Snow ice particle microphysical properties and fall speed from particle images taken in Kiruna (Sweden) 2014–2018

SND-ID: 2021-125

Overview
Data and documentation

Creator/Principal investigator(s)

Thomas Kuhn - Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering

Sandra Vázquez-Martín - Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering

Description

Accurate predictions of snowfall require good knowledge of the microphysical properties of the snow ice crystals and particles. Shape is an important parameter as it strongly influences the scattering properties of the ice particles, and thus their response to remote sensing techniques such as radar measurements. The fall speed of ice particles is another important parameter for both numerical forecast models as well as representation of ice clouds and snow in climate models, as it is responsible for the rate of removal of ice from these models. The particle mass is also a key quantity as it connects the cloud microphysical properties to radiative properties.

The ground-based in-situ instrument Dual Ice Crystal Imager (D-ICI) has been used in Kiruna, Sweden, to determine snow ice particle properties and fall speed simultaneously. D-ICI takes two high-resolution images of the same falling ice particle from two different viewing directions, a top view and a side view. Both images have a pixel resolution of approximately 4 μm/pixel and an optical resolution of approximately 10 μm.

The top-view image with its close to vertical viewing direction is used to provide particle size (maximum dimension), cross-sectional area, and shape of the ice particle. This viewing geometry is chosen instead of a horizontal one because shape and size of ice particles as viewed in the vertical direction are more relevant than these properties viewed horizontally as the vertical fall speed is more strongly influenced by the vertically viewed properties. In addition, a comparison with remote sensing instruments that mostly have a vertical or close to vertical viewing geometry is favoured when the particle properties are measured in the same direction.

The side-view image with its horizontal viewing direction is used both to aid shape determination as well as to determine fall speed by means of a double exposure. Two bright flashes of a light-emitting diode behind the camera illuminate the falling ice particle and create this double exposure, from which the vertical displacement of the particle is measured and used to determine its fall speed.

To add ice particle mass to the data from D-ICI, an empirical relationship between the dimensionless Reynolds and Best numbers can be used. Then, mass of individual ice particles can be derived from measured fall speed, particle size, and cross-sectional area.

During four winter seasons, 2014/2015–2017/2018, D-ICI was employed in Kiruna, northern Sweden (67.8N, 20.4E). The dataset presented here has resulted from the D-ICI measurements during this period and consists of the determined snow ice particle properties and the dual images of the same particles.

The dataset is the basis of the articles:
Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021): Shape dependence of snow crystal fall speed, Atmospheric Chemistry and Physics, 21(10), 7545–7565. https://doi.org/10.5194/acp-21-7545-2021
Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021). Mass of different snow crystal shapes derived from fall speed measurements, Atmospheric Chemistry and Physics, 21(24), 18669–18688. https://doi.org/10.5194/acp-2021-203 Show less..

Language

English

Research principal, contributors, and funding

Research principal

Luleå University of Technology

Responsible department/unit

Department of Computer Science, Electrical and Space Engineering

Protection and ethical review

Data contains personal data

Method and time period

Time period(s) investigated

2014-10-19 – 2018-05-11

Geographic coverage

Geographic spread

Geographic location: Sweden, Norrbotten County, Kiruna Municipality

Geographic description: Measurement site in Kiruna, northern Sweden (67.8°N, 20.4°E).

Topic and keywords

Research area

Natural Sciences, Earth and Related Environmental Sciences, Meteorology and Atmospheric Sciences (The Swedish standard of fields of research 2011)
Climatology / Meteorology / Atmosphere (INSPIRE topic categories)

Keywords

atmospheric conditions, snow, snowfall, snow fall speed, snow crystals

Publications

Sort by name | Sort by year

Vazquez-Martin, S., Kuhn, T., & Eliasson, S. (2020). Shape Dependence of Falling Snow Crystals’ Microphysical Properties Using an Updated Shape Classification. Applied Sciences, 10(3), Article 1163. https://doi.org/10.3390/app10031163
DOI: https://doi.org/10.3390/app10031163
URN: urn:nbn:se:smhi:diva-5682

Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021). Shape dependence of snow crystal fall speed. Atmospheric Chemistry and Physics, 21(10), 7545–7565. https://doi.org/10.5194/acp-21-7545-2021
URN: urn:nbn:se:ltu:diva-82170
DOI: https://doi.org/10.5194/acp-21-7545-2021

Kuhn, T., & Vázquez-Martín, S. (2020). Microphysical properties and fall speed measurements of snow ice crystals using the Dual Ice Crystal Imager (D-ICI). Atmospheric Measurement Techniques, 13, 1273–1285. https://doi.org/10.5194/amt-13-1273-2020
DOI: https://doi.org/10.5194/amt-13-1273-2020
URN: urn:nbn:se:ltu:diva-78097

Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021). Mass of different snow crystal shapes derived from fall speed measurements, Atmospheric Chemistry and Physics, 21(24), 18669–18688. https://doi.org/10.5194/acp-2021-203
DOI: https://doi.org/10.5194/acp-21-18669-2021

Show more.. (4)

If you have published anything based on these data, please notify us with a reference to your publication(s). If you are responsible for the catalogue entry, you can update the metadata/data description in DORIS.

Dataset 1

Snow ice particle microphysical properties and fall speed from particle images taken with the Dual Ice Crystal Imager (D-ICI) in Kiruna (Sweden) 2014–2018

Download data

Side.zip (1.32 GB)

Top_particle_cutout.zip (107.76 MB)

Top.zip (1.95 GB)

particle_data.txt (320.14 KB)

Associated documentation

Description_of_dataset_v2.txt

Shape_groups.txt

Shapes.txt

Description

The following description can also be found in the documentation file "Description_of_dataset.txt".

Data in Version 2 are identical to the data in Version 1, but they also contain ice particle mass as new variable.

The dataset is the basis of the articles:
Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021): Shape dependence of snow crystal fall speed, Atmospheric Chemistry and Physics, 21(10), 7545–7565. https://doi.org/10.5194/acp-21-7545-2021
Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (20

Version 2

2021-12-20
https://doi.org/10.5878/2dnq-5x15

Variable(s) added : Added ice particle mass as new variable

Citation

Thomas Kuhn, Sandra Vázquez-Martín. Luleå University of Technology (2021). Snow ice particle microphysical properties and fall speed from particle images taken with the Dual Ice Crystal Imager (D-ICI) in Kiruna (Sweden) 2014–2018. Swedish National Data Service. Version 2. https://doi.org/10.5878/2dnq-5x15

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Data format / data structure

Numeric

Still image

Creator/Principal investigator(s)

Thomas Kuhn - Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering

Sandra Vázquez-Martín - Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering

Keywords

snow fall speed, snowfall, snow crystals, snow

Time period(s) investigated

2014-10-19 – 2018-05-11

Variables

License

Dataset 2

Data 2

Associated documentation

Description_of_dataset_v2.txt

Shape_groups.txt

Shapes.txt

Description

Version 1

2022-03-28
https://doi.org/10.5878/mkm0-b191

Citation

Thomas Kuhn. Luleå University of Technology (2022). Data 2. Swedish National Data Service. Version 1. https://doi.org/10.5878/mkm0-b191

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Data format / data structure

Text

Creator/Principal investigator(s)

Thomas Kuhn - Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering

Keywords

snow fall speed, snowfall, snow crystals, snow

Variables

Published: 2021-05-17

Last updated: 2022-03-28

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