These HDF5 files contain reconstructed snow and ice melt over the Sierra Nevada USA from 
water year 2001-2019 using the ParBal model (Bair et al., 2016; Rittger et al., 2016; Bair et al., 2018).

These model runs were created using snow cover from a new approach called the Snow Property Inversion from Remote Sensing (SPIRES). Code and publication are forthcoming.

ParBal is on GitHub: https://github.com/edwardbair/ParBal.

The projection is CA Teale Albers with WGS 84 datum and 500m square pixels. The h5 files can 
be read with several software packages. We use MATLAB. We recommend using the "getMelt.m" function in the ParBal repository to read. This function will return the SWE or melt for any number of days (chunks) from the h5 file and the geographic header information.

function [x,hdr,h5mdates]=getMelt(h5file,meltvariable,varargin)
%reads different melt variables from h5 files output by
%reconstructSWE
%input:
%h5file - HDF 5 file, string
%meltvariable - variable to read, string, choices are:
% 'swe' - daily reconstructed swe, mm
% 'melt' - daily melt, mm
% 'maxswedates' - date of max swe
% optional 3rd input is scalar or vector of matdates to read, if not
% supplied, whole cube is read
%output:
% x - image or cube of requested variable, corresponding to dates
% requested
% hdr - geographic info
% h5mdates - matdates


The h5 files contain:

MATLAB datetimes, geographic information, spacetime cubes of daily melt, and SWE. The spacetime cubes are each one day, 
begin on 10-1 and end on 9-30. For example, in MATLAB, this shows the structure of the WY 2019 h5 file.

h5disp('reconstruction_Sierra_2019.h5')
HDF5 reconstruction_Sierra_2019.h5 
Group '/' 
    Attributes:
        'MATLABdates':  737334.000000 ... 737698.000000 
        'ISOdates':  2018274.000000 ... 2019273.000000 
    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:  1841x1334
            MaxSize:  1841x1334
            Datatype:   H5T_STD_U16LE (uint16)
            ChunkSize:  1841x1334
            Filters:  deflate(9)
            FillValue:  65535
        Dataset 'melt' 
            Size:  1841x1334x365
            MaxSize:  1841x1334x365
            Datatype:   H5T_STD_U16LE (uint16)
            ChunkSize:  1841x1334x1
            Filters:  deflate(9)
            FillValue:  65535
            Attributes:
                'units':  'mm'
        Dataset 'swe' 
            Size:  1841x1334x365
            MaxSize:  1841x1334x365
            Datatype:   H5T_STD_U16LE (uint16)
            ChunkSize:  1841x1334x1
            Filters:  deflate(9)
            FillValue:  65535
            Attributes:
                'units':  'mm'


To read all the SWE values for 2019 use either:

[swe,hdr,matdates]=getMelt('reconstruction_Sierra_2019.h5','swe')

or 

swe=h5read('reconstruction_Sierra_2019.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 2017-4-1:

dayswe=getMelt('reconstruction_Sierra_2019.h5','swe',datenum([2017 4 1]))


See grids.zip for csv files of the x,y,lat,lon grids.
 
NB 3/24/2021

References

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.