Articles | Volume 22, issue 21
https://doi.org/10.5194/acp-22-13967-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-13967-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Nov 2022
Research article |
| 02 Nov 2022
| 02 Nov 2022
Evaluation and bias correction of probabilistic volcanic ash forecasts
NOAA Air Resources Laboratory, College Park, MD, USA
Tianfeng Chai
NOAA Air Resources Laboratory, College Park, MD, USA
Cooperative Institute for Satellite and Earth System Studies (CISESS), University of Maryland, College Park, MD, USA
Binyu Wang
NOAA National Centers for Environmental Prediction, Environmental Modeling Center, College Park, MD, USA
IM Systems Group, Rockville, MD, USA
Allison Ring
Department of Atmospheric and Ocean Science, University of Maryland, College Park, MD, USA
Barbara Stunder
NOAA Air Resources Laboratory, College Park, MD, USA
Christopher P. Loughner
NOAA Air Resources Laboratory, College Park, MD, USA
Michael Pavolonis
NOAA National Environmental Satellite, Data and Information Service (NESDIS), Madison, WI, USA
Justin Sieglaff
University of Wisconsin-Madison, Cooperative Institute for Meteorological Satellite Studies (UW/CIMSS), Madison, WI, USA
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Short summary
This study describes the development of a workflow which produces probabilistic and quantitative forecasts of volcanic ash in the atmosphere. The workflow includes methods of incorporating satellite observations of the ash cloud into a modeling framework as well as verification statistics that can be used to guide further model development and provide information for risk-based approaches to flight planning.
This study describes the development of a workflow which produces probabilistic and quantitative...
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