Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption
Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrie...
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ftcopernicus:oai:publications.copernicus.org:acp51505 2023-05-15T16:09:30+02:00 Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption Fu, Guangliang Prata, Fred Lin, Hai Xiang Heemink, Arnold Segers, Arjo Lu, Sha 2018-09-15 application/pdf https://doi.org/10.5194/acp-17-1187-2017 https://www.atmos-chem-phys.net/17/1187/2017/ eng eng doi:10.5194/acp-17-1187-2017 https://www.atmos-chem-phys.net/17/1187/2017/ eISSN: 1680-7324 Text 2018 ftcopernicus https://doi.org/10.5194/acp-17-1187-2017 2019-12-24T09:51:43Z Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations. In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space. Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h. Text Eyjafjallajökull Copernicus Publications: E-Journals Atmospheric Chemistry and Physics 17 2 1187 1205 |
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Copernicus Publications: E-Journals |
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English |
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Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations. In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space. Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h. |
format |
Text |
author |
Fu, Guangliang Prata, Fred Lin, Hai Xiang Heemink, Arnold Segers, Arjo Lu, Sha |
spellingShingle |
Fu, Guangliang Prata, Fred Lin, Hai Xiang Heemink, Arnold Segers, Arjo Lu, Sha Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption |
author_facet |
Fu, Guangliang Prata, Fred Lin, Hai Xiang Heemink, Arnold Segers, Arjo Lu, Sha |
author_sort |
Fu, Guangliang |
title |
Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption |
title_short |
Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption |
title_full |
Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption |
title_fullStr |
Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption |
title_full_unstemmed |
Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption |
title_sort |
data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 eyjafjallajökull volcanic eruption |
publishDate |
2018 |
url |
https://doi.org/10.5194/acp-17-1187-2017 https://www.atmos-chem-phys.net/17/1187/2017/ |
genre |
Eyjafjallajökull |
genre_facet |
Eyjafjallajökull |
op_source |
eISSN: 1680-7324 |
op_relation |
doi:10.5194/acp-17-1187-2017 https://www.atmos-chem-phys.net/17/1187/2017/ |
op_doi |
https://doi.org/10.5194/acp-17-1187-2017 |
container_title |
Atmospheric Chemistry and Physics |
container_volume |
17 |
container_issue |
2 |
container_start_page |
1187 |
op_container_end_page |
1205 |
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1766405373579755520 |