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Conference Paper

Benefits of fuzzy methods for the evaluation of high-resolution snow models

Authors

Haddjeri,  Ange
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Baron,  Matthieu
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Lafaysse,  Matthieu
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Le Toumelin,  Louis
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Gascoin,  Simon
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Vionnet,  Vincent
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Lv,  Zhibang
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Marsh,  Christopher B.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Pomeroy,  John
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Dumont,  Marie
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Haddjeri, A., Baron, M., Lafaysse, M., Le Toumelin, L., Gascoin, S., Vionnet, V., Lv, Z., Marsh, C. B., Pomeroy, J., Dumont, M. (2023): Benefits of fuzzy methods for the evaluation of high-resolution snow models, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-0842


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5016631
Abstract
In mountainous areas, accurately resolving snowpack temporal and spatial variability is still challenging for the snow modelling community. The last decades have seen significant advances in snow processes simulations, including wind-induced snow transport. However, the verification/evaluation methods used to confront model results with observations have not improved at the same pace. A direct evaluation of every simulated processes is currently impracticable. Therefore, the evaluation of regional to continental snowpack simulations is mostly done using indirect observations of the physical process of interest. Snow depth measurements from satellite or airborne laser scanner or composite variables of snow absence and presence derived from satellites are usual verification data sources. Yet, using this kind of data in a pixel-to-pixel verification usually shows poor model performance, making it difficult to assess the added value of newly implemented processes, for instance, wind-induced snow transport. Fuzzy verification methods have been developed to account for these difficulties, allowing more spatial tolerance. We will illustrate this challenge with the evaluations of the SnowPappus model, a new simple blowing snow transport model coupled with the Crocus state-of-the-art physical snow model. It is designed to improve the snow spatial variability of the French snowpack simulation system by predicting blowing snow occurrence, transport fluxes and sublimation at 250m resolution. Although pixel-to-pixel comparisons of the Snowpappus model with satellite-retrieved snow depth show low added values of the model, fuzzy verification techniques demonstrate an increased spatial variability with the transport model, in line with observations.