Work Description

Title: Supporting data for the Near-Infrared Emitting and Reflectance-Monitoring Dome Open Access Deposited

http://creativecommons.org/publicdomain/zero/1.0/
Attribute Value
Methodology
  • Figure 1 - Spectral snow albedo data was generated from SNICAR-Online (Flanner et al., 2007); Figure 3 - X-ray micro-computed tomography was done at the U.S. Army Corps of Engineers Engineer Research & Development Center’s Cold Regions Research & Engineering Lab. Scans and analysis were conducted using Bruker microCT instrumentation and software; Figure 4 - Snow bidirectional reflectance factors were measured using the Near-Infrared Emitting and Reflectance Monitoring Dome. Snow specific surface area was measured using Bruker microCT instrumentation and analysis software. Snow bidirectional reflectance factors were modeled for varying ice particle radii and shape habit using the Monte-Carlo method for photon transport; Figure 5 - Snow albedo data was generated from SNICAR-Online (Flanner et al., 2007) and from Monte-Carlo modeling.; Figure 6 - Calibration curves were fit to data using least squares regression analysis.; Figure 7 - Snow bidirectional reflectance factors were measured throughout the day on February 14, 2017 and converted to snow specific surface area using an exponential calibration function.
Description
  • This dataset contains all data used to generate the figures in The Cryosphere manuscript “Measuring Snow Specific Surface Area with 1.30 and 1.55 micro-meter Bidirectional Reflectance Factors,” by Adam Schneider, Mark Flanner, and Roger De Roo. These data support the theory, calibration, and application of the Near-Infrared Emitting and Reflectance Monitoring Dome (NERD), an instrument engineered to rapidly retrieve surface snow specific surface area in the field. Note that this deposit includes a microCT scan database for natural snowfall samples collected in New Hampshire during 2015-2017, comprised of raw tiff files as well as reconstructions, binarized reconstructions, and some 3D model reconstructions.

  • Running python scripts generally require that the following packages are installed: NumPy, SciPy, Matplotlib, Pandas, and ipdb (for debugging).
Creator
Depositor
  • amschne@umich.edu
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
ORSP grant number
  • ARC-1253154
Keyword
Citations to related material
  • Schneider, Adam & Flanner, Mark & De Roo, Roger. (2018). Measuring Snow Specific Surface Area with 1.30 and 1.55 μm Bidirectional Reflectance Factors. The Cryosphere Discussions. https://doi.org/10.5194/tc-2018-198
Resource type
Last modified
  • 03/12/2020
Published
  • 07/12/2018
Language
DOI
  • https://doi.org/10.7302/Z23F4MVC
License
To Cite this Work:
Adam Schneider, Mark Flanner (2018). Supporting data for the Near-Infrared Emitting and Reflectance-Monitoring Dome [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/Z23F4MVC

Relationships

Files (Count: 9; Size: 71 GB)

Supporting data for the Near-Infrared Emitting and Reflectance-Monitoring Dome
Documentation:

- Research Overview -

Summary: The Near-Infrared Emitting and Reflectance-Monitoring Dome (NERD) is an
instrument designed to obtain snow specific surface area (SSA). We obtain snow
SSA by measuring 1.30 and 1.55 micrometer bidirectional reflectance factors
(BRFs) of natural snow. Natural snow was collected after snowfall events in
the winters of 2015, 2016, and 2017 in Hanover, New Hampshire.

Authors: Adam Schneider, Mark Flanner

Funding source: National Science Foundation (ARC-1253154)

- File Inventory -

fig01/ - SNICAR output arrays saved by snow grain effective radius (micrometer)
and black carbon (BC) concentration (ng/g). The spectral albedo
arrays are provided in each text file:

re_164um_0bc.txt
re_164um_100bc.txt
re_55um_0bc.txt
re_55um_100bc.txt

fig04/ - nerd_xct_snow_ssa.py
This file contains NERD snow BRF measurements and X-ray micro computed
tomography snow SSA calculations
- monte_carlo_data.py
This file processes Monte Carlo output data and plots BRFs versus snow
SSA

fig05/ - mc_albedo_ssa.py
This file reads in Monte Carlo data and calculates and plots
directional-hemispherical reflectance as a function of snow specific
surface area for various ice shape habits
- snicar_out.py
SNICAR-ONLINE output (snow.engin.umich.edu)
- monte_carlo_data.py
This file processes Monte Carlo output data and plots black-sky albedo
versus snow SSA

fig06/ - nerd_calibration_curves.py
This file contains NERD snow BRF measurements and X-ray micro computed
tomography snow specific surface area calculations
fig07/ - feb14_data.py
Raw data from February 14, 2017 soot-in-snow experiment

mc_ssa_cal.tar.gz - Monte Carlo output database. These raw output files contain
photon exit angles used to calculate
reflectances. monte_carlo_data.py, in fig04/ and fig05/,
import these data, calculate reflectances, and plot results
in figures 4 and 5.

fig03.tar.gz - MicroCT database. Top directories are named after unique
samples labeled in Figure 4.

- Definition of Terms and Variables -

fig04/nerd_xct_snow_ssa.py and fig06/nerd_calibration_curves.py:

nerd_ssa_cal_XX() sets up the snow SSA data object and adds observational data recorded
by the NERD for the XX degree viewing zenith angle at each wavelength (1.30 and 1.55 microns).

The raw observational data are recorded by self.add_observational_data(), which adds
NERD measurements, microCT derived SSA, and meta-data to the data object.

fig07/feb14_data.py

NERD BRFs are stored in Python dictionaries:

brf_natural, brf_bc, and brf_sand are snow BRFs measured by the NERD on Feb. 14 2017
for natural snow, snow contaminated with black carbon, and snow contaminated with sand, respectively.
Each row represents a set of measurements collected at a given time. Multiple rows
indicate measurements collected at multiple times throughout the day.

brf_cal_50 are measurements of the grey reflectance target
brf_cal_95 are measurements of the white reflectance target

For more information regarding the terms used in this documentation and the
physical meaning of the variables in this dataset, please see the corresponding
manuscript submitted to The Cryosphere (https://doi.org/10.5194/tc-2018-198).

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