Data Set ID: 
SNEX17_SSA

SnowEx17 Laser Snow Microstructure Specific Surface Area Data, Version 1

This data set reports vertical profiles of snow reflectance, specific surface area (SSA), and optical equivalent diameter (grain size) at Grand Mesa, Colorado, USA, a snow-covered, forested study site about 40 miles east of the city of Grand Junction, CO. Reflectance was measured in situ using a 1310 nm integrating sphere laser device and converted to SSA and optical equivalent diameter.

This is the most recent version of these data.

Version Summary: 

Initial release

STANDARD Level of Service

Data: Data integrity and usability verified

Documentation: Key metadata and user guide available

User Support: Assistance with data access and usage; guidance on use of data in tools

See All Level of Service Details

Parameter(s):
  • SURFACE RADIATIVE PROPERTIES > REFLECTANCE
  • GLACIERS/ICE SHEETS > FIRN > SNOW GRAIN SIZE
  • SNOW/ICE > SNOW MICROSTRUCTURE > SPECIFIC SURFACE AREA
Data Format(s):
  • PNG
  • Comma-Separated Values (.csv)
Spatial Coverage:
N: 39.10552, 
S: 39.02098, 
E: -107.846975, 
W: -108.23134528824981
Platform(s):GROUND-BASED OBSERVATIONS
Spatial Resolution:
  • Varies x Varies
Sensor(s):IRIS, IceCube
Temporal Coverage:
  • 7 February 2017 to 25 February 2017
Version(s):V1
Temporal ResolutionNot applicableMetadata XML:View Metadata Record
Data Contributor(s):Nick Rutter, Jinmei Pan, Michael Durand, Joshua King, Chris Derksen, Fanny Larue

Geographic Coverage

Other Access Options

Other Access Options

Close

As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

Rutter, N., J. Pan, M. Durand, J. King, C. Derksen, and F. Larue. 2018. SnowEx17 Laser Snow Microstructure Specific Surface Area Data, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/H9C1UVWN1UK3. [Date Accessed].

Back to Top

Collapse All / Open All

Detailed Data Description

Parameters

This data set characterizes snow microstructure using vertical profiles of reflectance, specific surface area (SSA, kg/m2), and optical equivalent diameter of snow grains (DO, mm).

Background color on
Format

Data files are provided in Comma Separated Values (.csv) format. Quick look (browse) images are also available in PNG format that show side-by-side depth profiles of reflectance, SSA, and equivalent optical diameter of snow grains. Each data file also has an associated XML file that contains science metadata.

Background color on
File Contents

Data files begin with a 12-row header that specifies the date and time of acquisition, snowpit ID and location (in UTM), instrument, operator, notes, timing, and total snow depth. For example, data file SnowEx17_SSA_01E_20170210T2000_IRIS.csv contains the following header:

# Date and Time in UTC (yyyy-mm-ddTHH:MM): 2017-02-10T20:00 
# Name field campaign: SnowEx_Week1 
# Snowpit ID: 01E 
# UTMN: 4323962 
# UTME: 747952 
# UTM Zone: 12 
# Instrument: IRIS 
# Operator: Fanny Larue 
# Timing: 30mins 
# Notes: N/A 
# Total snow depth (cm): 83
#

Starting with row 13, the data are stored in columns A through E, with Column F reserved for operator comments (see Figure 1):

Image showing data rows from file a csv file
Figure 1. Column headers and sample data from file SnowEx17_SSA_01E_20170210T2000_IRIS.csv.

Quick look (browse) images show side-by-side depth profiles of reflectance, SSA, and equivalent optical diameter, as shown in Figure 2:

Quick look image for data file SnowEx17_SSA_01E_20170210T2000_IRIS.png
Figure 2. Quick look image for data file SnowEx17_SSA_01E_20170210T2000_IRIS.png.

Background color on
File Naming Convention

Data files utilize the following naming convention:

Example:

  • SnowEx17_SSA_01E_20170210T2000_IRIS.csv
  • SnowEx17_SSA_[SID]_[YYYYMMDD]T[hhmm]_[INST].[EXT]

where:

Table 1. File Naming Convention
Variable Description
SnowEx17_SSA    SnowEx 2017 field season specific surface area  
SID Three digit station ID.
YYYYMMDD Year, month, and day of data acquisition
T Acquisition time follows
hhmm Hour and minute (UTC) of data acquisition in 24-hour format. E.g., 1800 = 18:00 UTC.
INST Instrument code. Values are one of IRIS, IceCubeOSU, or IceCubeNU. See Data Acquisition and Processing for details.
EXT File type: .csv (data file) or .png (quick look)

XML metadata files have the same name as their corresponding .csv files, but with .xml appended. For example: SnowEx17_SSA_01E_20170210T2000_IRIS.csv.xml.

Background color on
Volume

CSV files are approximately 2 KB each. PNG files range from approximately 60—80 KB. The entire data set is approximately 7 MB.

Background color on
Spatial Coverage

Northernmost Latitude: 39.10552º N
Southernmost Latitude: 39.02098º N
Easternmost Longitude: 107.846975° W
Westernmost Longitude: 108.231345° W

Spatial Resolution

Vertical profiles were obtained at 95 locations within the Grand Mesa study site. The vertical distance (depth) between measurements varies, but never exceeds 3 cm.

Projection and Grid Description

All data lie within UTM Zones 12N and 13N. Refer to Table 2 for details.

Table 2. Geolocation Details
Geographic coordinate system WGS 84
Projected coordinate system WGS 84 / UTM zone 12N
WGS 84 / UTM zone 13N
Longitude of true origin -111 (12N)
-105 (13N)
Latitude of true origin 0
Scale factor at longitude of true origin 0.9996
Datum WGS 1984
Ellipsoid/spheroid WGS 84
Units meters
False easting 500000
False northing 0
EPSG Code 32612 (12N)
32613 (13N)
PROJ4 string +proj=utm +zone=12 +datum=WGS84 +units=m +no_defs
Reference https://epsg.io/32612
https://epsg.io/32613

Background color on
Temporal Coverage

07 February 2017 to 25 February 2017 

Temporal Resolution

Measurements for each snow pit were taken within a single time span.

Background color on

Software and Tools

CSV files can be accessed using software that reads ASCII text.

Background color on

Data Acquisition and Processing

Vertical profiles of reflectance to a 1310 nm laser were recorded in the field using one of two integrating sphere systems: IRIS (InfraRed Integrating Sphere) or IceCube. These devices utilize the same underlying principle—the relationship between the hemispherical infrared reflectance of snow and SSA—and differ only in their respective sphere sizes. One IRIS and two IceCube instruments were deployed in the field. The instrument used is denoted in data file names by IRIS, IceCubeOSU, or IceCubeNU.

In the field, a snow sample is illuminated with the intrument's laser. An InGaAs photodiode converts the reflected light to current and the voltages are converted to reflectance using certified standards. SSA is calculated from reflectance during post-processing, using custom calibration algorithms for each IRIS or IceCube instrument. Finally, equivalent optical diameter is computed from SSA.

Quality, Errors, and Limitations

Quality control was performed by visually inspecting graphs of each reflectance, SSA, and equivalent diameter profile.

Background color on

References and Related Publications

Contacts and Acknowledgments

Nick Rutter
Department of Geography
Northumbria University
Newcastle upon Tyne
NE1 8ST
UK

Michael Durand
School Of Earth Sciences
275 Mendenhall Laboratory
125 South Oval Mall
Ohio State University
Columbus OH 43210-1308
USA

Chris Derksen, Josh King
Environment and Climate Change Canada
4905 Dufferin Street
Toronto, ON M3H5T4
CAN

Jinmei Pan
School Of Earth Sciences
275 Mendenhall Laboratory
125 South Oval Mall
Ohio State University
Columbus OH 43210-1308
USA

Fanny Larue
University of Sherbrooke
2500 Boulevard of the University
Sherbrooke, QC J1K 2R1
CAN
 

Document Information

DOCUMENT CREATION DATE

May, 2018

No technical references available for this data set.

How To

Programmatic Data Access Guide
Data from the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) can be accessed directly from our HTTPS file system or through our Application Programming Interface (API). Our API offers you the ability to order data using specific temporal and spatial filters... read more