We have prepared scripts in Matlab, Jupyter Notebook, and Python that will calculate snow water
equivalent based on day, month, year, lat, lon, and snow depth at the observation point. In this
repo you will find a single matlab (.m) function file, a single jupyter notebook (.ipynb) file,
a single python (.py) file, two zipped ESRI ASCII grid files, and one (.rtf) file with information
about the grid files. The grids contain climatological normals of winter precipitation (Dec-Jan-Feb)
and temperature difference (diff between mean T of warmest month and mean T of coldest month).
To use, unzip the grids and place them in the same location as the .m, .ipynb, or .py file.
The SWE calculator implements a power law regression in order to produce a value of snow water
equivalent (SWE) based on various parameters associated with a snow depth measurement. The
following information on units, etc., is critical:
H - snow depth (mm)
Y - year
M - month (1 --> 12)
D - day (1 --> 31 (or 28, or 30, as appropriate))
LAT - latitude. Positive for N. Hem.
LON - longitude (signed). So, -120 or so for North America.
SWE - snow water equivalent (mm) DOY - day of water year (Oct 1 is the origin, i.e. DOY=1)
Specific script information is below.
function [SWE,DOY] = dfhill_swe_calculator(Y,M,D,H,LAT,LON)
The input variables can be scalars (single measurement) or vectors (batch measurements).
David Hill, Oregon State University, June 2019
The input variables can be a list or an array of one or more values.
Required modules/packages:
import numpy as np
from osgeo import gdal
from scipy.interpolate import interp2d
from datetime import date
Christina Aragon, Oregon State University, December 2019