000903143 001__ 903143
000903143 005__ 20240712101058.0
000903143 0247_ $$2doi$$a10.5194/gmd-2021-30
000903143 0247_ $$2Handle$$a2128/29285
000903143 0247_ $$2altmetric$$aaltmetric:105607001
000903143 037__ $$aFZJ-2021-04867
000903143 082__ $$a910
000903143 1001_ $$0P:(DE-Juel1)162342$$aFranke, Philipp$$b0$$eCorresponding author
000903143 245__ $$aParticle filter based volcanic ash emission inversion applied to a hypothetical sub-Plinian Eyjafjallajökull eruption using the chemical component of the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-chem) version 1.0
000903143 260__ $$aKatlenburg-Lindau$$bCopernicus$$c2021
000903143 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1638450104_17873
000903143 3367_ $$2ORCID$$aWORKING_PAPER
000903143 3367_ $$028$$2EndNote$$aElectronic Article
000903143 3367_ $$2DRIVER$$apreprint
000903143 3367_ $$2BibTeX$$aARTICLE
000903143 3367_ $$2DataCite$$aOutput Types/Working Paper
000903143 520__ $$aAbstract. A particle filter based inversion system to derive time- and altitude-resolved volcanic ash emission fluxes along with its uncertainty is presented. For the underlying observation information only vertically integrated ash load data as provided by retrievals from nadir looking imagers mounted on geostationary satellites is assimilated. We aim to estimate the temporally varying emission profile with error margins, along with evidence of its dependencies on wind driven transport patterns within variable observation intervals. Thus, a variety of observation types, although not directly related to volcanic ash, can be utilized to constrain the probabilistic volcanic ash estimate. The system validation addresses the special challenge of ash cloud height analyses in case of observations restricted to bulk column mass loading information, mimicking the typical case of geostationary satellite data. The underlying method rests on a linear-combination of height-time emission finite elements of arbitrary resolution, each of which is assigned to a model run subject to ensemble-based space-time data assimilation. Employing a modular concept, this setup builds the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-chem) that comprises a particle smoother in combination with a discrete-grid ensemble extension of the Nelder-Mead minimization method. The ensemble version of the EURopean Air pollution Dispersion – Inverse Model (EURAD-IM) is integrated into ESIAS-chem but can be replaced by other models. The performance of ESIAS-chem is tested by identical twin experiments. The application of the inversion system to two notional sub-Plinian eruptions of the Eyjafjallajökull with strong ash emission changes with time and injection heights demonstrate the ability of ESIAS-chem to retrieve the volcanic ash emission fluxes from the assimilation of column mass loading data only. However, the analysed emission profiles strongly differ in their levels of accuracy depending of the strength of wind shear conditions. Under strong wind shear conditions at the volcano the temporal and vertical varying volcanic emissions are analyzed up to an error of only 10 % for the estimated emission fluxes. For weak wind shear conditions, however, analysis errors are larger and ESIAS-chem is less able to determine the ash emission flux variations. This situation, however, can be remedied by extending the assimilation window. In the performed test cases, the ensemble predicts the location of high volcanic ash column mass loading in the atmosphere with a very high probability of > 95 %. Additionally, the ensemble is able to provide a vertically resolved probability map of high volcanic ash concentrations to a high accuracy for both, high and weak wind shear conditions.
000903143 536__ $$0G:(DE-HGF)POF4-2111$$a2111 - Air Quality (POF4-211)$$cPOF4-211$$fPOF IV$$x0
000903143 588__ $$aDataset connected to CrossRef
000903143 7001_ $$0P:(DE-Juel1)162344$$aLange, Anne Caroline$$b1
000903143 7001_ $$0P:(DE-Juel1)129194$$aElbern, Hendrik$$b2
000903143 773__ $$0PERI:(DE-600)2456729-2$$a10.5194/gmd-2021-30$$tGeoscientific model development discussions$$x1991-9611$$y2021
000903143 8564_ $$uhttps://juser.fz-juelich.de/record/903143/files/gmd-2021-30.pdf$$yOpenAccess
000903143 909CO $$ooai:juser.fz-juelich.de:903143$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000903143 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162342$$aForschungszentrum Jülich$$b0$$kFZJ
000903143 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162344$$aForschungszentrum Jülich$$b1$$kFZJ
000903143 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129194$$aForschungszentrum Jülich$$b2$$kFZJ
000903143 9131_ $$0G:(DE-HGF)POF4-211$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2111$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vDie Atmosphäre im globalen Wandel$$x0
000903143 9141_ $$y2021
000903143 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000903143 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000903143 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2020-09-02
000903143 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2020-09-02
000903143 9201_ $$0I:(DE-Juel1)IEK-8-20101013$$kIEK-8$$lTroposphäre$$x0
000903143 9801_ $$aFullTexts
000903143 980__ $$apreprint
000903143 980__ $$aVDB
000903143 980__ $$aUNRESTRICTED
000903143 980__ $$aI:(DE-Juel1)IEK-8-20101013
000903143 981__ $$aI:(DE-Juel1)ICE-3-20101013