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Improving volcanic sulfur dioxide cloud dispersal forecasts by progressive assimilation of satellite observations
Author(s) -
Boichu Marie,
Clarisse Lieven,
Khvorostyanov Dmitry,
Clerbaux Cathy
Publication year - 2014
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2014gl059496
Subject(s) - volcano , environmental science , meteorology , cloud computing , biological dispersal , satellite , sulfur dioxide , data assimilation , vulcanian eruption , remote sensing , geology , earth science , atmospheric sciences , computer science , geography , seismology , aerospace engineering , engineering , ecology , population , demography , sociology , biology , operating system
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 (SO 2 ) 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 SO 2 ‐rich parts.