Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 467–480, 2021
Nonlin. Processes Geophys., 28, 467–480, 2021
Research article
16 Sep 2021
Research article | 16 Sep 2021

Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics

Guillaume Evin et al.

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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
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Cited articles

Bellier, J., Bontron, G., and Zin, I.: Using Meteorological Analogues for Reordering Postprocessed Precipitation Ensembles in Hydrological Forecasting, Water Resour. Res., 53, 10085–10107,, 2017. a
Boisserie, M., Decharme, B., Descamps, L., and Arbogast, P.: Land Surface Initialization Strategy for a Global Reforecast Dataset, Q. J. Roy. Meteor. Soc., 142, 880–888,, 2016. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,, 2001. a
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A.: Classification and Regression Trees, Chapman and Hall/CRC, Boca Raton, United States, 1984. a
Bremnes, J. B.: Ensemble Postprocessing Using Quantile Function Regression Based on Neural Networks and Bernstein Polynomials, Mon. Weather Rev., 148, 403–414,, 2020. a
Short summary
Forecasting the height of new snow is essential for avalanche hazard surveys, road and ski resort management, tourism attractiveness, etc. Météo-France operates a probabilistic forecasting system using a numerical weather prediction system and a snowpack model. It provides better forecasts than direct diagnostics but exhibits significant biases. Post-processing methods can be applied to provide automatic forecasting products from this system.