Improving automated global detection of volcanic SO2 plumes 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 the development of an automated volcanic plume detection method utilizing daily, global observations of sulphur 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 are analysed using a logistic regression analysis which permits the identification of volcanic events with an overall accuracy of over 80 %, and consistent plume identification when the volcanic plume SO2 loading exceeds ~ 400 tons. The accuracy and minimal user input requirements of the developed procedure provide a basis for the creation of an automated SO2 alert system providing volcanic alerts in regions where ground based volcano monitoring capabilities are limited. The technique could easily be adapted for use with satellite measurements of volcanic SO2 emissions from other platforms.

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

Atmospheric Measurement Techniques ◽

10.5194/amt-9-5487-2016 ◽

2016 ◽

Vol 9 (11) ◽

pp. 5487-5498 ◽

Cited By ~ 5

Author(s):

Verity J. B. Flower ◽

Thomas Oommen ◽

Simon A. Carn

Keyword(s):

Traffic Control ◽

Service Providers ◽

Volcanic Eruptions ◽

Volcanic Plume ◽

Human Populations ◽

Ozone Monitoring Instrument ◽

User Input ◽

Monitoring Instrument ◽

Ozone Monitoring

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.

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Volcanic SO2 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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New-generation NASA Aura Ozone Monitoring Instrument (OMI) volcanic SO2 dataset: Algorithm description, initial results, and continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

10.5194/amt-2016-221 ◽

2016 ◽

Cited By ~ 3

Author(s):

Can Li ◽

Nickolay A. Krotkov ◽

Simon Carn ◽

Yan Zhang ◽

Robert J. D. Spurr ◽

...

Keyword(s):

Lower Troposphere ◽

Climate Impacts ◽

Wavelength Shift ◽

Ozone Monitoring Instrument ◽

So2 Emissions ◽

Monitoring Instrument ◽

Spectral Fitting ◽

Ozone Monitoring ◽

Pca Algorithm ◽

New Generation

Abstract. Since the fall of 2004, the Ozone Monitoring Instrument (OMI) has been providing global monitoring of volcanic SO2 emissions, helping to understand their climate impacts and to mitigate aviation hazards. Here we introduce a new generation OMI volcanic SO2 dataset based on a principal component analysis (PCA) retrieval technique. To reduce retrieval noise and artifacts as seen in the current operational linear fit (LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic features extracted directly from OMI radiances in the spectral fitting, thereby helping to minimize interferences from various geophysical processes (e.g., O3 absorption) and measurement details (e.g., wavelength shift). To solve the problem of low bias for large SO2 total columns in the LF product, the OMSO2VOLCANO algorithm employs a table lookup approach to estimate SO2 Jacobians (i.e., the instrument sensitivity to a perturbation in the SO2 column amount) and iteratively adjusts the spectral fitting window to exclude shorter wavelengths where the SO2 absorption signals are saturated. To first order, the effects of clouds and aerosols are accounted for using a simple Lambertian equivalent reflectivity approach. As with the LF algorithm, OMSO2VOLCANO provides total column retrievals based on a set of pre-defined SO2 profiles from the lower troposphere to the lower stratosphere, including a new profile peaked at 13 km for plumes in the upper troposphere. Examples given in this study indicate that the new dataset shows significant improvement over the LF product, with at least 50 % reduction in retrieval noise over the remote Pacific. For large eruptions such as Kasatochi in 2008 (~ 1700 kt total SO2) and Sierra Negra in 2005 (> 1100 DU maximal SO2), OMSO2VOLCANO generally agrees well with other algorithms that also utilize the full spectral content of satellite measurements, while the LF algorithm tends to underestimate SO2. We also demonstrate that, despite the coarser spatial and spectral resolution of the Suomi National Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite (OMPS) instrument, application of the new PCA algorithm to OMPS data produces highly consistent retrievals between OMI and OMPS. The new PCA algorithm is therefore capable of continuing the volcanic SO2 data record well into the future using current and future hyperspectral UV satellite instruments.

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New-generation NASA Aura Ozone Monitoring Instrument (OMI) volcanic SO2 dataset: algorithm description, initial results, and continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

Atmospheric Measurement Techniques ◽

10.5194/amt-10-445-2017 ◽

2017 ◽

Vol 10 (2) ◽

pp. 445-458 ◽

Cited By ~ 36

Author(s):

Can Li ◽

Nickolay A. Krotkov ◽

Simon Carn ◽

Yan Zhang ◽

Robert J. D. Spurr ◽

...

Keyword(s):

Lower Troposphere ◽

Climate Impacts ◽

Wavelength Shift ◽

Ozone Monitoring Instrument ◽

So2 Emissions ◽

Monitoring Instrument ◽

Spectral Fitting ◽

Ozone Monitoring ◽

Pca Algorithm ◽

New Generation

Abstract. Since the fall of 2004, the Ozone Monitoring Instrument (OMI) has been providing global monitoring of volcanic SO2 emissions, helping to understand their climate impacts and to mitigate aviation hazards. Here we introduce a new-generation OMI volcanic SO2 dataset based on a principal component analysis (PCA) retrieval technique. To reduce retrieval noise and artifacts as seen in the current operational linear fit (LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic features extracted directly from OMI radiances in the spectral fitting, thereby helping to minimize interferences from various geophysical processes (e.g., O3 absorption) and measurement details (e.g., wavelength shift). To solve the problem of low bias for large SO2 total columns in the LF product, the OMSO2VOLCANO algorithm employs a table lookup approach to estimate SO2 Jacobians (i.e., the instrument sensitivity to a perturbation in the SO2 column amount) and iteratively adjusts the spectral fitting window to exclude shorter wavelengths where the SO2 absorption signals are saturated. To first order, the effects of clouds and aerosols are accounted for using a simple Lambertian equivalent reflectivity approach. As with the LF algorithm, OMSO2VOLCANO provides total column retrievals based on a set of predefined SO2 profiles from the lower troposphere to the lower stratosphere, including a new profile peaked at 13  km for plumes in the upper troposphere. Examples given in this study indicate that the new dataset shows significant improvement over the LF product, with at least 50 % reduction in retrieval noise over the remote Pacific. For large eruptions such as Kasatochi in 2008 (∼ 1700 kt total SO2) and Sierra Negra in 2005 (> 1100 DU maximum SO2), OMSO2VOLCANO generally agrees well with other algorithms that also utilize the full spectral content of satellite measurements, while the LF algorithm tends to underestimate SO2. We also demonstrate that, despite the coarser spatial and spectral resolution of the Suomi National Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite (OMPS) instrument, application of the new PCA algorithm to OMPS data produces highly consistent retrievals between OMI and OMPS. The new PCA algorithm is therefore capable of continuing the volcanic SO2 data record well into the future using current and future hyperspectral UV satellite instruments.

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A global catalogue of large SO2 sources and emissions derived from the Ozone Monitoring Instrument

10.5194/acp-2016-417 ◽

2016 ◽

Cited By ~ 2

Author(s):

Vitali E. Fioletov ◽

Chris A. McLinden ◽

Nickolay Krotkov ◽

Can Li ◽

Joanna Joiner ◽

...

Keyword(s):

Power Plants ◽

Oil And Gas ◽

Principal Component ◽

Oil And Gas Industry ◽

Point Sources ◽

Ozone Monitoring Instrument ◽

So2 Emissions ◽

Gas Industry ◽

Monitoring Instrument ◽

Ozone Monitoring

Abstract. Sulphur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new Principal Component Analysis (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr−1 to more than 4000 kt yr−1 of SO2 per year have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emissions detection algorithm, and their evolution during the 2005–2014 period was traced by estimating annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30 % of total SO2 emissions estimated from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly constant. Anthropogenic emissions from the USA declined by 80 % over the 2005–2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly constant. In total, OMI-based estimates account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr−1 and not detected by OMI.

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Ozone Monitoring Instrument Observations of Interannual Increases in SO2 Emissions from Indian Coal-Fired Power Plants during 2005–2012

Environmental Science & Technology ◽

10.1021/es4039648 ◽

2013 ◽

Vol 47 (24) ◽

pp. 13993-14000 ◽

Cited By ~ 74

Author(s):

Zifeng Lu ◽

David G. Streets ◽

Benjamin de Foy ◽

Nickolay A. Krotkov

Keyword(s):

Power Plants ◽

Ozone Monitoring Instrument ◽

So2 Emissions ◽

Monitoring Instrument ◽

Indian Coal ◽

Ozone Monitoring

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A global catalogue of large SO2 sources and emissions derived from the Ozone Monitoring Instrument

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-11497-2016 ◽

2016 ◽

Vol 16 (18) ◽

pp. 11497-11519 ◽

Cited By ~ 96

Author(s):

Vitali E. Fioletov ◽

Chris A. McLinden ◽

Nickolay Krotkov ◽

Can Li ◽

Joanna Joiner ◽

...

Keyword(s):

Power Plants ◽

Oil And Gas ◽

Principal Component ◽

Oil And Gas Industry ◽

Point Sources ◽

Ozone Monitoring Instrument ◽

So2 Emissions ◽

Gas Industry ◽

Monitoring Instrument ◽

Ozone Monitoring

Abstract. Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new principal component analysis (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr−1 to more than 4000 kt yr−1 of SO2 per year have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emission detection algorithm, and their evolution during the 2005–2014 period was traced by estimating annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30 % of total SO2 emissions estimated from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly constant. Anthropogenic emissions from the USA declined by 80 % over the 2005–2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly constant. In total, OMI-based estimates account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr−1 and not detected by OMI.

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