Loading [MathJax]/jax/output/HTML-CSS/fonts/TeX/fontdata.js
Articles | Volume 9, issue 1
https://doi.org/10.5194/gmd-9-307-2016
https://doi.org/10.5194/gmd-9-307-2016
Model description paper
 | 
26 Jan 2016
Model description paper |  | 26 Jan 2016

PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1)

S. Härer, M. Bernhardt, and K. Schulz

Related authors

On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales
Stefan Härer, Matthias Bernhardt, Matthias Siebers, and Karsten Schulz
The Cryosphere, 12, 1629–1642, https://doi.org/10.5194/tc-12-1629-2018,https://doi.org/10.5194/tc-12-1629-2018, 2018
Short summary
PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.1.0)
S. Härer, M. Bernhardt, J. G. Corripio, and K. Schulz
Geosci. Model Dev., 6, 837–848, https://doi.org/10.5194/gmd-6-837-2013,https://doi.org/10.5194/gmd-6-837-2013, 2013

Related subject area

Cryosphere
CMIP6 models overestimate sea ice melt, growth and conduction relative to ice mass balance buoy estimates
Alex E. West and Edward W. Blockley
Geosci. Model Dev., 18, 3041–3064, https://doi.org/10.5194/gmd-18-3041-2025,https://doi.org/10.5194/gmd-18-3041-2025, 2025
Short summary
Coupling framework (1.0) for the Úa (2023b) ice sheet model and the FESOM-1.4 z-coordinate ocean model in an Antarctic domain
Ole Richter, Ralph Timmermann, G. Hilmar Gudmundsson, and Jan De Rydt
Geosci. Model Dev., 18, 2945–2960, https://doi.org/10.5194/gmd-18-2945-2025,https://doi.org/10.5194/gmd-18-2945-2025, 2025
Short summary
A gradient-boosted tree framework to model the ice thickness of the world's glaciers (IceBoost v1.1)
Niccolò Maffezzoli, Eric Rignot, Carlo Barbante, Troels Petersen, and Sebastiano Vascon
Geosci. Model Dev., 18, 2545–2568, https://doi.org/10.5194/gmd-18-2545-2025,https://doi.org/10.5194/gmd-18-2545-2025, 2025
Short summary
Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025,https://doi.org/10.5194/gmd-18-1829-2025, 2025
Short summary
Quantitative sub-ice and marine tracing of Antarctic sediment provenance (TASP v1.0)
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025,https://doi.org/10.5194/gmd-18-1673-2025, 2025
Short summary

Cited articles

Aronica, G., Bates, P. D., and Horrit, M. S.: Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE, Hydrol. Process., 16, 2001–2016, https://doi.org/10.1002/hyp.398, 2002.
Aschenwald, J., Leichter, K., Tasser, E., and Tappeiner, U.: Spatiotemporal landscape analysis in mountainous terrain by means of small format photography: a methodological approach, IEEE T. Geosci. Remote, 39, 885–893, https://doi.org/10.1109/36.917917, 2001.
Bernhardt, M. and Schulz, K.: SnowSlide: a simple routine for calculating gravitational snow transport, Geophys. Res. Lett., 37, L11502, https://doi.org/10.1029/2010GL043086, 2010.
Bernhardt, M., Schulz, K., Liston, G. E., and Zängl, G.: The influence of lateral snow redistribution processes on snow melt and sublimation in alpine regions, J. Hydrol., 424–425, 196–206, https://doi.org/10.1016/j.jhydrol.2012.01.001, 2012.
Bernhardt, M., Härer, S., Jacobeit, J., Wetzel, K. F., and Schulz, K.: The virtual alpine observatory – research focus Alpine hydrology, Hydrol. Wasserbewirts., 58, 241–243, 2014.
Short summary
This paper describes a new method to produce spatially and temporally calibrated NDSI-based...
Share