Evaluation and bias correction of probabilistic volcanic ash forecasts
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...
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ftcopernicus:oai:publications.copernicus.org:acp102935 2023-05-15T16:59:20+02:00 Evaluation and bias correction of probabilistic volcanic ash forecasts Crawford, Alice Chai, Tianfeng Wang, Binyu Ring, Allison Stunder, Barbara Loughner, Christopher P. Pavolonis, Michael Sieglaff, Justin 2022-11-02 application/pdf https://doi.org/10.5194/acp-22-13967-2022 https://acp.copernicus.org/articles/22/13967/2022/ eng eng doi:10.5194/acp-22-13967-2022 https://acp.copernicus.org/articles/22/13967/2022/ eISSN: 1680-7324 Text 2022 ftcopernicus https://doi.org/10.5194/acp-22-13967-2022 2022-11-07T17:22:42Z 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 when only small areas are still detectable in satellite imagery. 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 areas 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 to strategies in which areas with certain concentrations or column mass loadings of ash need to be avoided with a chosen amount of confidence. Text Kamchatka Copernicus Publications: E-Journals Atmospheric Chemistry and Physics 22 21 13967 13996 |
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Copernicus Publications: E-Journals |
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English |
description |
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 when only small areas are still detectable in satellite imagery. 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 areas 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 to strategies in which areas with certain concentrations or column mass loadings of ash need to be avoided with a chosen amount of confidence. |
format |
Text |
author |
Crawford, Alice Chai, Tianfeng Wang, Binyu Ring, Allison Stunder, Barbara Loughner, Christopher P. Pavolonis, Michael Sieglaff, Justin |
spellingShingle |
Crawford, Alice Chai, Tianfeng Wang, Binyu Ring, Allison Stunder, Barbara Loughner, Christopher P. Pavolonis, Michael Sieglaff, Justin Evaluation and bias correction of probabilistic volcanic ash forecasts |
author_facet |
Crawford, Alice Chai, Tianfeng Wang, Binyu Ring, Allison Stunder, Barbara Loughner, Christopher P. Pavolonis, Michael Sieglaff, Justin |
author_sort |
Crawford, Alice |
title |
Evaluation and bias correction of probabilistic volcanic ash forecasts |
title_short |
Evaluation and bias correction of probabilistic volcanic ash forecasts |
title_full |
Evaluation and bias correction of probabilistic volcanic ash forecasts |
title_fullStr |
Evaluation and bias correction of probabilistic volcanic ash forecasts |
title_full_unstemmed |
Evaluation and bias correction of probabilistic volcanic ash forecasts |
title_sort |
evaluation and bias correction of probabilistic volcanic ash forecasts |
publishDate |
2022 |
url |
https://doi.org/10.5194/acp-22-13967-2022 https://acp.copernicus.org/articles/22/13967/2022/ |
genre |
Kamchatka |
genre_facet |
Kamchatka |
op_source |
eISSN: 1680-7324 |
op_relation |
doi:10.5194/acp-22-13967-2022 https://acp.copernicus.org/articles/22/13967/2022/ |
op_doi |
https://doi.org/10.5194/acp-22-13967-2022 |
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Atmospheric Chemistry and Physics |
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22 |
container_issue |
21 |
container_start_page |
13967 |
op_container_end_page |
13996 |
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1766051580238364672 |