pypdd 0.3.1

The PDDModel class

Requires: NumPy, SciPy.

A PDD model instance can be created by:

from pypdd import PDDModel
pdd = PDDModel()

Several model parameters can be set at initialization. See help(PDDModel) for a list. Provided two arrays temp and prec of shape (t, x, y) containing temperature and precipitation data, the PDD model can be called with:

pdd(temp, prec)

This will return a dictionary containing a number of two- and three-dimensional arrays, including the number of positive degree day 'pdd' and total surface mass balance 'smb'. Temperature variability can be included in a third array stdv containing temperature standard deviation values:

pdd(temp, prec, stdv)

If any of temp, prec, or stdv has shape (x, y), it will be interpreted as constant in time and expanded along the time dimension. Floats with be interpreted as constant in time and space and expanded along all dimensions.

NetCDF interface

Requires: netCDF4-Python.

The PDDModel class holds a netCDF operator, which can be called by:

pdd.nco('input.nc', 'output.nc')

The file 'input.nc' should contain temperatures and precipitation in variables 'temp' and 'prec'. The calculated number of positive degree days and total surface mass balance are stored in variables 'pdd' and 'smb' of 'output.nc'. Keyword argument output_size or output_variables can be used to produce more output.

The netCDF interface can be used directly from the command line by executing the module as a script:

python pypdd.py -i 'input.nc' -o 'output.nc'

If no input file is provided, an artificial climate will be generated under atm.nc and used by the model. By default, output is saved as smb.nc. Many more command-line options are available. For an overview type:

python pypdd.py --help

GRASS GIS interface

Requires: GRASS GIS.

PyPDD can also operate on GRASS raster maps using the attached module r.pdd. Temperature, precipitation and standard deviation maps should be provided as comma-separated lists:

r.pdd.py temp=list,of,temp,maps prec=list,of,prec,maps pdd=pdd_map smb=smb_map

All time-independent PyPDD output variables can currently be exported as raster maps. Alike any other GRASS module, a graphical prompt can be invoked by calling r.pdd without arguments, and a list of options can be obtained with:

r.pdd.py --help

References

Applications of PyPDD:

• N. Gandy, L. J. Gregoire, J. C. Ely, C. D. Clark, D. M. Hodgson, V. Lee, T. Bradwell, and R. F. Ivanovic. Marine ice sheet instability and ice shelf buttressing of the Minch Ice Stream, northwest Scotland. The Cryosphere, 12(11):3635–3651, https://doi.org/10.5194/tc-12-3635-2018, 2018.

• A. Plach, K. H. Nisancioglu, S. Le clec’h, A. Born, P. M. Langebroek, C. Guo, M. Imhof, and T. F. Stocker. Eemian Greenland SMB strongly sensitive to model choice. Clim. Past, 14:1463–1485, https://doi.org/10.5194/cp-14-1463-2018, 2018.

• J. Seguinot. Spatial and seasonal effects of temperature variability in a positive degree-day glacier surface mass-balance model. J. Glaciol., 59(218):1202–1204, http://doi.org/10.3189/2013JoG13J081, 2013.