GSTDTAP  > 地球科学
DOI10.5194/acp-17-2865-2017
Improving volcanic ash predictions with the HYSPLIT dispersion model by assimilating MODIS satellite retrievals
Chai, Tianfeng1,2; Crawford, Alice1,2; Stunder, Barbara1; Pavolonis, Michael J.3; Draxler, Roland1; Stein, Ariel1
2017-02-24
发表期刊ATMOSPHERIC CHEMISTRY AND PHYSICS
ISSN1680-7316
EISSN1680-7324
出版年2017
卷号17期号:4
文章类型Article
语种英语
国家USA
英文摘要

Currently, the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) runs the HYSPLIT dispersion model with a unit mass release rate to predict the transport and dispersion of volcanic ash. The model predictions provide information for the Volcanic Ash Advisory Centers (VAAC) to issue advisories to meteorological watch offices, area control centers, flight information centers, and others. This research aims to provide quantitative forecasts of ash distributions generated by objectively and optimally estimating the volcanic ash source strengths, vertical distribution, and temporal variations using an observation-modeling inversion technique. In this top-down approach, a cost functional is defined to quantify the differences between the model predictions and the satellite measurements of column-integrated ash concentrations weighted by the model and observation uncertainties. Minimizing this cost functional by adjusting the sources provides the volcanic ash emission estimates. As an example, MODIS (Moderate Resolution Imaging Spectroradiometer) satellite retrievals of the 2008 Kasatochi volcanic ash clouds are used to test the HYSPLIT volcanic ash inverse system. Because the satellite retrievals include the ash cloud top height but not the bottom height, there are different model diagnostic choices for comparing the model results with the observed mass loadings. Three options are presented and tested. Although the emission estimates vary significantly with different options, the subsequent model predictions with the different release estimates all show decent skill when evaluated against the unassimilated satellite observations at later times. Among the three options, integrating over three model layers yields slightly better results than integrating from the surface up to the observed volcanic ash cloud top or using a single model layer. Inverse tests also show that including the ash-free region to constrain the model is not beneficial for the current case. In addition, extra constraints on the source terms can be given by explicitly enforcing "no-ash" for the atmosphere columns above or below the observed ash cloud top height. However, in this case such extra constraints are not helpful for the inverse modeling. It is also found that simultaneously assimilating observations at different times produces better hindcasts than only assimilating the most recent observations.


领域地球科学
收录类别SCI-E
WOS记录号WOS:000395116400001
WOS关键词OBJECTSA GENERALIZED FRAMEWORK ; AUTOMATED DETECTION ; SOURCE PARAMETERS ; CLOUD TRANSPORT ; PLUMES ; ERUPTION ; IMPACTS
WOS类目Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/16227
专题地球科学
作者单位1.NOAA, Air Resources Lab, Ctr Weather & Climate Predict, 5830 Univ Res Court, College Pk, MD 20740 USA;
2.Univ Maryland, Cooperat Inst Climate & Satellites, College Pk, MD 20740 USA;
3.NOAA, Ctr Satellite Applicat & Res, Madison, WI USA
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GB/T 7714
Chai, Tianfeng,Crawford, Alice,Stunder, Barbara,et al. Improving volcanic ash predictions with the HYSPLIT dispersion model by assimilating MODIS satellite retrievals[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2017,17(4).
APA Chai, Tianfeng,Crawford, Alice,Stunder, Barbara,Pavolonis, Michael J.,Draxler, Roland,&Stein, Ariel.(2017).Improving volcanic ash predictions with the HYSPLIT dispersion model by assimilating MODIS satellite retrievals.ATMOSPHERIC CHEMISTRY AND PHYSICS,17(4).
MLA Chai, Tianfeng,et al."Improving volcanic ash predictions with the HYSPLIT dispersion model by assimilating MODIS satellite retrievals".ATMOSPHERIC CHEMISTRY AND PHYSICS 17.4(2017).
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