The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption
Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, ofte...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dürig, Tobias [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© International Association of Volcanology & Chemistry of the Earth's Interior 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Bulletin of volcanology - Berlin : Springer, 1924, 84(2022), 3 vom: 28. Feb. |
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| Übergeordnetes Werk: |
volume:84 ; year:2022 ; number:3 ; day:28 ; month:02 |
| Links: |
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| DOI / URN: |
10.1007/s00445-022-01541-z |
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SPR046430105 |
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| 245 | 1 | 4 | |a The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption |
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| 520 | |a Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, often only the plume height. A model’s output, however, depends also on the plume height data handling strategy (sampling rate, gap reconstruction methods and statistical treatment), especially in long-term eruptions with incomplete plume height records. To represent such an eruption, we used Eyjafjallajökull 2010 to test the sensitivity of six simple and two explicitly wind-affected plume models against 22 data handling strategies. Based on photogrammetric measurements, the wind deflection of the plume was determined and used to recalibrate radar-derived height data. The resulting data was then subjected to different data handling strategies, before being used as input for the plume models. The model results were compared to the erupted mass measured on the ground, allowing us to assess the prediction accuracy of each combination of data handling strategy and model. Combinations that provide highest prediction accuracies vary, depending on data coverage, eruption intensity and fragmentation mechanism. However, for this type of moderate-to-weak eruption (VEI 3 in terms of maximum intensity), the most important factor was found to be the prevailing wind speed. When wind speeds exceed 20 m/s, most combinations of strategies and models provide predictions that underestimate the erupted mass by more than 40%. Under such conditions, the optimal choice of data handling strategy and plume model is of particular importance. | ||
| 650 | 4 | |a Eyjafjallajökull eruption |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Plume height reconstruction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mass eruption rate |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Ash plumes |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Gudmundsson, Magnús T. |4 aut | |
| 700 | 1 | |a Ágústsdóttir, Thorbjörg |4 aut | |
| 700 | 1 | |a Högnadóttir, Thórdís |4 aut | |
| 700 | 1 | |a Schmidt, Louise S. |4 aut | |
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10.1007/s00445-022-01541-z doi (DE-627)SPR046430105 (SPR)s00445-022-01541-z-e DE-627 ger DE-627 rakwb eng Dürig, Tobias verfasserin (orcid)0000-0002-7453-4369 aut The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Association of Volcanology & Chemistry of the Earth's Interior 2022 Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, often only the plume height. A model’s output, however, depends also on the plume height data handling strategy (sampling rate, gap reconstruction methods and statistical treatment), especially in long-term eruptions with incomplete plume height records. To represent such an eruption, we used Eyjafjallajökull 2010 to test the sensitivity of six simple and two explicitly wind-affected plume models against 22 data handling strategies. Based on photogrammetric measurements, the wind deflection of the plume was determined and used to recalibrate radar-derived height data. The resulting data was then subjected to different data handling strategies, before being used as input for the plume models. The model results were compared to the erupted mass measured on the ground, allowing us to assess the prediction accuracy of each combination of data handling strategy and model. Combinations that provide highest prediction accuracies vary, depending on data coverage, eruption intensity and fragmentation mechanism. However, for this type of moderate-to-weak eruption (VEI 3 in terms of maximum intensity), the most important factor was found to be the prevailing wind speed. When wind speeds exceed 20 m/s, most combinations of strategies and models provide predictions that underestimate the erupted mass by more than 40%. Under such conditions, the optimal choice of data handling strategy and plume model is of particular importance. Eyjafjallajökull eruption (dpeaa)DE-He213 Plume height reconstruction (dpeaa)DE-He213 Mass eruption rate (dpeaa)DE-He213 Ash plumes (dpeaa)DE-He213 Gudmundsson, Magnús T. aut Ágústsdóttir, Thorbjörg aut Högnadóttir, Thórdís aut Schmidt, Louise S. aut Enthalten in Bulletin of volcanology Berlin : Springer, 1924 84(2022), 3 vom: 28. Feb. (DE-627)253390397 (DE-600)1458483-9 1432-0819 nnns volume:84 year:2022 number:3 day:28 month:02 https://dx.doi.org/10.1007/s00445-022-01541-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 84 2022 3 28 02 |
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10.1007/s00445-022-01541-z doi (DE-627)SPR046430105 (SPR)s00445-022-01541-z-e DE-627 ger DE-627 rakwb eng Dürig, Tobias verfasserin (orcid)0000-0002-7453-4369 aut The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Association of Volcanology & Chemistry of the Earth's Interior 2022 Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, often only the plume height. A model’s output, however, depends also on the plume height data handling strategy (sampling rate, gap reconstruction methods and statistical treatment), especially in long-term eruptions with incomplete plume height records. To represent such an eruption, we used Eyjafjallajökull 2010 to test the sensitivity of six simple and two explicitly wind-affected plume models against 22 data handling strategies. Based on photogrammetric measurements, the wind deflection of the plume was determined and used to recalibrate radar-derived height data. The resulting data was then subjected to different data handling strategies, before being used as input for the plume models. The model results were compared to the erupted mass measured on the ground, allowing us to assess the prediction accuracy of each combination of data handling strategy and model. Combinations that provide highest prediction accuracies vary, depending on data coverage, eruption intensity and fragmentation mechanism. However, for this type of moderate-to-weak eruption (VEI 3 in terms of maximum intensity), the most important factor was found to be the prevailing wind speed. When wind speeds exceed 20 m/s, most combinations of strategies and models provide predictions that underestimate the erupted mass by more than 40%. Under such conditions, the optimal choice of data handling strategy and plume model is of particular importance. Eyjafjallajökull eruption (dpeaa)DE-He213 Plume height reconstruction (dpeaa)DE-He213 Mass eruption rate (dpeaa)DE-He213 Ash plumes (dpeaa)DE-He213 Gudmundsson, Magnús T. aut Ágústsdóttir, Thorbjörg aut Högnadóttir, Thórdís aut Schmidt, Louise S. aut Enthalten in Bulletin of volcanology Berlin : Springer, 1924 84(2022), 3 vom: 28. Feb. (DE-627)253390397 (DE-600)1458483-9 1432-0819 nnns volume:84 year:2022 number:3 day:28 month:02 https://dx.doi.org/10.1007/s00445-022-01541-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 84 2022 3 28 02 |
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10.1007/s00445-022-01541-z doi (DE-627)SPR046430105 (SPR)s00445-022-01541-z-e DE-627 ger DE-627 rakwb eng Dürig, Tobias verfasserin (orcid)0000-0002-7453-4369 aut The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Association of Volcanology & Chemistry of the Earth's Interior 2022 Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, often only the plume height. A model’s output, however, depends also on the plume height data handling strategy (sampling rate, gap reconstruction methods and statistical treatment), especially in long-term eruptions with incomplete plume height records. To represent such an eruption, we used Eyjafjallajökull 2010 to test the sensitivity of six simple and two explicitly wind-affected plume models against 22 data handling strategies. Based on photogrammetric measurements, the wind deflection of the plume was determined and used to recalibrate radar-derived height data. The resulting data was then subjected to different data handling strategies, before being used as input for the plume models. The model results were compared to the erupted mass measured on the ground, allowing us to assess the prediction accuracy of each combination of data handling strategy and model. Combinations that provide highest prediction accuracies vary, depending on data coverage, eruption intensity and fragmentation mechanism. However, for this type of moderate-to-weak eruption (VEI 3 in terms of maximum intensity), the most important factor was found to be the prevailing wind speed. When wind speeds exceed 20 m/s, most combinations of strategies and models provide predictions that underestimate the erupted mass by more than 40%. Under such conditions, the optimal choice of data handling strategy and plume model is of particular importance. Eyjafjallajökull eruption (dpeaa)DE-He213 Plume height reconstruction (dpeaa)DE-He213 Mass eruption rate (dpeaa)DE-He213 Ash plumes (dpeaa)DE-He213 Gudmundsson, Magnús T. aut Ágústsdóttir, Thorbjörg aut Högnadóttir, Thórdís aut Schmidt, Louise S. aut Enthalten in Bulletin of volcanology Berlin : Springer, 1924 84(2022), 3 vom: 28. Feb. (DE-627)253390397 (DE-600)1458483-9 1432-0819 nnns volume:84 year:2022 number:3 day:28 month:02 https://dx.doi.org/10.1007/s00445-022-01541-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 84 2022 3 28 02 |
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10.1007/s00445-022-01541-z doi (DE-627)SPR046430105 (SPR)s00445-022-01541-z-e DE-627 ger DE-627 rakwb eng Dürig, Tobias verfasserin (orcid)0000-0002-7453-4369 aut The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Association of Volcanology & Chemistry of the Earth's Interior 2022 Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, often only the plume height. A model’s output, however, depends also on the plume height data handling strategy (sampling rate, gap reconstruction methods and statistical treatment), especially in long-term eruptions with incomplete plume height records. To represent such an eruption, we used Eyjafjallajökull 2010 to test the sensitivity of six simple and two explicitly wind-affected plume models against 22 data handling strategies. Based on photogrammetric measurements, the wind deflection of the plume was determined and used to recalibrate radar-derived height data. The resulting data was then subjected to different data handling strategies, before being used as input for the plume models. The model results were compared to the erupted mass measured on the ground, allowing us to assess the prediction accuracy of each combination of data handling strategy and model. Combinations that provide highest prediction accuracies vary, depending on data coverage, eruption intensity and fragmentation mechanism. However, for this type of moderate-to-weak eruption (VEI 3 in terms of maximum intensity), the most important factor was found to be the prevailing wind speed. When wind speeds exceed 20 m/s, most combinations of strategies and models provide predictions that underestimate the erupted mass by more than 40%. Under such conditions, the optimal choice of data handling strategy and plume model is of particular importance. Eyjafjallajökull eruption (dpeaa)DE-He213 Plume height reconstruction (dpeaa)DE-He213 Mass eruption rate (dpeaa)DE-He213 Ash plumes (dpeaa)DE-He213 Gudmundsson, Magnús T. aut Ágústsdóttir, Thorbjörg aut Högnadóttir, Thórdís aut Schmidt, Louise S. aut Enthalten in Bulletin of volcanology Berlin : Springer, 1924 84(2022), 3 vom: 28. Feb. (DE-627)253390397 (DE-600)1458483-9 1432-0819 nnns volume:84 year:2022 number:3 day:28 month:02 https://dx.doi.org/10.1007/s00445-022-01541-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 84 2022 3 28 02 |
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10.1007/s00445-022-01541-z doi (DE-627)SPR046430105 (SPR)s00445-022-01541-z-e DE-627 ger DE-627 rakwb eng Dürig, Tobias verfasserin (orcid)0000-0002-7453-4369 aut The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Association of Volcanology & Chemistry of the Earth's Interior 2022 Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, often only the plume height. A model’s output, however, depends also on the plume height data handling strategy (sampling rate, gap reconstruction methods and statistical treatment), especially in long-term eruptions with incomplete plume height records. To represent such an eruption, we used Eyjafjallajökull 2010 to test the sensitivity of six simple and two explicitly wind-affected plume models against 22 data handling strategies. Based on photogrammetric measurements, the wind deflection of the plume was determined and used to recalibrate radar-derived height data. The resulting data was then subjected to different data handling strategies, before being used as input for the plume models. The model results were compared to the erupted mass measured on the ground, allowing us to assess the prediction accuracy of each combination of data handling strategy and model. Combinations that provide highest prediction accuracies vary, depending on data coverage, eruption intensity and fragmentation mechanism. However, for this type of moderate-to-weak eruption (VEI 3 in terms of maximum intensity), the most important factor was found to be the prevailing wind speed. When wind speeds exceed 20 m/s, most combinations of strategies and models provide predictions that underestimate the erupted mass by more than 40%. Under such conditions, the optimal choice of data handling strategy and plume model is of particular importance. Eyjafjallajökull eruption (dpeaa)DE-He213 Plume height reconstruction (dpeaa)DE-He213 Mass eruption rate (dpeaa)DE-He213 Ash plumes (dpeaa)DE-He213 Gudmundsson, Magnús T. aut Ágústsdóttir, Thorbjörg aut Högnadóttir, Thórdís aut Schmidt, Louise S. aut Enthalten in Bulletin of volcanology Berlin : Springer, 1924 84(2022), 3 vom: 28. Feb. (DE-627)253390397 (DE-600)1458483-9 1432-0819 nnns volume:84 year:2022 number:3 day:28 month:02 https://dx.doi.org/10.1007/s00445-022-01541-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 84 2022 3 28 02 |
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Dürig, Tobias @@aut@@ Gudmundsson, Magnús T. @@aut@@ Ágústsdóttir, Thorbjörg @@aut@@ Högnadóttir, Thórdís @@aut@@ Schmidt, Louise S. @@aut@@ |
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Dürig, Tobias |
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The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption Eyjafjallajökull eruption (dpeaa)DE-He213 Plume height reconstruction (dpeaa)DE-He213 Mass eruption rate (dpeaa)DE-He213 Ash plumes (dpeaa)DE-He213 |
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effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 eyjafjallajökull eruption |
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The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption |
| abstract |
Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, often only the plume height. A model’s output, however, depends also on the plume height data handling strategy (sampling rate, gap reconstruction methods and statistical treatment), especially in long-term eruptions with incomplete plume height records. To represent such an eruption, we used Eyjafjallajökull 2010 to test the sensitivity of six simple and two explicitly wind-affected plume models against 22 data handling strategies. Based on photogrammetric measurements, the wind deflection of the plume was determined and used to recalibrate radar-derived height data. The resulting data was then subjected to different data handling strategies, before being used as input for the plume models. The model results were compared to the erupted mass measured on the ground, allowing us to assess the prediction accuracy of each combination of data handling strategy and model. Combinations that provide highest prediction accuracies vary, depending on data coverage, eruption intensity and fragmentation mechanism. However, for this type of moderate-to-weak eruption (VEI 3 in terms of maximum intensity), the most important factor was found to be the prevailing wind speed. When wind speeds exceed 20 m/s, most combinations of strategies and models provide predictions that underestimate the erupted mass by more than 40%. Under such conditions, the optimal choice of data handling strategy and plume model is of particular importance. © International Association of Volcanology & Chemistry of the Earth's Interior 2022 |
| abstractGer |
Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, often only the plume height. A model’s output, however, depends also on the plume height data handling strategy (sampling rate, gap reconstruction methods and statistical treatment), especially in long-term eruptions with incomplete plume height records. To represent such an eruption, we used Eyjafjallajökull 2010 to test the sensitivity of six simple and two explicitly wind-affected plume models against 22 data handling strategies. Based on photogrammetric measurements, the wind deflection of the plume was determined and used to recalibrate radar-derived height data. The resulting data was then subjected to different data handling strategies, before being used as input for the plume models. The model results were compared to the erupted mass measured on the ground, allowing us to assess the prediction accuracy of each combination of data handling strategy and model. Combinations that provide highest prediction accuracies vary, depending on data coverage, eruption intensity and fragmentation mechanism. However, for this type of moderate-to-weak eruption (VEI 3 in terms of maximum intensity), the most important factor was found to be the prevailing wind speed. When wind speeds exceed 20 m/s, most combinations of strategies and models provide predictions that underestimate the erupted mass by more than 40%. Under such conditions, the optimal choice of data handling strategy and plume model is of particular importance. © International Association of Volcanology & Chemistry of the Earth's Interior 2022 |
| abstract_unstemmed |
Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, often only the plume height. A model’s output, however, depends also on the plume height data handling strategy (sampling rate, gap reconstruction methods and statistical treatment), especially in long-term eruptions with incomplete plume height records. To represent such an eruption, we used Eyjafjallajökull 2010 to test the sensitivity of six simple and two explicitly wind-affected plume models against 22 data handling strategies. Based on photogrammetric measurements, the wind deflection of the plume was determined and used to recalibrate radar-derived height data. The resulting data was then subjected to different data handling strategies, before being used as input for the plume models. The model results were compared to the erupted mass measured on the ground, allowing us to assess the prediction accuracy of each combination of data handling strategy and model. Combinations that provide highest prediction accuracies vary, depending on data coverage, eruption intensity and fragmentation mechanism. However, for this type of moderate-to-weak eruption (VEI 3 in terms of maximum intensity), the most important factor was found to be the prevailing wind speed. When wind speeds exceed 20 m/s, most combinations of strategies and models provide predictions that underestimate the erupted mass by more than 40%. Under such conditions, the optimal choice of data handling strategy and plume model is of particular importance. © International Association of Volcanology & Chemistry of the Earth's Interior 2022 |
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The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption |
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https://dx.doi.org/10.1007/s00445-022-01541-z |
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Gudmundsson, Magnús T. Ágústsdóttir, Thorbjörg Högnadóttir, Thórdís Schmidt, Louise S. |
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Gudmundsson, Magnús T. Ágústsdóttir, Thorbjörg Högnadóttir, Thórdís Schmidt, Louise S. |
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10.1007/s00445-022-01541-z |
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2024-07-03T22:28:53.548Z |
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| score |
7.401963 |