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).