Adjusting particle-size distributions to account for aggregation in
tephra-deposit model forecasts

Mastin, Larry G.; Van Eaton, Alexa R.; Durant, Adam J.

doi:10.5194/acp-16-9399-2016

Articles | Volume 16, issue 14

https://doi.org/10.5194/acp-16-9399-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/acp-16-9399-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 14

Research article

|

28 Jul 2016

Research article |

| 28 Jul 2016

Adjusting particle-size distributions to account for aggregation in tephra-deposit model forecasts

Larry G. Mastin, Alexa R. Van Eaton, and Adam J. Durant

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Views and downloads (calculated since 31 Mar 2016)

Month	HTML	PDF	XML	Total
Apr 2016	88	42	3	133
May 2016	20	7	1	28
Jun 2016	6	3	0	9
Jul 2016	46	8	0	54
Aug 2016	54	21	1	76
Sep 2016	15	11	1	27
Oct 2016	12	5	3	20
Nov 2016	9	5	2	16
Dec 2016	8	3	0	11
Jan 2017	16	7	1	24
Feb 2017	9	3	4	16
Mar 2017	22	9	2	33
Apr 2017	30	26	2	58
May 2017	36	19	1	56
Jun 2017	34	13	2	49
Jul 2017	18	12	1	31
Aug 2017	21	15	1	37
Sep 2017	15	27	2	44
Oct 2017	15	22	0	37
Nov 2017	15	30	0	45
Dec 2017	14	24	0	38
Jan 2018	11	17	0	28
Feb 2018	12	13	0	25
Mar 2018	9	15	0	24
Apr 2018	20	10	0	30
May 2018	34	11	4	49
Jun 2018	9	13	0	22
Jul 2018	4	9	1	14
Aug 2018	16	7	3	26
Sep 2018	8	9	0	17
Oct 2018	13	7	3	23
Nov 2018	12	4	0	16
Dec 2018	10	4	0	14
Jan 2019	3	7	1	11
Feb 2019	6	5	0	11
Mar 2019	7	7	1	15
Apr 2019	5	8	0	13
May 2019	16	9	1	26
Jun 2019	11	14	2	27
Jul 2019	11	8	0	19
Aug 2019	4	6	0	10
Sep 2019	9	5	0	14
Oct 2019	5	13	1	19
Nov 2019	5	4	0	9
Dec 2019	3	6	0	9
Jan 2020	12	8	0	20
Feb 2020	10	10	0	20
Mar 2020	5	6	0	11
Apr 2020	3	8	0	11
May 2020	6	3	1	10
Jun 2020	19	9	0	28
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Aug 2020	10	13	2	25
Sep 2020	18	5	2	25
Oct 2020	4	4	1	9
Nov 2020	2	6	1	9
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Jan 2021	15	7	1	23
Feb 2021	13	7	0	20
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Apr 2021	11	16	0	27
May 2021	9	8	1	18
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Oct 2021	4	36	0	40
Nov 2021	12	24	0	36
Dec 2021	20	13	1	34
Jan 2022	16	9	3	28
Feb 2022	8	7	2	17
Mar 2022	6	6	0	12
Apr 2022	21	10	1	32
May 2022	9	14	0	23
Jun 2022	3	4	0	7
Jul 2022	8	7	1	16
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Oct 2022	6	4	1	11
Nov 2022	17	5	0	22
Dec 2022	10	8	0	18
Jan 2023	9	8	1	18
Feb 2023	11	11	1	23
Mar 2023	17	6	0	23
Apr 2023	9	2	0	11
May 2023	31	8	0	39
Jun 2023	42	4	0	46
Jul 2023	36	5	1	42
Aug 2023	38	7	2	47
Sep 2023	39	3	1	43
Oct 2023	88	8	1	97
Nov 2023	155	6	0	161
Dec 2023	43	8	2	53
Jan 2024	36	11	0	47
Feb 2024	41	10	1	52
Mar 2024	43	9	0	52
Apr 2024	41	7	2	50
May 2024	11	10	5	26
Jun 2024	11	10	1	22
Jul 2024	20	13	2	35
Aug 2024	23	14	4	41
Sep 2024	12	5	0	17
Oct 2024	9	6	1	16
Nov 2024	11	10	2	23
Dec 2024	17	5	0	22
Jan 2025	18	12	3	33
Feb 2025	20	13	5	38
Mar 2025	24	13	1	38
Apr 2025	17	35	0	52
May 2025	18	10	0	28
Jun 2025	34	22	1	57
Jul 2025	28	20	1	49
Aug 2025	22	17	0	39
Sep 2025	18	15	2	35
Oct 2025	16	28	0	44
Nov 2025	27	34	4	65

Cumulative views and downloads (calculated since 31 Mar 2016)

Month	HTML	PDF	XML	Total
Apr 2016	88	42	3	133
May 2016	20	7	1	28
Jun 2016	6	3	0	9
Jul 2016	46	8	0	54
Aug 2016	54	21	1	76
Sep 2016	15	11	1	27
Oct 2016	12	5	3	20
Nov 2016	9	5	2	16
Dec 2016	8	3	0	11
Jan 2017	16	7	1	24
Feb 2017	9	3	4	16
Mar 2017	22	9	2	33
Apr 2017	30	26	2	58
May 2017	36	19	1	56
Jun 2017	34	13	2	49
Jul 2017	18	12	1	31
Aug 2017	21	15	1	37
Sep 2017	15	27	2	44
Oct 2017	15	22	0	37
Nov 2017	15	30	0	45
Dec 2017	14	24	0	38
Jan 2018	11	17	0	28
Feb 2018	12	13	0	25
Mar 2018	9	15	0	24
Apr 2018	20	10	0	30
May 2018	34	11	4	49
Jun 2018	9	13	0	22
Jul 2018	4	9	1	14
Aug 2018	16	7	3	26
Sep 2018	8	9	0	17
Oct 2018	13	7	3	23
Nov 2018	12	4	0	16
Dec 2018	10	4	0	14
Jan 2019	3	7	1	11
Feb 2019	6	5	0	11
Mar 2019	7	7	1	15
Apr 2019	5	8	0	13
May 2019	16	9	1	26
Jun 2019	11	14	2	27
Jul 2019	11	8	0	19
Aug 2019	4	6	0	10
Sep 2019	9	5	0	14
Oct 2019	5	13	1	19
Nov 2019	5	4	0	9
Dec 2019	3	6	0	9
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Mar 2020	5	6	0	11
Apr 2020	3	8	0	11
May 2020	6	3	1	10
Jun 2020	19	9	0	28
Jul 2020	15	12	2	29
Aug 2020	10	13	2	25
Sep 2020	18	5	2	25
Oct 2020	4	4	1	9
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Jan 2021	15	7	1	23
Feb 2021	13	7	0	20
Mar 2021	13	4	0	17
Apr 2021	11	16	0	27
May 2021	9	8	1	18
Jun 2021	11	9	1	21
Jul 2021	9	11	0	20
Aug 2021	11	14	0	25
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Oct 2021	4	36	0	40
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Dec 2021	20	13	1	34
Jan 2022	16	9	3	28
Feb 2022	8	7	2	17
Mar 2022	6	6	0	12
Apr 2022	21	10	1	32
May 2022	9	14	0	23
Jun 2022	3	4	0	7
Jul 2022	8	7	1	16
Aug 2022	10	3	1	14
Sep 2022	8	14	0	22
Oct 2022	6	4	1	11
Nov 2022	17	5	0	22
Dec 2022	10	8	0	18
Jan 2023	9	8	1	18
Feb 2023	11	11	1	23
Mar 2023	17	6	0	23
Apr 2023	9	2	0	11
May 2023	31	8	0	39
Jun 2023	42	4	0	46
Jul 2023	36	5	1	42
Aug 2023	38	7	2	47
Sep 2023	39	3	1	43
Oct 2023	88	8	1	97
Nov 2023	155	6	0	161
Dec 2023	43	8	2	53
Jan 2024	36	11	0	47
Feb 2024	41	10	1	52
Mar 2024	43	9	0	52
Apr 2024	41	7	2	50
May 2024	11	10	5	26
Jun 2024	11	10	1	22
Jul 2024	20	13	2	35
Aug 2024	23	14	4	41
Sep 2024	12	5	0	17
Oct 2024	9	6	1	16
Nov 2024	11	10	2	23
Dec 2024	17	5	0	22
Jan 2025	18	12	3	33
Feb 2025	20	13	5	38
Mar 2025	24	13	1	38
Apr 2025	17	35	0	52
May 2025	18	10	0	28
Jun 2025	34	22	1	57
Jul 2025	28	20	1	49
Aug 2025	22	17	0	39
Sep 2025	18	15	2	35
Oct 2025	16	28	0	44
Nov 2025	27	34	4	65

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Short summary

During volcanic eruptions, fine ash settles out of the atmosphere to form deposits. Particle aggregation makes it difficult for models to calculate where fine ash will fall. In this study we show that the Ash3d dispersion model can accurately predict where fine ash will land if one assumes a Gaussian size distribution of aggregates, ~ 0.18–0.23 mm in diameter and 600 kg m⁻³ in density. This aggregation scheme has optimally reproduced deposits for four well-documented eruptions.