These HDF5 files contain reconstructed snow and ice melt over the Sierra Nevada USA from 
CY 2001-2017 using the ParBal model (Bair et al., 2016; Rittger et al., 2016; Bair et al., 2018). 
Since those papers, we've made two important changes to ParBal: 1) is to use the new CERES 
4A 1 hr forcings; 2) to use a new grain size to clean snow albedo function. The clean snow 
albedo is still then adjusted for impurities, see Bair et al. (2016). All of the current code for 
ParBal is on GitHub: https://github.com/edwardbair/ParBal.

The projection is California Albers with 500 m square pixels and size 1450x876. The h5 files can 
be read with several software packages. We use MATLAB. I've included the GetCoordinateInfo.m MAT 
file to help with reading the geographic information. For further help with the spatial information, 
I've included a "spatial_info.mat" MATLAB file containing:

hdr - a structure with the Referencing matrix, RasterReference, and Projection Structure
x - grid of x coordinates (center of pixel) in CA Albers
y - grid of y coordinates in CA Albers
lat - grid of latitudes in CA albers
lon - grid of longitudes in CA albers

The h5 files contain: 
MATLAB datetimes, geographic information, spacetime cubes of daily melt, SWE, and 
maxswedates, which are not used for this dataset. The spacetime cubes are each one day, 
begin on 1-1 and end on 12-31. For example, in MATLAB, this shows the structure of the CY h5 
file.

h5disp('reconstruction_sierra_500m_CY2017.h5')
HDF5 reconstruction_sierra_500m_CY2017.h5 
Group '/' 
    Attributes:
        'MATLABdates':  736696.000000 736697.000000...736998.000000 
    Group '/Grid' 
        Attributes:
            'mapprojection':  'eqaconicstd'
            'angleunits':  'degrees'
            'aspect':  'normal'
            'falsenorthing':  -4000000.000000 
            'falseeasting':  0.000000 
            'geoid':  6378137.000000 0.081819 
            'maplatlimit':  -90.000000 90.000000 
            'maplonlimit':  -255.000000 15.000000 
            'mapparallels':  34.000000 40.500000 
            'nparallels':  2.000000 
            'origin':  0.000000 -120.000000 0.000000 
            'scalefactor':  1.000000 
            'trimlat':  -90.000000 90.000000 
            'trimlon':  -135.000000 135.000000 
            'ReferencingMatrix':  3x2 H5T_FLOAT
        Dataset 'maxswedates' 
            Size:  1450x876
            MaxSize:  1450x876
            Datatype:   H5T_IEEE_F64LE (double)
            ChunkSize:  1450x876
            Filters:  deflate(9)
            FillValue:  0.000000
        Dataset 'melt' 
            Size:  1450x876x303
            MaxSize:  1450x876x303
            Datatype:   H5T_STD_U16LE (uint16)
            ChunkSize:  1450x876x1
            Filters:  deflate(9)
            FillValue:  0
            Attributes:
                'units':  'mm'
        Dataset 'swe' 
            Size:  1450x876x303
            MaxSize:  1450x876x303
            Datatype:   H5T_STD_U16LE (uint16)
            ChunkSize:  1450x876x1
            Filters:  deflate(9)
            FillValue:  0
            Attributes:
                'units':  'mm'

To read the coordinate info:

hdr=GetCoordinateInfo('reconstruction_sierra_500m_CY2017.h5','/Grid',[1450 876]);

To read all the SWE values for 2017:

yearswe=h5read('reconstruction_sierra_500m_CY2017.h5','/Grid/swe');

The h5 files are stored with block compression such that each day can be decompressed and 
read without going through the entire spacetime cube. For example, to read doy 91 (2017-4-1):

dayswe=h5read('reconstruction_sierra_500m_CY2017.h5','/Grid/swe',[1 1 91],[1450 876 1]);

Note that the h5 file from WY 2017 is only 303 days (2017-1-1 to 2017-10-30) because the 
CERES 4A data were not available past Oct 2017 when these reconstructions were done.

NB 2018-10-3


Bair, E.H., Abreu Calfa, A., Rittger, K. and Dozier, J., 2018. Using machine learning for real-time 
estimates of snow water equivalent in the watersheds of Afghanistan. The Cryosphere, 
12(5): 1579-1594.

Bair, E.H., Rittger, K., Davis, R.E., Painter, T.H. and Dozier, J., 2016. Validating reconstruction 
of snow water equivalent in California's Sierra Nevada using measurements from the 
NASA Airborne Snow Observatory. Water Resources Research, 52: 8437-8460.

Rittger, K., Bair, E.H., Kahl, A. and Dozier, J., 2016. Spatial estimates of snow water equivalent 
from reconstruction. Advances in Water Resources, 94: 345-363.