| DOI: | 10.1002/2014GL059496
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| 论文题名: | Improving volcanic sulfur dioxide cloud dispersal forecasts by progressive assimilation of satellite observations |
| 作者: | Boichu M.; Clarisse L.; Khvorostyanov D.; Clerbaux C.
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| 刊名: | Geophysical Research Letters
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| ISSN: | 0094-10181
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| EISSN: | 1944-9912
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| 出版年: | 2014
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| 卷: | 41, 期:7 | | 起始页码: | 2637
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| 结束页码: | 2643
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| 语种: | 英语
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| 英文关键词: | chemistry-transport modeling
; forecast
; inverse modeling
; satellite imagery
; volcanic cloud
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| Scopus关键词: | Clouds
; Forecasting
; Inverse problems
; Loading
; Models
; Satellite imagery
; Sulfur
; Volcanoes
; Aviation safety
; Chemistry transport model
; Emission parameters
; High temporal resolution
; Inverse modeling
; Satellite observations
; Volcanic clouds
; Volcanic emission
; Sulfur dioxide
; emission inventory
; inverse analysis
; satellite imagery
; sulfur dioxide
; volcanic cloud
; volcanic eruption
; weather forecasting
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| 英文摘要: | Forecasting the dispersal of volcanic clouds during an eruption is of primary importance, especially for ensuring aviation safety. As volcanic emissions are characterized by rapid variations of emission rate and height, the (generally) high level of uncertainty in the emission parameters represents a critical issue that limits the robustness of volcanic cloud dispersal forecasts. An inverse modeling scheme, combining satellite observations of the volcanic cloud with a regional chemistry-transport model, allows reconstructing this source term at high temporal resolution. We demonstrate here how a progressive assimilation of freshly acquired satellite observations, via such an inverse modeling procedure, allows for delivering robust sulfur dioxide (SO2) cloud dispersal forecasts during the eruption. This approach provides a computationally cheap estimate of the expected location and mass loading of volcanic clouds, including the identification of SO2-rich parts. Key Points Refined SO2 cloud dispersal forecasts by assimilation of satellite observations Refined estimation of source emissions using an inverse modeling approach Compared to standard methods, cloud SO2-rich parts are robustly forecasted © 2014. American Geophysical Union. All Rights Reserved. |
| URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899044092&doi=10.1002%2f2014GL059496&partnerID=40&md5=228d9eb28acb13b98bb342d3bcdfde11
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| Citation statistics: |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/7445
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| Appears in Collections: | 气候减缓与适应
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作者单位: | Laboratoire de Météorologie Dynamique, CNRS/INSU, UMR 8539, IPSL, Paris, France
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| Recommended Citation: | |
Boichu M.,Clarisse L.,Khvorostyanov D.,et al. Improving volcanic sulfur dioxide cloud dispersal forecasts by progressive assimilation of satellite observations[J]. Geophysical Research Letters,2014-01-01,41(7).
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