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5. Spatial variability of Saharan dust deposition revealed through a citizen science campaign

Author

Dumont, Marie, primary, Gascoin, Simon, additional, Réveillet, Marion, additional, Voisin, Didier, additional, Tuzet, François, additional, Arnaud, Laurent, additional, Bonnefoy, Mylène, additional, Bacardit Peñarroya, Montse, additional, Carmagnola, Carlo, additional, Deguine, Alexandre, additional, Diacre, Aurélie, additional, Dürr, Lukas, additional, Evrard, Olivier, additional, Fontaine, Firmin, additional, Frankl, Amaury, additional, Fructus, Mathieu, additional, Gandois, Laure, additional, Gouttevin, Isabelle, additional, Gherab, Abdelfateh, additional, Hagenmuller, Pascal, additional, Hansson, Sophia, additional, Herbin, Hervé, additional, Josse, Béatrice, additional, Jourdain, Bruno, additional, Lefevre, Irene, additional, Le Roux, Gaël, additional, Libois, Quentin, additional, Liger, Lucie, additional, Morin, Samuel, additional, Petitprez, Denis, additional, Robledano, Alvaro, additional, Schneebeli, Martin, additional, Salze, Pascal, additional, Six, Delphine, additional, Thibert, Emmanuel, additional, Trachsel, Jürg, additional, Vernay, Matthieu, additional, Viallon-Galinier, Léo, additional, and Voiron, Céline, additional

Published
2023
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9. Supplementary material to "Spatial variability of Saharan dust deposition revealed through a citizen science campaign"

Author

Dumont, Marie, primary, Gascoin, Simon, additional, Réveillet, Marion, additional, Voisin, Didier, additional, Tuzet, François, additional, Arnaud, Laurent, additional, Bonnefoy, Mylène, additional, Bacardit Peñarroya, Montse, additional, Carmagnola, Carlo, additional, Deguine, Alexandre, additional, Diacre, Aurélie, additional, Dürr, Lukas, additional, Evrard, Olivier, additional, Fontaine, Firmin, additional, Frankl, Amaury, additional, Fructus, Mathieu, additional, Gandois, Laure, additional, Gouttevin, Isabelle, additional, Gherab, Abdelfateh, additional, Hagenmuller, Pascal, additional, Hansson, Sophia, additional, Herbin, Hervé, additional, Josse, Béatrice, additional, Jourdain, Bruno, additional, Lefevre, Irene, additional, Le Roux, Gaël, additional, Libois, Quentin, additional, Liger, Lucie, additional, Morin, Samuel, additional, Petitprez, Denis, additional, Robledano, Alvaro, additional, Schneebeli, Martin, additional, Salze, Pascal, additional, Six, Delphine, additional, Thibert, Emmanuel, additional, Trachsel, Jürg, additional, Vernay, Matthieu, additional, Viallon-Galinier, Léo, additional, and Voiron, Céline, additional

Published
2023
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13. Dépôts massifs de poussières sahariennes sur le manteau neigeux dans les Alpes et les Pyrénées du 5 au 7 février 2021 : Contexte, enjeux et résultats préliminaires Version du 3 mai 2021

Author

Réveillet, Marion, Tuzet, François, Dumont, Marie, Gascoin, Simon, Arnaud, Laurent, Bonnefoy, Mylène, Carmagnola, Carlo, Deguine, Alexandre, Evrard, Olivier, Flin, Frédéric, Fontaine, Firmin, Gandois, Laure, Hagenmuller, Pascal, Herbin, Hervé, Josse, Béatrice, Lafaysse, Matthieu, Le Roux, Gaël, Morin, Samuel, Nabat, Pierre, Petitprez, Denis, Picard, Ghislain, Robledano, Alvaro, Schneebeli, Martin, Six, Delphine, Thibert, Emmanuel, Vernay, Matthieu, Viallon-Galinier, Léo, Voiron, Céline, Voisin, Didier, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), IRD/CESBIO, Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d’Optique Atmosphérique - UMR 8518 (LOA), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Physicochimie des Processus de Combustion et de l’Atmosphère - UMR 8522 (PC2A), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Géochimie Des Impacts (GEDI), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire Ecologie Fonctionnelle et Environnement (LEFE), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT), Swiss Federal Institute for Forest, Snow and Landscape Research WSL, CNRM, Université de Toulouse, Météo-France, CNRS, Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Laboratoire Ecologie Fonctionnelle et Environnement (ECOLAB), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDE]Environmental Sciences, Panoply
Published
2021

14. Can Saharan dust deposition impact snowpack stability in the French Alps?

Author

Dick, Oscar, Viallon-Galinier, Léo, Tuzet, François, Hagenmuller, Pascal, Fructus, Mathieu, Reuter, Benjamin, Lafaysse, Matthieu, and Dumont, Marie

Abstract

Saharan dust deposits can turn snow covered mountains into a spectacular orange landscape. When avalanches release, a formerly buried dust layer can become apparent, possibly marking the base of the crown. This appearance may suggest a relation between avalanche release and the prior deposited dust, which found mention among recreationists and avalanche professionals alike. While dust deposition affects the absorption of solar energy altering snowpack temperatures and melt rates, to date, there is no clear scientific evidence that dust deposition can significantly modify avalanche activity. Here we investigate, using an ensemble snow cover model, the impact of dust deposition on snow properties and mechanical stability by comparing simulations with and without dust deposition for synthetic and observed dust deposition events. The study focuses on two typical avalanche situations: artificial triggering on persistent weak layers and natural release of wet-snow avalanches. We study several situations with and without dust deposition and demonstrate how sensitive the impact of dust deposition is to the deposited dust mass, the slope aspect, the elevation and the meteorological conditions following the dust deposition. The additional energy absorbed by the dust layer speeds up warming and may advance surface wetting to ease the formation of a melt-freeze crust. If the crust is buried, the phenomenon of a strong temperature gradient close to the crust may promote the formation of persistent weak layers inside the snowpack after weak layer burial. On the other hand, the melt-freeze crust may also lead to an increase of snowpack stability by redistributing the stress applied to buried weak layers. Regarding wet-snow instabilities, we show that dust deposition can advance the onset of wet-snow avalanche activity by up to one month in spring, as hypothesized in previous studies. Thus, the impact of Saharan dust deposition on snow mechanical stability can be either neutral, positive or negative, depending on the local snow and meteorological conditions. Even though not all physical processes are implemented, state-of the art snow cover models are able to mimic the speed-up of crust formation and snow instability models can point out relevant situations for avalanche forecasting. [ABSTRACT FROM AUTHOR]

Published
2022
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15. Combining random forests and class-balancing to discriminate between three classes of avalanche activity in the French Alps

Author

UCL - SST/ELI/ELIC - Earth & Climate, Dkengne Sielenou, Pascal, Viallon-Galinier, Léo, Hagenmuller, Pascal, Naveau, Philippe, Morin, Samuel, Dumont, Marie, Verfaillie, Deborah, Eckert, Nicolas, UCL - SST/ELI/ELIC - Earth & Climate, Dkengne Sielenou, Pascal, Viallon-Galinier, Léo, Hagenmuller, Pascal, Naveau, Philippe, Morin, Samuel, Dumont, Marie, Verfaillie, Deborah, and Eckert, Nicolas

Abstract

Determining avalanche activity corresponding to given snow and meteorological conditions is an old problem of high practical relevance. To address it, numerous statistical forecasting models have been developed, but intercomparisons of their efficiency on very large datasets are seldom. In this work, an approach combining random forests with class-balancing is presented and systematically compared with competing methods currently described in the avalanche literature. On more than 50 years of daily avalanche observations, in the 23 massifs of the French Alps, the competing classifiers are evaluated on their ability to distinguish three classes of avalanche activity: non-avalanche days, days with moderate activity, and days with high activity. Moreover, the variables of higher importance in the random forest classifiers are shown to be coherent with current avalanche literature and a clustering based on these variable importance separates massifs which are known to have different avalanche activities. Our approach opens perspectives to support operational avalanche forecasting.

Published
2021

19. Combining snow physics and machine learning to predict avalanche activity: does it help?

Author

Viallon-Galinier, Léo, Hagenmuller, Pascal, and Eckert, Nicolas

Abstract

Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how the combination of snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a Random Forest approach to predict avalanche days at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent test set, confusion matrices, and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the statistical model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically-based stability indices and their time-derivatives in addition to simple snow and meteorological variables increases the recall from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche days in the training data set, the precision remains low, around 3.5 %, due to the scarcity of avalanche days. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current cutting-edge data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting. [ABSTRACT FROM AUTHOR]

Published
2022
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20. Latest scientific and technical evolutions in the Crocus snowpack model

Author

Lafaysse, Matthieu, primary, Dumont, Marie, additional, Nheili, Rafife, additional, Viallon-Galinier, Léo, additional, Carmagnola, Carlo, additional, Cluzet, Bertrand, additional, Fructus, Mathieu, additional, Hagenmuller, Pascal, additional, Morin, Samuel, additional, Spandre, Pierre, additional, Tuzet, François, additional, and Vionnet, Vincent, additional

Published
2020
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21. Emergence of Shear Bands in Confined Granular Systems: Singularity of the q-Statistics.

Author

Viallon-Galinier, Léo, Combe, Gaël, Richefeu, Vincent, and Picardi Faria Atman, Allbens

Subjects
*SHEAR (Mechanics), *DISTRIBUTION (Probability theory), *GRANULAR materials, *DISPLACEMENT (Mechanics), *IMAGE analysis
Abstract

The statistics of grain displacements probability distribution function (pdf) during the shear of a granular medium displays an unusual dependence with the shear increment upscaling as recently evinced (see "experimental validation of a nonextensive scaling law in confined granular media"). Basically, the pdf of grain displacements has clear nonextensive (q-Gaussian) features at small scales, but approaches to Gaussian characteristics at large shear window scales—the granulence effect. Here, we extend this analysis studying a larger system (more grains considered in the experimental setup), which exhibits a severe shear band fault during the macroscopic straining. We calculate the pdf of grain displacements and the dependency of the q-statistics with the shear increment. This analysis has shown a singular behavior of q at large scales, displaying a non-monotonic dependence with the shear increment. By means of an independent image analysis, we demonstrate that this singular non-monotonicity could be associated with the emergence of a shear band within the confined system. We show that the exact point where the q-value inverts its tendency coincides with the emergence of a giant percolation cluster along the system, caused by the shear band. We believe that this original approach using Statistical Mechanics tools to identify shear bands can be a very useful piece to solve the complex puzzle of the rheology of dense granular systems. [ABSTRACT FROM AUTHOR]

Published
2018
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