A Novel Approach to Estimating Time-Averaged Volcanic SO<sub>2</sub> Fluxes from Infrared Satellite Measurements
Long-term continuous time series of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> emission...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/5/966 |
| _version_ | 1797414428589686784 |
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| author | David M.R. Hyman Michael J. Pavolonis Justin Sieglaff |
| author_facet | David M.R. Hyman Michael J. Pavolonis Justin Sieglaff |
| author_sort | David M.R. Hyman |
| collection | DOAJ |
| description | Long-term continuous time series of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> plume from which to estimate the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> datasets from ultraviolet (UV) sensors lack altitude information, which must be assumed, to correctly calibrate the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> data. We then fit a solution to the general time-averaged dispersion of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> from a point source. We demonstrate these techniques using <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> data can fill an important data gap in the UV-based record: estimating <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used. |
| first_indexed | 2024-03-09T05:32:53Z |
| format | Article |
| id | doaj.art-0f6f044309d347dabc3ab94ee4992a3b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:32:53Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0f6f044309d347dabc3ab94ee4992a3b2023-12-03T12:30:55ZengMDPI AGRemote Sensing2072-42922021-03-0113596610.3390/rs13050966A Novel Approach to Estimating Time-Averaged Volcanic SO<sub>2</sub> Fluxes from Infrared Satellite MeasurementsDavid M.R. Hyman0Michael J. Pavolonis1Justin Sieglaff2Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison, 1225 W. Dayton St., Madison, WI 53706, USANational Oceanic and Atmospheric Administration (NOAA), 1225 W. Dayton St., Madison, WI 53706, USACooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison, 1225 W. Dayton St., Madison, WI 53706, USALong-term continuous time series of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> plume from which to estimate the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> datasets from ultraviolet (UV) sensors lack altitude information, which must be assumed, to correctly calibrate the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> data. We then fit a solution to the general time-averaged dispersion of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> from a point source. We demonstrate these techniques using <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> data can fill an important data gap in the UV-based record: estimating <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>SO</mi><mn>2</mn></msub></semantics></math></inline-formula> emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used.https://www.mdpi.com/2072-4292/13/5/966SO<sub>2</sub> emissionscomputer visiontime-averaged dispersion modelCrISJPSS |
| spellingShingle | David M.R. Hyman Michael J. Pavolonis Justin Sieglaff A Novel Approach to Estimating Time-Averaged Volcanic SO<sub>2</sub> Fluxes from Infrared Satellite Measurements Remote Sensing SO<sub>2</sub> emissions computer vision time-averaged dispersion model CrIS JPSS |
| title | A Novel Approach to Estimating Time-Averaged Volcanic SO<sub>2</sub> Fluxes from Infrared Satellite Measurements |
| title_full | A Novel Approach to Estimating Time-Averaged Volcanic SO<sub>2</sub> Fluxes from Infrared Satellite Measurements |
| title_fullStr | A Novel Approach to Estimating Time-Averaged Volcanic SO<sub>2</sub> Fluxes from Infrared Satellite Measurements |
| title_full_unstemmed | A Novel Approach to Estimating Time-Averaged Volcanic SO<sub>2</sub> Fluxes from Infrared Satellite Measurements |
| title_short | A Novel Approach to Estimating Time-Averaged Volcanic SO<sub>2</sub> Fluxes from Infrared Satellite Measurements |
| title_sort | novel approach to estimating time averaged volcanic so sub 2 sub fluxes from infrared satellite measurements |
| topic | SO<sub>2</sub> emissions computer vision time-averaged dispersion model CrIS JPSS |
| url | https://www.mdpi.com/2072-4292/13/5/966 |
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