discussed.

Version 2 of the global catalogue of large anthropogenic and volcanic SO2 sources and emissions derived from satellite measurements

Abstract. Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI), Ozone Mapping and Profiler Suite (OMPS), and TROPOspheric Monitoring Instrument (TROPOMI) satellite spectrometers were used to update and extend the previously developed global catalogue of large SO2 emission sources. This version 2 of the global catalogue covers the period of 2005–2021 and includes a total of 759 continuously emitting point sources releasing from about 10 kt yr−1 to more than 4000 kt yr−1 of SO2, that have been identified and grouped by country and primary source origin: volcanoes (106 sources); power plants (477); smelters (74); and sources related to the oil and gas industry (102). There are several major improvements compared to the original catalogue: it combines emissions estimates from three satellite instruments instead of just OMI, uses a new version 2 of the OMI and OMPS SO2 dataset, and updated consistent site-specific air mass factors (AMFs) are used to calculate SO2 vertical column densities (VCDs). The newest TROPOMI SO2 data processed with the Covariance-Based Retrieval Algorithm (COBRA), used in the catalogue, can detect sources with emissions as low as 8 kt yr−1 (in 2018–2021) compared to the 30 kt yr−1 limit for OMI. In general, there is an overall agreement within ±12 % in total emissions estimated from the three satellite instruments for large regions. For individual emission sources, the spread is larger: the annual emissions estimated from OMI and TROPOMI agree within ±13 % in 50 % of cases and within ±28 % in 90 % of cases. The version 2 catalogue emissions were calculated as a weighted average of emission estimates from the three satellite instruments using an inverse-variance weighting method. OMI, OMPS, and TROPOMI data contribute 7 %, 5 %, and 88 % to the average, respectively, for small (<30 kt yr−1) sources and 33 %, 20 %, and 47 %, respectively, for large (>300 kt yr−1) sources. The catalogue data show an approximate 50 % decline in global SO2 emissions between 2005 and 2021, although emissions were relatively stable during the last 3 years. The version 2 of the global catalogue has been posted at the NASA global SO2 monitoring website (https://doi.org/10.5067/MEASURES/SO2/DATA406, Fioletov et al., 2022).

A new machine-learning-based analysis for improving satellite-retrieved atmospheric composition data: OMI SO2 as an example

Abstract. Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-to-noise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a top-down emission algorithm to estimate the annual SO2 emissions for ∼500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Version 2 Ozone Monitoring Instrument SO<sub>2</sub> product (OMSO2 V2): new anthropogenic SO<sub>2</sub> vertical column density dataset

Abstract. The Ozone Monitoring Instrument (OMI) has been providing global observations of SO2 pollution since 2004. Here we introduce the new anthropogenic SO2 vertical column density (VCD) dataset in the version 2 OMI SO2 product (OMSO2 V2). As with the previous version (OMSO2 V1.3), the new dataset is generated with an algorithm based on principal component analysis of OMI radiances but features several updates. The most important among those is the use of expanded lookup tables and model a priori profiles to estimate SO2 Jacobians for individual OMI pixels, in order to better characterize pixel-to-pixel variations in SO2 sensitivity including over snow and ice. Additionally, new data screening and spectral fitting schemes have been implemented to improve the quality of the spectral fit. As compared with the planetary boundary layer SO2 dataset in OMSO2 V1.3, the new dataset has substantially better data quality, especially over areas that are relatively clean or affected by the South Atlantic Anomaly. The updated retrievals over snow/ice yield more realistic seasonal changes in SO2 at high latitudes and offer enhanced sensitivity to sources during wintertime. An error analysis has been conducted to assess uncertainties in SO2 VCDs from both the spectral fit and Jacobian calculations. The uncertainties from spectral fitting are reflected in SO2 slant column densities (SCDs) and largely depend on the signal-to-noise ratio of the measured radiances, as implied by the generally smaller SCD uncertainties over clouds or for smaller solar zenith angles. The SCD uncertainties for individual pixels are estimated to be ∼ 0.15–0.3 DU (Dobson units) between ∼ 40∘ S and ∼ 40∘ N and to be ∼ 0.2–0.5 DU at higher latitudes. The uncertainties from the Jacobians are approximately ∼ 50 %–100 % over polluted areas and are primarily attributed to errors in SO2 a priori profiles and cloud pressures, as well as the lack of explicit treatment for aerosols. Finally, the daily mean and median SCDs over the presumably SO2-free equatorial east Pacific have increased by only ∼ 0.0035 DU and ∼ 0.003 DU respectively over the entire 15-year OMI record, while the standard deviation of SCDs has grown by only ∼ 0.02 DU or ∼ 10%. Such remarkable long-term stability makes the new dataset particularly suitable for detecting regional changes in SO2 pollution.

Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations

The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) are used to characterize particle pollution and their primary gaseous precursors, sulfur dioxide (SO2), and nitrogen dioxide (NO2) in Hubei province during 2005–2017. Unlike other regions in eastern China, particle and gaseous pollutants exhibit distinct spatial and temporal patterns in central China due to differences in emission sources and control measures. OMI SO2 of the whole Hubei region reached the highest value of ~0.2 Dobson unit (DU) in 2007 and then declined by more than 90% to near background levels. By contrast, OMI NO2 grew from ~3.2 to 5.9 × 1015 molecules cm−2 during 2005–2011 and deceased to ~3.9 × 1015 molecules cm−2 in 2017. Unlike the steadily declining SO2, variations of OMI NO2 flattened out in 2016 and increased ~0.5 × 1015 molecules cm−2 during 2017. As result, MODIS AOD at 550 nm increased from 0.55 to the peak value of 0.7 during 2005–2011 and then decreased continuously to 0.38 by 2017. MODIS AOD and OMI SO2 has a high correlation (R > 0.8), indicating that annual variations of SO2 can explain most changes of AOD. The air pollution in central China has notable seasonal variations, which is heaviest in winter and light in summer. While air quality in eastern Hubei is dominated by gaseous pollution such as O3 and NOx, particle pollutants are mainly concentrated in central Hubei. The high consistency with ground measurements demonstrates that satellite observation can well capture variations of air pollution in regional scales. The increasing ozone (O3) and NO2 since 2016 suggests that more control measures should be made to reduce O3-related emissions. To improve the air quality in regional scale, it is necessary to monitor the dynamic emission sources with satellite observations at a finer resolution.

Influence of regional emissions on SO2 concentrations over Bhubaneswar, a capital city in eastern India downwind of the Indian SO2 hotspots

In a scenario when haze and smog events are wreaking havoc in many parts of the world, accurate information on ambient sulfur dioxide (SO2) sources is a required input to models for prediction and mitigation of air pollution. Globally, India is one of the highest emitters of anthropogenic SO2. However, the Ozone Monitoring Instrument (OMI) data reveal that unlike many other anthropogenic air pollutants, the strongest anthropogenic SO2 sources are not located in the Indo-Gangetic plain (IGP) but over the coal belts of North Eastern peninsular India. In this study, we report SO2 measurements conducted at Bhubaneswar, a rapidly developing smart city in the Eastern Coast of India. The city is ideally located just downwind of the Indian SO2 hotspots during winter, when SO2 concentrations are the highest, reaching 14.3 ppbv for hourly averaged data. The measurements, reported here from the summer of 2010 to the summer of 2012, show that despite being a rapidly developing location, SO2 over Bhubaneswar is dominated by regional emissions rather than local sources like vehicular exhausts. The influence of regional emissions is strongest in winter as the city is directly downwind of 6 of the 10 largest thermal power plants and 7 out of the 13 largest integrated steel plants in India with a mean SO2 concentration of 3.2 ppbv. This is evinced by multiple pointers viz. Concentration-Weighted Trajectory (CWT) analysis for surface SO2 data, correlation analysis for OMI SO2 data, analysis of surface concentrations of SO2, organic carbon, black carbon and carbon monoxide; which reveal SO2 emissions from the industrial and mining regions of Chhattisgarh and north-west Odisha. This is the same region that shows up as hotspots of SO2 in OMI retrievals over India. However, despite being downwind of major SO2 sources during winter, SO2 concentrations in Bhubaneswar are lower than over some other cities (Kolkata, Kanpur) in the IGP, due to influence of strong local sources in the later cases. Unlike most other urban regions in India, the diurnal variation of SO2 over Bhubaneswar shows elevated daytime values due to predominant influence of regional emissions.

Linking improvements in sulfur dioxide emissions to decreasing sulfate wet deposition by combining satellite and surface observations with trajectory analysis

Sulfur dioxide (SO2), a criteria pollutant, and sulfate (SO42−) deposition are major environmental concerns in the eastern U.S. and both have been on the decline for two decades. In this study, we use satellite column SO2 data from the Ozone Monitoring Instrument (OMI), and SO42− wet deposition data from the NADP (National Atmospheric Deposition Program) to investigate the temporal and spatial relationship between trends in SO2 emissions and the downward sulfate wet deposition over the eastern U.S. from 2005 to 2015. To establish the relationship between SO2 emission sources and receptor sites, we conducted a Potential Source Contribution Function (PSCF) analysis using HYSPLIT back trajectories for five selected Air Quality System (AQS) sites - (Hackney, OH, Akron, OH, South Fayette, PA, Wilmington, DE, and Beltsville, MD) - in close proximity to NADP sites with large downward SO42− trends since 2005. Back trajectories were run for three summers (JJA) and three winters (DJF) and used to generate seasonal climatology PSCFs for each site. The OMI SO2 and interpolated NADP sulfate deposition trends were normalized and overlapped with the PSCF, to identify the areas that had the highest contribution to the observed drop. The results suggest that emission reductions along the Ohio River Valley have led to decreases in sulfate deposition in eastern OH and western PA (Hackney, Akron and South Fayette). Farther to the east, emission reductions in southeast PA resulted in improvements in sulfate deposition at Wilmington, DE, while for Beltsville, reductions in both the Ohio River Valley and nearby favorably impacted sulfate deposition. For Beltsville, sources closer than 300 km from the site contribute roughly 56% observed deposition trends in winter, and 82% in summer, reflecting seasonal changes in transport pattern as well as faster oxidation and washout of sulfur in summer. This suggests that emissions and wet deposition are linked through not only the location of sources relative to the observing sites, but also to photochemistry and the weather patterns characteristic to the region, as evidenced by a west to east shift in the contribution between winter and summer. The method developed here is applicable to other regions with significant trends such as China and India, and can be used to estimate the potential benefits of emission reduction in those areas.

Continuation of long-term global SO<sub>2</sub> pollution monitoring from OMI to OMPS

Abstract. Over the past 20 years, advances in satellite remote sensing of pollution-relevant species have made space-borne observations an increasingly important part of atmospheric chemistry research and air quality management. This progress has been facilitated by advanced UV–vis spectrometers, such as the Ozone Monitoring Instrument (OMI) on board the NASA Earth Observing System (EOS) Aura satellite, and continues with new instruments, such as the Ozone Mapping and Profiler Suite (OMPS) on board the NASA–NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite. In this study, we demonstrate that it is possible, using our state-of-the-art principal component analysis (PCA) retrieval technique, to continue the long-term global SO2 pollution monitoring started by OMI with the current and future OMPS instruments that will fly on the NOAA Joint Polar Satellite System (JPSS) 1, 2, 3, and 4 satellites in addition to SNPP, with a very good consistency of retrievals from these instruments. Since OMI SO2 data have been primarily used for (1) providing regional context on air pollution and long-range transport on a daily basis and (2) providing information on point emission sources on an annual basis after data averaging, we focused on these two aspects in our OMI–OMPS comparisons. Four years of retrievals (2012–2015) have been compared for three regions: eastern China, Mexico, and South Africa. In general, the comparisons show relatively high correlations (r = 0. 79–0.96) of daily regional averaged SO2 mass between the two instruments and near-unity regression slopes (0.76–0.97). The annual averaged SO2 loading differences between OMI and OMPS are small (< 0.03 Dobson unit (DU) over South Africa and up to 0.1 DU over eastern China). We also found a very good correlation (r = 0. 92–0.97) in the spatial distribution of annual averaged SO2 between OMI and OMPS over the three regions during 2012–2015. The emissions from ∼ 400 SO2 sources calculated with the two instruments also show a very good correlation (r = ∼ 0.9) in each year during 2012–2015. OMPS-detected SO2 point source emissions are slightly lower than those from OMI, but OMI–OMPS differences decrease with increasing strength of source. The OMI–OMPS SO2 mass differences on a pixel by pixel (daily) basis in each region can show substantial differences. The two instruments have a spatial correlation coefficient of 0.7 or better on < ∼ 50 % of the days. It is worth noting that consistent SO2 retrievals were achieved without any explicit adjustments to OMI or OMPS radiance data and that the retrieval agreement may be further improved by introducing a more comprehensive Jacobian lookup table than is currently used.

Detecting volcanic sulfur dioxide plumes in the Northern Hemisphere using the Brewer spectrophotometers, other networks, and satellite observations

Abstract. This study examines the adequacy of the existing Brewer network to supplement other networks from the ground and space to detect SO2 plumes of volcanic origin. It was found that large volcanic eruptions of the last decade in the Northern Hemisphere have a positive columnar SO2 signal seen by the Brewer instruments located under the plume. It is shown that a few days after the eruption the Brewer instrument is capable of detecting significant columnar SO2 increases, exceeding on average 2 DU relative to an unperturbed pre-volcanic 10-day baseline, with a mean close to 0 and σ = 0.46, as calculated from the 32 Brewer stations under study. Intercomparisons with independent measurements from the ground and space as well as theoretical calculations corroborate the capability of the Brewer network to detect volcanic plumes. For instance, the comparison with OMI (Ozone Monitoring Instrument) and GOME-2 (Global Ozone Monitoring Experiment-2) SO2 space-borne retrievals shows statistically significant agreement between the Brewer network data and the collocated satellite overpasses in the case of the Kasatochi eruption. Unfortunately, due to sparsity of satellite data, the significant positive departures seen in the Brewer and other ground networks following the Eyjafjallajökull, Bárðarbunga and Nabro eruptions could not be statistically confirmed by the data from satellite overpasses. A model exercise from the MACC (Monitoring Atmospheric Composition and Climate) project shows that the large increases in SO2 over Europe following the Bárðarbunga eruption in Iceland were not caused by local pollution sources or ship emissions but were clearly linked to the volcanic eruption. Sulfur dioxide positive departures in Europe following Bárðarbunga could be traced by other networks from the free troposphere down to the surface (AirBase (European air quality database) and EARLINET (European Aerosol Research Lidar Network)). We propose that by combining Brewer data with that from other networks and satellites, a useful tool aided by trajectory analyses and modelling could be created which can also be used to forecast high SO2 values both at ground level and in air flight corridors following future eruptions.

Improving global detection of volcanic eruptions using the Ozone Monitoring Instrument (OMI)

Abstract. Volcanic eruptions pose an ever-present threat to human populations around the globe, but many active volcanoes remain poorly monitored. In regions where ground-based monitoring is present the effects of volcanic eruptions can be moderated through observational alerts to both local populations and service providers, such as air traffic control. However, in regions where volcano monitoring is limited satellite-based remote sensing provides a global data source that can be utilised to provide near-real-time identification of volcanic activity. This paper details a volcanic plume detection method capable of identifying smaller eruptions than is currently feasible, which could potentially be incorporated into automated volcanic alert systems. This method utilises daily, global observations of sulfur dioxide (SO2) by the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite. Following identification and classification of known volcanic eruptions in 2005–2009, the OMI SO2 data, analysed using a logistic regression analysis, permitted the correct classification of volcanic events with an overall accuracy of over 80 %. Accurate volcanic plume identification was possible when lower-tropospheric SO2 loading exceeded ∼ 400 t. The accuracy and minimal user input requirements of the developed procedure provide a basis for incorporation into automated SO2 alert systems.

The impact of satellite sensor viewing geometry on time-series analysis of volcanic emissions

Abstract Time-series analysis techniques are being increasingly used to process satellite observations of volcanic gas emissions and heat flux, with the aim of identifying cyclic behaviour that could inform hazard assessment or elucidate volcanic processes. However, it can be difficult to distinguish cyclic variations due to geophysical processes from those that are artefacts of the observation technique. Here, we conduct a comprehensive investigation into the origin of cyclicity in volcanic observations by analysing daily, global satellite measurements of volcanic SO2 loading by the Ozone Monitoring Instrument (OMI) and thermal infrared anomalies detected by the Moderate Resolution Imaging Spectroradiometer (MODIS). We use a fast Fourier Transform (FFT) multi-taper method (MTM) to analyse multiple phases of activity at 32 target volcanoes, utilising measurements obtained from three NASA satellite instruments (Aura – OMI, Aqua – MODIS and Terra – MODIS), and identify a common cycle (period of ~ 2.3 days), which is not considered to be of volcanic origin. Based on the presence of this cycle in multiple satellite datasets, we attribute it to variations in viewing angle during the 16-day orbit repeat cycle of sun-synchronous satellites maintained in Low Earth Orbit (LEO). A 5-point averaging correction procedure is tested on satellite observations from Kilauea volcano, Hawaii, and is found to reduce the impact of higher frequency cycles and reveal the presence of longer-period geophysical signals. In addition to the identification of a signal common to different measurement techniques, an underlying cyclical pattern was found in the OMI SO2 observations (periods of ~ 7.9 and 3.2 days) generated by the OMI Row Anomaly (ORA). We conclude that identification of the presence and magnitude of non-geophysical cyclic behaviour, which can suppress natural cycles in time-series data, and implementation of appropriate corrections, is crucial for accurate interpretation of satellite observations. The use of data averaging to suppress non-geophysical cycles imposes limits on the length of natural cycles that can be confidently identified in moderate resolution satellite observations from polar-orbiting spacecraft.

Comparison and validation of band residual difference algorithm and principal component analysis algorithm for retrievals of atmospheric SO2 columns from satellite observations

Remote sensing technology provides an unprecedented tool for the continuous and real-time monitoring of atmospheric SO2 from volcanic eruption and anthropogenic emission. The Global Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), and Ozone Monitoring Instrument (OMI) have high SO2 monitoring capability. The OMI, which was launched on the EOS/Aura platform in July 2004, has the same hyperspectral measurements as the GOME and SCIAMACHY, but offers the improved spatial resolution at nadir (1324 km2) and daily global coverage for short-lifetime SO2. For OMI operational SO2 planetary boundary layer (PBL) retrieval, the previous band residual difference (BRD) algorithm has been replaced by principal component analysis (PCA) algorithm, which effectively reduces the systematic biases in SO2 column retrievals. However, there are few studies on the evaluations and validations of PCA SO2 retrievals over China, and the long-term comparisons with BRD SO2 retrievals also need to be conducted. In this study, the accuracies of PCA and BRD SO2 retrievals are validated by using ground-based multi axis differential optical absorption spectroscopy (MAX-DOAS) located in Beijing, and regional atmospheric modeling system, community multi-scale air quality (RAMS-CMAQ) modeling system model which can simulate the vertical distribution of atmospheric SO2. Moreover, BRD and PCA SO2 retrievals from oceanic area, eastern China and Reunion volcanic eruption are compared to find the long-term trend and spatiotemporal differences between SO2 columns. Finally, the uncertainty of SO2 retrieval, caused by measurement errors, band selection and input parameter errors in radiative transfer model, are analysed to understand the limitations of BRD and PCA algorithms. Results show that both PCA and BRD SO2 retrievals over Beijing are lower than ground-based MAX-DOAS measurements of SO2. PCA and BRD SO2 retrievals over eastern China are lower than the simulated SO2 columns from RAMS-CMAQ in winter 2008, but in July and August BRD SO2 columns are higher than RAMS-CMAQ simulations. The values of SO2 columns from BRD over China are more consistent with those from ground-based MAX-DOAS and RAMS-CMAQ model than from PCA. Although PCA algorithm effectively reduces the noise in SO2 column retrieval, SO2 columns from PCA over China are lower than those from BRD. For oceanic area where SO2 amount is nearly zero, the standard deviation of PCA results is lower than that of BRD, but the absolute value of averaged PCA SO2 column is larger than that of BRD. In the case of Reunion volcanic eruption with SO2 columns larger than 25 DU, the BRD SO2 columns are lower than PCA retrievals. Meanwhile, with the increase of SO2 column, the difference between BRD and PCA SO2 retrievals increases. Detailed uncertainty analysis shows the influences of measurement errors, band selection and inputs of radiative transfer model on the retrieval results. This study is important for developing the retrieval algorithm, and can also improve the application of OMI SO2 products.

A multi-sensor satellite assessment of SO 2 emissions from the 2012–13 eruption of Plosky Tolbachik volcano, Kamchatka

Abstract Prolonged basaltic effusive eruptions at high latitudes can have significant atmospheric and environmental impacts, but can be challenging to observe in winter conditions. Here, we use multi-sensor satellite data to assess sulfur dioxide (SO2) emissions from the 2012–2013 eruption of Plosky Tolbachik volcano (Kamchatka), which lasted ~ 9–10 months and erupted ~ 0.55 km3 DRE. Observations from the Ozone Monitoring Instrument (OMI), the Ozone Mapping and Profiler Suite (OMPS), the Atmospheric Infrared Sounder (AIRS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to evaluate volcanic activity, SO2 emissions and heat flux associated with the effusion of lava flows. Gaps in the primary OMI SO2 time-series dataset occurred due to instrument limitations and adverse meteorological conditions. Four methods were tested to assess how efficiently they could fill these data gaps and improve estimates of total SO2 emissions. When available, using data from other SO2 observing instruments was the most comprehensive way to address these data gaps. Satellite measurements yield a total SO2 loading of ~ 200 kt SO2 during the 10-month Plosky Tolbachik eruption, although actual SO2 emissions may have been greater. Based on the satellite SO2 measurements, the Fast Fourier Transform (FFT) multi-taper method (MTM) was used to analyze cyclical behavior in the complete data series and a 55-day cycle potentially attributable to the eruptive behavior of Plosky Tolbachik during the 2012 – 2013 eruption was identified.

Modeling of 2008 Kasatochi volcanic sulfate direct radiative forcing: assimilation of OMI SO<sub>2</sub> plume height data and comparison with MODIS and CALIOP observations

Abstract. Volcanic SO2 column amount and injection height retrieved from the Ozone Monitoring Instrument (OMI) with the Extended Iterative Spectral Fitting (EISF) technique are used to initialize a global chemistry transport model (GEOS-Chem) to simulate the atmospheric transport and lifecycle of volcanic SO2 and sulfate aerosol from the 2008 Kasatochi eruption, and to subsequently estimate the direct shortwave, top-of-the-atmosphere radiative forcing of the volcanic sulfate aerosol. Analysis shows that the integrated use of OMI SO2 plume height in GEOS-Chem yields: (a) good agreement of the temporal evolution of 3-D volcanic sulfate distributions between model simulations and satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP), and (b) an e-folding time for volcanic SO2 that is consistent with OMI measurements, reflecting SO2 oxidation in the upper troposphere and stratosphere is reliably represented in the model. However, a consistent (~25%) low bias is found in the GEOS-Chem simulated SO2 burden, and is likely due to a high (~20%) bias of cloud liquid water amount (as compared to the MODIS cloud product) and the resultant stronger SO2 oxidation in the GEOS meteorological data during the first week after eruption when part of SO2 underwent aqueous-phase oxidation in clouds. Radiative transfer calculations show that the forcing by Kasatochi volcanic sulfate aerosol becomes negligible 6 months after the eruption, but its global average over the first month is −1.3 Wm−2, with the majority of the forcing-influenced region located north of 20° N, and with daily peak values up to −2 Wm−2 on days 16–17. Sensitivity experiments show that every 2 km decrease of SO2 injection height in the GEOS-Chem simulations will result in a ~25 % decrease in volcanic sulfate forcing; similar sensitivity but opposite sign also holds for a 0.03 μm increase of geometric radius of the volcanic aerosol particles. Both sensitivities highlight the need to characterize the SO2 plume height and aerosol particle size from space. While more research efforts are warranted, this study is among the first to assimilate both satellite-based SO2 plume height and amount into a chemical transport model for an improved simulation of volcanic SO2 and sulfate transport.

Corrections for OMI SO<sub>2</sub> BRD retrievals influenced by row anomalies

Abstract. Since June 2007, the Ozone Monitoring Instrument (OMI) Earth radiance data at specific viewing angles have been affected by the row anomaly, which causes large biases in sulfur dioxide (SO2) columns retrieved using the band residual difference (BRD) algorithm. To improve global measurements of atmospheric SO2 from OMI, we developed two correction approaches for the row anomaly effects in the northern latitudes and along the full orbit. Firstly the residual correction approach with median residual from a sliding 10° latitude range, and with that near the Equator was used to remove the anomalous high SO2 columns in the northern latitudes. Secondly, in the case of the row anomaly along the full orbit, the SO2 biases caused by the anomalous ozone (O3) column and underestimated Lambertian effective reflectivity (LER) were reduced, respectively, by using unaffected adjacent O3 column and residual correction with median residual from a sliding 10° latitude range. Comparisons with the OMI SO2 columns processed with median residual from a sliding 30° latitude range have illustrated the drastic improvements of our correction approaches under row anomaly conditions. The consistencies among the SO2 columns inside and outside the row anomaly areas have also demonstrated the effectiveness of our correction approaches under row anomaly conditions. The analyses of the underestimation and the errors caused by the O3 column and LER were conducted to understand the limitations of our correction approaches. The proposed approaches for the row anomaly effects can extend the valid range of OMI SO2 Planetary Boundary Layer (PBL) data produced using the BRD algorithm.

Comparing different light-emitting diodes as light sources for long path differential optical absorption spectroscopy NO2 and SO2 measurements

In this paper, we present a comparison of different light-emitting diodes (LEDs) as the light source for long path differential optical absorption spectroscopy (LP-DOAS) atmospheric trace gas measurements. In our study, we use a fiberoptic design, where high power LEDs used as the light source are coupled into the telescope using a Y shape fiber bundle. Two blue and one ultraviolet (UV) LEDs with different emission wavelength ranges are tested for NO2 and SO2 measurements. The detailed description of the instrumental setup, the NO2 and SO2 retrieval procedure, the error analysis, and the preliminary results from the measurements carried out in Science Island, Hefei, Anhui, China are presented. Our first measurement results show that atmospheric NO2 and SO2 have strong temporal variations in that area and that the measurement accuracy is strongly dependent on the visibility conditions. The measured NO2 and SO2 data are compared to the Ozone Monitoring Instrument (OMI) satellite observations. The results show that the OMI NO2 product underestimates the ground level NO2 by 45%, while the OMI SO2 data are highly influenced by clouds and aerosols, which can lead to large biases in the ground level concentrations. During the experiment, the mixing ratios of the atmospheric NO2 and SO2 vary from 8 ppbv to 36 ppbv and from 3 ppbv to 18 ppbv, respectively.

Using horizontal transport characteristics to infer an emission height time series of volcanic SO2

Characterizing the emission height of sulfur dioxide (SO2) from volcanic eruptions yields information about the strength of volcanic activity, and is crucial for the assessment of possible climate impacts and validation of satellite retrievals of SO2. Sensors such as the Ozone Monitoring Instrument (OMI) on the polar‐orbiting Aura satellite provide accurate maps of the spatial distribution of volcanic SO2, but provide limited information on its vertical distribution. The goal of this work is to explore the possible use of a trajectory model in reconstructing both the temporal activity and injection altitude of volcanic SO2 from OMI column measurements observed far from the volcano. Using observations from the November 2006 eruption of Nyamuragira, back trajectories are run and statistical analyses are computed based on the distance of closest approach to the volcano. These statistical analyses provide information about the emission height time series of SO2 injection from that eruption. It is found that the eruption begins first injecting SO2 into the upper troposphere, between 13 km and 17 km, on November 28th 2006. This is then followed by a slow decay in injection altitude, down to 6 km, over subsequent days. The emission height profile is used to generate an optimal reconstruction based on forward trajectories and compared to OMI SO2 observations. The inferred altitude of the Nyamuragira SO2cloud is also compared to the altitude of sulfate aerosols detected in aerosol backscatter vertical profiles from the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument aboard the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO).

Evaluation of Redoubt Volcano's sulfur dioxide emissions by the Ozone Monitoring Instrument

The 2009 eruption of Redoubt Volcano, Alaska, provided a rare opportunity to compare satellite measurements of sulfur dioxide (SO2) by the Ozone Monitoring Instrument (OMI) with airborne SO2 measurements by the Alaska Volcano Observatory (AVO). Herein we: (1) compare OMI and airborne SO2 column density values for Redoubt's tropospheric plume, (2) calculate daily SO2 masses from Mount Redoubt for the first three months of the eruption, (3) develop simple methods to convert daily measured SO2 masses into emission rates to allow satellite data to be directly integrated with the airborne SO2 emissions dataset, (4) calculate cumulative SO2 emissions from the eruption, and (5) evaluate OMI as a monitoring tool for high-latitude degassing volcanoes. A linear correlation (R2~0.75) is observed between OMI and airborne SO2 column densities. OMI daily SO2 masses for the sample period ranged from ~60.1kt on 24 March to below detection limit, with an average daily SO2 mass of ~6.7kt. The highest SO2 emissions were observed during the initial part of the explosive phase and the emissions exhibited an overall decreasing trend with time. OMI SO2 emission rates were derived using three methods and compared to airborne measurements. This comparison yields a linear correlation (R2~0.82) with OMI-derived emission rates consistently lower than airborne measurements. The comparison results suggest that OMI's detection limit for high latitude, springtime conditions varies from ~2000 to 4000t/d. Cumulative SO2 masses calculated from daily OMI data for the sample period are estimated to range from 542 to 615kt, with approximately half of this SO2 produced during the explosive phase of the eruption. These cumulative masses are similar in magnitude to those estimated for the 1989–90 Redoubt eruption. Strong correlations between daily OMI SO2 mass and both tephra mass and acoustic energy during the explosive phase of the eruption suggest that OMI data may be used to infer relative eruption size and explosivity. Further, when used in conjunction with complementary datasets, OMI daily SO2 masses may be used to help distinguish explosive from effusive activity and identify changes in lava extrusion rates. The results of this study suggest that OMI is a useful volcano monitoring tool to complement airborne measurements, capture explosive SO2 emissions, and provide high temporal resolution SO2 emissions data that can be used with interdisciplinary datasets to illuminate volcanic processes.

First synoptic analysis of volcanic degassing in Papua New Guinea

We report the first satellite‐based survey of volcanic sulphur dioxide (SO2) degassing in Papua New Guinea, using Ozone Monitoring Instrument (OMI) data. OMI is sensitive to low‐level passive degassing. These observations are useful for volcano monitoring, hazard assessment (particularly aviation hazard) and assessment of arc geochemical budgets and are of immense value in remote regions with little ground‐based instrumentation, such as Papua New Guinea. We identify Bagana, Manam, Rabaul, Ulawun and Langila as the active sources of volcanic SO2 in Papua New Guinea, with Bagana being the largest source. We present an OMI SO2 time series for 2005–2008 and a total detected regional output of ∼1.8 × 109 kg SO2. About 40% of emissions were released by major eruption events at Manam (January 2005), Bagana (June 2006) and Rabaul (October 2006). Over the past century however, we estimate that major explosive eruptions contribute <5% of the arc‐scale SO2emission budget. Ground‐based DOAS measurements of SO2 degassing at five of Papua New Guinea's volcanoes are compared with our OMI observations. The total OMI SO2 output is only ∼20% of the total extrapolated from DOAS, a discrepancy which we demonstrate is consistent with other volcanic arcs. Therefore, the true total regional SO2 output may be considerably higher than that detected by OMI. Uncertainties in the OMI SO2data include the effects of in‐plume chemical processing and dilution of SO2 prior to the satellite overpass, OMI's reduced sensitivity to low levels of SO2 in the planetary boundary layer and interference by meteorological clouds.

SO2 over central China: Measurements, numerical simulations and the tropospheric sulfur budget

SO2in central China was measured in situ from an aircraft and remotely using the Ozone Monitoring Instrument (OMI) from the Aura satellite; results were used to develop a numerical tool for evaluating the tropospheric sulfur budget ‐ sources, sinks, transformation and transport. In April 2008, measured ambient SO2 concentrations decreased from ∼7 ppbv near the surface to ∼1 ppbv at 1800 m altitude (an effective scale height of ∼800 m), but distinct SO2 plumes were observed between 1800 and 4500 m, the aircraft's ceiling. These free tropospheric plumes play a major role in the export of SO2 and in the accuracy of OMI retrievals. The mean SO2 column contents from aircraft measurements (0.73 DU, Dobson Units) and operational OMI SO2 products (0.63 ± 0.26 DU) were close. The OMI retrievals were well correlated with in situ measurements (r = 0.84), but showed low bias (slope = 0.54). A new OMI retrieval algorithm was tested and showed improved agreement and bias (r = 0.87, slope = 0.86). The Community Multiscale Air Quality (CMAQ) model was used to simulate sulfur chemistry, exhibiting reasonable agreement (r = 0.62, slope = 1.33) with in situ SO2 columns. The mean CMAQ SO2 loading over central and eastern China was 54 kT, ∼30% more than the estimate from OMI SO2 products, 42 kT. These numerical simulations, constrained by observations, indicate that ∼50% (35 to 61%) of the anthropogenic sulfur emissions were transported downwind, and the overall lifetime of tropospheric SO2 was 38 ± 7 h.