Articles | Volume 17, issue 4
https://doi.org/10.5194/acp-17-2865-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-17-2865-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
24 Feb 2017
Research article |
| 24 Feb 2017
Improving volcanic ash predictions with the HYSPLIT dispersion model by assimilating MODIS satellite retrievals
NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD 20740, USA
Alice Crawford
NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD 20740, USA
Barbara Stunder
NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Michael J. Pavolonis
NOAA Center for Satellite Applications and Research, Madison, WI, USA
Roland Draxler
NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
retired
Ariel Stein
NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
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- Air quality impacts of the 2018 Mt. Kilauea Volcano eruption in Hawaii: A regional chemical transport model study with satellite-constrained emissions Y. Tang et al. 10.1016/j.atmosenv.2020.117648
- Progress in protecting air travel from volcanic ash clouds L. Mastin et al. 10.1007/s00445-021-01511-x
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- Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions X. Jin et al. 10.7717/peerj.13203
- Observing ocean ecosystem responses to volcanic ash K. Bisson et al. 10.1016/j.rse.2023.113749
- Using Satellite Data to Determine Empirical Relationships between Volcanic Ash Source Parameters M. Zidikheri & C. Lucas 10.3390/atmos11040342
- Quantitative Verification and Calibration of Volcanic Ash Ensemble Forecasts Using Satellite Data M. Zidikheri et al. 10.1002/2017JD027740
- Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations H. Kim et al. 10.5194/acp-20-10259-2020
- Toward quantitative forecasts of volcanic ash dispersal: Using satellite retrievals for optimal estimation of source terms M. Zidikheri et al. 10.1002/2017JD026679
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- Spatio-temporal assessment of aerosol and cloud properties using MODIS satellite data and a HYSPLIT model: Implications for climate and agricultural systems M. Haseeb et al. 10.1016/j.aeaoa.2024.100242
- Tropospheric Atmospheric Heterogeneities of ALOS-2 Interferograms in the Greater Bay Area P. Liu & X. Chen 10.1109/JSTARS.2024.3414111
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- Improving Ensemble Volcanic Ash Forecasts by Direct Insertion of Satellite Data and Ensemble Filtering M. Zidikheri & C. Lucas 10.3390/atmos12091215
Latest update: 17 Nov 2024
Short summary
An inverse system based on the HYSPLIT dispersion model has been built to estimate volcanic ash source strengths, vertical distribution, and temporal variations. Using MODIS retrievals from the 2008 Kasatochi volcanic ash clouds, three options for matching model results to satellite mass loadings are tested. They all show decent skill. It is also found that simultaneously assimilating observations at different times produces better hindcasts than only assimilating the most recent observations.
An inverse system based on the HYSPLIT dispersion model has been built to estimate volcanic ash...
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