Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

Gergely, Mathias; Cooper, Steven J.; Garrett, Timothy J.

doi:https://doi.org/10.5194/acp-17-12011-2017

Articles | Volume 17, issue 19

https://doi.org/10.5194/acp-17-12011-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/acp-17-12011-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 17, issue 19

Research article

|

10 Oct 2017

Research article |

| 10 Oct 2017

Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

Mathias Gergely, Steven J. Cooper, and Timothy J. Garrett

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Mar 2018	14	11	0	25
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Jun 2018	10	8	1	19
Jul 2018	6	6	0	12
Aug 2018	11	11	1	23
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May 2019	5	6	0	11
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Mar 2022	5	11	0	16
Apr 2022	3	2	1	6
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Jun 2023	33	4	1	38
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Sep 2023	35	9	1	45
Oct 2023	37	7	1	45
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Dec 2023	46	14	1	61
Jan 2024	32	14	1	47
Feb 2024	41	7	1	49
Mar 2024	42	18	4	64
Apr 2024	37	12	4	53
May 2024	21	9	0	30
Jun 2024	23	11	1	35
Jul 2024	8	9	3	20
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Sep 2024	10	7	1	18
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Nov 2024	9	5	6	20
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Cumulative views and downloads (calculated since 16 May 2017)

Month	HTML	PDF	XML	Total
May 2017	124	38	11	173
Jun 2017	48	6	16	70
Jul 2017	13	2	0	15
Aug 2017	16	5	0	21
Sep 2017	16	3	0	19
Oct 2017	149	53	7	209
Nov 2017	25	20	0	45
Dec 2017	13	21	0	34
Jan 2018	11	12	1	24
Feb 2018	14	12	0	26
Mar 2018	14	11	0	25
Apr 2018	9	12	1	22
May 2018	13	12	3	28
Jun 2018	10	8	1	19
Jul 2018	6	6	0	12
Aug 2018	11	11	1	23
Sep 2018	10	14	3	27
Oct 2018	9	13	2	24
Nov 2018	14	15	1	30
Dec 2018	10	17	1	28
Jan 2019	7	8	1	16
Feb 2019	5	6	0	11
Mar 2019	7	8	0	15
Apr 2019	9	3	0	12
May 2019	5	6	0	11
Jun 2019	6	4	0	10
Jul 2019	9	8	0	17
Aug 2019	4	7	0	11
Sep 2019	5	3	0	8
Oct 2019	7	5	2	14
Nov 2019	5	5	0	10
Dec 2019	5	6	0	11
Jan 2020	14	4	0	18
Feb 2020	8	6	1	15
Mar 2020	1	3	0	4
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Jun 2020	9	8	0	17
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Aug 2020	9	9	1	19
Sep 2020	7	5	0	12
Oct 2020	5	4	0	9
Nov 2020	4	6	2	12
Dec 2020	2	3	0	5
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Feb 2021	3	3	0	6
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Apr 2021	8	9	0	17
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Jun 2021	6	5	1	12
Jul 2021	7	7	0	14
Aug 2021	7	19	0	26
Sep 2021	10	7	0	17
Oct 2021	7	39	1	47
Nov 2021	6	24	0	30
Dec 2021	10	11	0	21
Jan 2022	10	13	1	24
Feb 2022	6	9	2	17
Mar 2022	5	11	0	16
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Jun 2022	7	2	0	9
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Jan 2023	10	11	0	21
Feb 2023	11	8	0	19
Mar 2023	21	12	0	33
Apr 2023	9	13	0	22
May 2023	31	8	0	39
Jun 2023	33	4	1	38
Jul 2023	43	8	1	52
Aug 2023	33	14	2	49
Sep 2023	35	9	1	45
Oct 2023	37	7	1	45
Nov 2023	20	3	0	23
Dec 2023	46	14	1	61
Jan 2024	32	14	1	47
Feb 2024	41	7	1	49
Mar 2024	42	18	4	64
Apr 2024	37	12	4	53
May 2024	21	9	0	30
Jun 2024	23	11	1	35
Jul 2024	8	9	3	20
Aug 2024	16	8	4	28
Sep 2024	10	7	1	18
Oct 2024	11	5	4	20
Nov 2024	9	5	6	20
Dec 2024	12	12	0	24

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Short summary

This study investigates the importance of snowflake surface-area-to-volume ratio (SAV) for the interpretation of snowfall triple-frequency radar signatures. The results indicate that snowflake SAV has a strong impact on modeled snowfall radar signatures and therefore may be used to further constrain (the large variety and high natural variability of) snowflake shape for snowfall remote sensing, e.g., to distinguish graupel snow from snowfall characterized by large aggregate snowflakes.

Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

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13 citations as recorded by crossref.

12 citations as recorded by crossref.

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