Preprints
https://doi.org/10.5194/egusphere-2022-290
https://doi.org/10.5194/egusphere-2022-290
 
11 May 2022
11 May 2022

Evaluation and Bias Correction of Probabilistic Volcanic Ash Forecasts

Alice Crawford1, Tianfeng Chai1,2, Binyu Wang3,4, Allison Ring5, Barbara Stunder1, Christopher Loughner1, Michael Pavolonis6, and Justin Sieglaff7 Alice Crawford et al.
  • 1NOAA Air Resources Laboratory, College Park, MD, USA
  • 2Cooperative Institute for Satellite and Earth System Studies (CISESS), University of Maryland, College Park, MD, USA
  • 3NOAA National Centers for Environmental Prediction, Environmental Modeling Center, College Park, MD, USA
  • 4IM Systems Group, Rockville, MD, USA
  • 5Department of Atmospheric and Ocean Science, University of Maryland, College Park, MD, USA
  • 6NOAA National Environmental Satellite, Data and Information Service (NESDIS), Madison, WI, USA
  • 7Cooperative Institute for Meteorological Satellite Studies (CIMSS), Madison, WI, USA

Abstract. Satellite retrievals of column mass loading of volcanic ash are incorporated into the HYSPLIT transport and dispersion modeling system for source determination, bias correction, and forecast verification of probabilistic ash forecasts of a short eruption of Bezymianny in Kamchatka. The probabilistic forecasts are generated with a dispersion model ensemble created by driving HYSPLIT with 31 members of the NOAA global ensemble forecast system (GEFS). An inversion algorithm is used for source determination. A bias correction procedure called cumulative distribution function (CDF) matching is used to very effectively reduce bias. Evaluation is performed with rank histograms, reliability diagrams, fractions skill score, and precision recall curves. Particular attention is paid to forecasting the end of life of the ash cloud. We find indications that the simulated dispersion of the ash cloud does not represent the observed dispersion well, resulting in difficulty simulating the observed evolution of the ash cloud area. This can be ameliorated with the bias correction procedure. Individual model runs struggle to capture the exact placement and shape of the small pieces of ash left near the end of the clouds lifetime. The ensemble tends to be overconfident, but does capture the range of possibilities of ash cloud placement. Probabilistic forecasts such as ensemble relative frequency of exceedance and agreement in percentile levels are suited for strategies in which areas with certain concentrations or mass loadings of ash need to be avoided with a chosen amount of confidence.

Alice Crawford et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-290', Arnau Folch, 26 May 2022
    • AC1: 'Reply on RC1', Alice Crawford, 18 Jun 2022
  • RC2: 'Comment on egusphere-2022-290', Anonymous Referee #2, 26 Jul 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-290', Arnau Folch, 26 May 2022
    • AC1: 'Reply on RC1', Alice Crawford, 18 Jun 2022
  • RC2: 'Comment on egusphere-2022-290', Anonymous Referee #2, 26 Jul 2022

Alice Crawford et al.

Alice Crawford et al.

Viewed

Total article views: 410 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
313 86 11 410 10 6
  • HTML: 313
  • PDF: 86
  • XML: 11
  • Total: 410
  • BibTeX: 10
  • EndNote: 6
Views and downloads (calculated since 11 May 2022)
Cumulative views and downloads (calculated since 11 May 2022)

Viewed (geographical distribution)

Total article views: 362 (including HTML, PDF, and XML) Thereof 362 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Sep 2022
Download
Short summary
This study describes 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.