Articles | Volume 14, issue 11
https://doi.org/10.5194/tc-14-3995-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-14-3995-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulating optical top-of-atmosphere radiance satellite images over snow-covered rugged terrain
Centre d'Etudes de la Neige, Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, 38000 Grenoble, France
Institut des Géosciences de l'Environnement (IGE), UGA, CNRS, UMR 5001, Grenoble, France
Centre d'Etudes de la Neige, Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, 38000 Grenoble, France
Ghislain Picard
Institut des Géosciences de l'Environnement (IGE), UGA, CNRS, UMR 5001, Grenoble, France
Fanny Larue
Institut des Géosciences de l'Environnement (IGE), UGA, CNRS, UMR 5001, Grenoble, France
François Tuzet
Centre d'Etudes de la Neige, Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, 38000 Grenoble, France
Institut des Géosciences de l'Environnement (IGE), UGA, CNRS, UMR 5001, Grenoble, France
Clément Delcourt
Centre d'Etudes de la Neige, Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, 38000 Grenoble, France
Institut des Géosciences de l'Environnement (IGE), UGA, CNRS, UMR 5001, Grenoble, France
now at: Cluster Aarde en Klimaat, Faculteit der Bètawetenschappen, VU University,De Boelelaan 1081–1087, Amsterdam, the Netherlands
Laurent Arnaud
Institut des Géosciences de l'Environnement (IGE), UGA, CNRS, UMR 5001, Grenoble, France
Related authors
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, https://doi.org/10.5194/tc-14-4553-2020, 2020
Short summary
Short summary
This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
Adrien Ooms, Mathieu Casado, Ghislain Picard, Laurent Arnaud, Maria Hörhold, Andrea Spolaor, Rita Traversi, Joel Savarino, Patrick Ginot, Pete Akers, Birthe Twarloh, and Valérie Masson-Delmotte
EGUsphere, https://doi.org/10.5194/egusphere-2025-3259, https://doi.org/10.5194/egusphere-2025-3259, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This work presents a new approach to the estimation of accumulation rates at Concordia Station, East-Antarctica, for the last 20 years, from a new data set of chemical tracers and snow micro-scale properties measured in a snow trench. Multi-annual and meter to decameter scale variability of accumulation rates are compared again in-situ measurements of surface laser scanner and stake farm, with very good agreement. This further constrains SMB estimation for Antarctica at high temporal resolution.
Titouan Tcheng, Elise Fourré, Christophe Leroy-Dos-Santos, Frédéric Parrenin, Emmanuel Le Meur, Frédéric Prié, Olivier Jossoud, Roxanne Jacob, Bénédicte Minster, Olivier Magand, Cécile Agosta, Niels Dutrievoz, Vincent Favier, Léa Baubant, Coralie Lassalle-Bernard, Mathieu Casado, Martin Werner, Alexandre Cauquoin, Laurent Arnaud, Bruno Jourdain, Ghislain Picard, Marie Bouchet, and Amaëlle Landais
EGUsphere, https://doi.org/10.5194/egusphere-2025-2863, https://doi.org/10.5194/egusphere-2025-2863, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Studying Antarctic ice cores is crucial to assess past climate changes, as they hold historical climate data. This study examines multiple ice cores from three sites in coastal Adélie Land to see if combining cores improves data interpretability. It does at two sites, but at a third, wind-driven snow layer mixing limited benefits. We show that using multiple ice cores from one location can better uncover climate history, especially in areas with less wind disturbance.
Anna C. Talucci, Michael M. Loranty, Jean E. Holloway, Brendan M. Rogers, Heather D. Alexander, Natalie Baillargeon, Jennifer L. Baltzer, Logan T. Berner, Amy Breen, Leya Brodt, Brian Buma, Jacqueline Dean, Clement J. F. Delcourt, Lucas R. Diaz, Catherine M. Dieleman, Thomas A. Douglas, Gerald V. Frost, Benjamin V. Gaglioti, Rebecca E. Hewitt, Teresa Hollingsworth, M. Torre Jorgenson, Mark J. Lara, Rachel A. Loehman, Michelle C. Mack, Kristen L. Manies, Christina Minions, Susan M. Natali, Jonathan A. O'Donnell, David Olefeldt, Alison K. Paulson, Adrian V. Rocha, Lisa B. Saperstein, Tatiana A. Shestakova, Seeta Sistla, Oleg Sizov, Andrey Soromotin, Merritt R. Turetsky, Sander Veraverbeke, and Michelle A. Walvoord
Earth Syst. Sci. Data, 17, 2887–2909, https://doi.org/10.5194/essd-17-2887-2025, https://doi.org/10.5194/essd-17-2887-2025, 2025
Short summary
Short summary
Wildfires have the potential to accelerate permafrost thaw and the associated feedbacks to climate change. We assembled a dataset of permafrost thaw depth measurements from burned and unburned sites contributed by researchers from across the northern high-latitude region. We estimated maximum thaw depth for each measurement, which addresses a key challenge: the ability to assess impacts of wildfire on maximum thaw depth when measurement timing varies.
Kévin Fourteau, Julien Brondex, Clément Cancès, and Marie Dumont
EGUsphere, https://doi.org/10.5194/egusphere-2025-444, https://doi.org/10.5194/egusphere-2025-444, 2025
Short summary
Short summary
The percolation of liquid water down snowpacks is a complex phenomenon, and its representation can sometimes be complicated for snowpack models. The goal of this article is to transpose some state-of-the-art strategies used for modeling liquid percolation in other media (such as rocks or soil) into snowpack models. With this, snowpack models can be made more efficient, requiring less time and power to perform their computation.
Florent Domine, Mireille Quémener, Ludovick Bégin, Benjamin Bouchard, Valérie Dionne, Sébastien Jerczynski, Raphaël Larouche, Félix Lévesque-Desrosiers, Simon-Olivier Philibert, Marc-André Vigneault, Ghislain Picard, and Daniel C. Côté
The Cryosphere, 19, 1757–1774, https://doi.org/10.5194/tc-19-1757-2025, https://doi.org/10.5194/tc-19-1757-2025, 2025
Short summary
Short summary
Shrubs buried in snow absorb solar radiation and reduce irradiance in the snowpack. This decreases photochemical reaction rates and emissions to the atmosphere. By monitoring irradiance in snowpacks with and without shrubs, we conclude that shrubs absorb solar radiation as much as 140 ppb of soot and reduce irradiance by a factor of 2. Shrub expansion in the Arctic may therefore affect tropospheric composition during the snow season with climatic effects.
Léon Roussel, Marie Dumont, Marion Réveillet, Delphine Six, Marin Kneib, Pierre Nabat, Kevin Fourteau, Diego Monteiro, Simon Gascoin, Emmanuel Thibert, Antoine Rabatel, Jean-Emmanuel Sicart, Mylène Bonnefoy, Luc Piard, Olivier Laarman, Bruno Jourdain, Mathieu Fructus, Matthieu Vernay, and Matthieu Lafaysse
EGUsphere, https://doi.org/10.5194/egusphere-2025-1741, https://doi.org/10.5194/egusphere-2025-1741, 2025
Short summary
Short summary
Saharan dust deposits frequently color alpine glaciers orange. Mineral dust reduces snow albedo and increases snow and glaciers melt rate. Using physical modeling, we quantified the impact of dust on the Argentière Glacier over the period 2019–2022. We found that that the contribution of mineral dust to the melt represents between 6 and 12 % of Argentière Glacier summer melt. At specific locations, the impact of dust over one year can rise to an equivalent of 1 meter of melted ice.
Léa Elise Bonnefoy, Catherine Prigent, Ghislain Picard, Clément Soriot, Alice Le Gall, Lise Kilic, and Carlos Jimenez
EGUsphere, https://doi.org/10.5194/egusphere-2024-3972, https://doi.org/10.5194/egusphere-2024-3972, 2025
Short summary
Short summary
Microwave radiometry senses the thermal emission from a target, whereas its active counterpart, radar, sends a signal to the target and measures the signal reflected back. We simultaneously model radar and radiometry over the East Antarctic ice sheet, which we propose as an analog for icy moons: we can reproduce most data with a unique model. Saturn's moons' radar brightness cannot be reproduced and must be caused by processes unaccounted for in the model and less active in the Antarctic.
Marion Leduc-Leballeur, Ghislain Picard, Pierre Zeiger, and Giovanni Macelloni
EGUsphere, https://doi.org/10.5194/egusphere-2025-732, https://doi.org/10.5194/egusphere-2025-732, 2025
Short summary
Short summary
This study presents a quantitative and synthetic classification of the snowpack in 10 dry-wet status by aggregating separate binary indicators derived from satellite observations. The classification follows the expected evolution of the melt season: night refreezing is frequent at the onset, sustained melting is observed during the summer peak, and remnant liquid water at depth occurs at the end. This dataset improves the knowledge of melt processes using passive microwave remote sensing.
Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux
The Cryosphere, 19, 769–792, https://doi.org/10.5194/tc-19-769-2025, https://doi.org/10.5194/tc-19-769-2025, 2025
Short summary
Short summary
This study presents an efficient method to improve large-scale snow albedo simulations by considering the spatial variability in light-absorbing particles (LAPs) like black carbon and dust. A global climatology of LAP deposition was created and used to optimize a parameter in the Crocus snow model. Testing at 10 global sites improved albedo predictions by 10 % on average and over 25 % in the Arctic. This method can enhance other snow models' predictions without complex simulations.
Inès Ollivier, Hans Christian Steen-Larsen, Barbara Stenni, Laurent Arnaud, Mathieu Casado, Alexandre Cauquoin, Giuliano Dreossi, Christophe Genthon, Bénédicte Minster, Ghislain Picard, Martin Werner, and Amaëlle Landais
The Cryosphere, 19, 173–200, https://doi.org/10.5194/tc-19-173-2025, https://doi.org/10.5194/tc-19-173-2025, 2025
Short summary
Short summary
The role of post-depositional processes taking place at the ice sheet's surface on the water stable isotope signal measured in polar ice cores is not fully understood. Using field observations and modelling results, we show that the original precipitation isotopic signal at Dome C, East Antarctica, is modified by post-depositional processes and provide the first quantitative estimation of their mean impact on the isotopic signal observed in the snow.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
Short summary
Short summary
The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Lucas R. Diaz, Clement J. F. Delcourt, Moritz Langer, Michael M. Loranty, Brendan M. Rogers, Rebecca C. Scholten, Tatiana A. Shestakova, Anna C. Talucci, Jorien E. Vonk, Sonam Wangchuk, and Sander Veraverbeke
Earth Syst. Dynam., 15, 1459–1482, https://doi.org/10.5194/esd-15-1459-2024, https://doi.org/10.5194/esd-15-1459-2024, 2024
Short summary
Short summary
Our study in eastern Siberia investigated how fires affect permafrost thaw depth in larch forests. We found that fire induces deeper thaw, yet this process was mediated by topography and vegetation. By combining field and satellite data, we estimated summer thaw depth across an entire fire scar. This research provides insights into post-fire permafrost dynamics and the use of satellite data for mapping fire-induced permafrost thaw.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
Short summary
Short summary
Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024, https://doi.org/10.5194/essd-16-3913-2024, 2024
Short summary
Short summary
High-accuracy precision maps of the surface temperature of snow were acquired with an uncooled thermal-infrared camera during winter 2021–2022 and spring 2023. The accuracy – i.e., mean absolute error – improved from 1.28 K to 0.67 K between the seasons thanks to an improved camera setup and temperature stabilization. The dataset represents a major advance in the validation of satellite measurements and physical snow models over a complex topography.
Romilly Harris Stuart, Amaëlle Landais, Laurent Arnaud, Christo Buizert, Emilie Capron, Marie Dumont, Quentin Libois, Robert Mulvaney, Anaïs Orsi, Ghislain Picard, Frédéric Prié, Jeffrey Severinghaus, Barbara Stenni, and Patricia Martinerie
The Cryosphere, 18, 3741–3763, https://doi.org/10.5194/tc-18-3741-2024, https://doi.org/10.5194/tc-18-3741-2024, 2024
Short summary
Short summary
Ice core δO2/N2 records are useful dating tools due to their local insolation pacing. A precise understanding of the physical mechanism driving this relationship, however, remain ambiguous. By compiling data from 15 polar sites, we find a strong dependence of mean δO2/N2 on accumulation rate and temperature in addition to the well-documented insolation dependence. Snowpack modelling is used to investigate which physical properties drive the mechanistic dependence on these local parameters.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
The Cryosphere, 18, 3081–3116, https://doi.org/10.5194/tc-18-3081-2024, https://doi.org/10.5194/tc-18-3081-2024, 2024
Short summary
Short summary
Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Richard Essery, Ghislain Picard, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Jérôme Bouffard, Alessandro Di Bella, and Michele Scagliola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1583, https://doi.org/10.5194/egusphere-2024-1583, 2024
Preprint archived
Short summary
Short summary
Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurement, but uncertainty arises from interaction of the signal with the snow cover. Therefore, modelling the signal interaction with the snow is necessary to improve retrieval. A radar model was used to simulate the radar signal from the snow-covered sea ice. This work paved the way to improved physical algorithm to retrieve snow depth and sea ice thickness for radar altimeter missions.
Naomi E. Ochwat, Ted A. Scambos, Alison F. Banwell, Robert S. Anderson, Michelle L. Maclennan, Ghislain Picard, Julia A. Shates, Sebastian Marinsek, Liliana Margonari, Martin Truffer, and Erin C. Pettit
The Cryosphere, 18, 1709–1731, https://doi.org/10.5194/tc-18-1709-2024, https://doi.org/10.5194/tc-18-1709-2024, 2024
Short summary
Short summary
On the Antarctic Peninsula, there is a small bay that had sea ice fastened to the shoreline (
fast ice) for over a decade. The fast ice stabilized the glaciers that fed into the ocean. In January 2022, the fast ice broke away. Using satellite data we found that this was because of low sea ice concentrations and a high long-period ocean wave swell. We find that the glaciers have responded to this event by thinning, speeding up, and retreating by breaking off lots of icebergs at remarkable rates.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
Short summary
Short summary
In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Justin Murfitt, Claude Duguay, Ghislain Picard, and Juha Lemmetyinen
The Cryosphere, 18, 869–888, https://doi.org/10.5194/tc-18-869-2024, https://doi.org/10.5194/tc-18-869-2024, 2024
Short summary
Short summary
This research focuses on the interaction between microwave signals and lake ice under wet conditions. Field data collected for Lake Oulujärvi in Finland were used to model backscatter under different conditions. The results of the modelling likely indicate that a combination of increased water content and roughness of different interfaces caused backscatter to increase. These results could help to identify areas where lake ice is unsafe for winter transportation.
Claudio Stefanini, Giovanni Macelloni, Marion Leduc-Leballeur, Vincent Favier, Benjamin Pohl, and Ghislain Picard
The Cryosphere, 18, 593–608, https://doi.org/10.5194/tc-18-593-2024, https://doi.org/10.5194/tc-18-593-2024, 2024
Short summary
Short summary
Local and large-scale meteorological conditions have been considered in order to explain some peculiar changes of snow grains on the East Antarctic Plateau from 2000 to 2022, by using remote sensing observations and reanalysis. We identified some extreme grain size events on the highest ice divide, resulting from a combination of conditions of low wind speed and low temperature. Moreover, the beginning of seasonal grain growth has been linked to the occurrence of atmospheric rivers.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
Short summary
Short summary
Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Samuel Morin, Hugues François, Marion Réveillet, Eric Sauquet, Louise Crochemore, Flora Branger, Étienne Leblois, and Marie Dumont
Hydrol. Earth Syst. Sci., 27, 4257–4277, https://doi.org/10.5194/hess-27-4257-2023, https://doi.org/10.5194/hess-27-4257-2023, 2023
Short summary
Short summary
Ski resorts are a key socio-economic asset of several mountain areas. Grooming and snowmaking are routinely used to manage the snow cover on ski pistes, but despite vivid debate, little is known about their impact on water resources downstream. This study quantifies, for the pilot ski resort La Plagne in the French Alps, the impact of grooming and snowmaking on downstream river flow. Hydrological impacts are mostly apparent at the seasonal scale and rather neutral on the annual scale.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
Short summary
Short summary
Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Thomas Dethinne, Quentin Glaude, Ghislain Picard, Christoph Kittel, Patrick Alexander, Anne Orban, and Xavier Fettweis
The Cryosphere, 17, 4267–4288, https://doi.org/10.5194/tc-17-4267-2023, https://doi.org/10.5194/tc-17-4267-2023, 2023
Short summary
Short summary
We investigate the sensitivity of the regional climate model
Modèle Atmosphérique Régional(MAR) to the assimilation of wet-snow occurrence estimated by remote sensing datasets. The assimilation is performed by nudging the MAR snowpack temperature. The data assimilation is performed over the Antarctic Peninsula for the 2019–2021 period. The results show an increase in the melt production (+66.7 %) and a decrease in surface mass balance (−4.5 %) of the model for the 2019–2020 melt season.
Yaowen Zheng, Nicholas R. Golledge, Alexandra Gossart, Ghislain Picard, and Marion Leduc-Leballeur
The Cryosphere, 17, 3667–3694, https://doi.org/10.5194/tc-17-3667-2023, https://doi.org/10.5194/tc-17-3667-2023, 2023
Short summary
Short summary
Positive degree-day (PDD) schemes are widely used in many Antarctic numerical ice sheet models. However, the PDD approach has not been systematically explored for its application in Antarctica. We have constructed a novel grid-cell-level spatially distributed PDD (dist-PDD) model and assessed its accuracy. We suggest that an appropriately parameterized dist-PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.
Esteban Alonso-González, Simon Gascoin, Sara Arioli, and Ghislain Picard
The Cryosphere, 17, 3329–3342, https://doi.org/10.5194/tc-17-3329-2023, https://doi.org/10.5194/tc-17-3329-2023, 2023
Short summary
Short summary
Data assimilation techniques are a promising approach to improve snowpack simulations in remote areas that are difficult to monitor. This paper studies the ability of satellite-observed land surface temperature to improve snowpack simulations through data assimilation. We show that it is possible to improve snowpack simulations, but the temporal resolution of the observations and the algorithm used are critical to obtain satisfactory results.
Fanny Brun, Owen King, Marion Réveillet, Charles Amory, Anton Planchot, Etienne Berthier, Amaury Dehecq, Tobias Bolch, Kévin Fourteau, Julien Brondex, Marie Dumont, Christoph Mayer, Silvan Leinss, Romain Hugonnet, and Patrick Wagnon
The Cryosphere, 17, 3251–3268, https://doi.org/10.5194/tc-17-3251-2023, https://doi.org/10.5194/tc-17-3251-2023, 2023
Short summary
Short summary
The South Col Glacier is a small body of ice and snow located on the southern ridge of Mt. Everest. A recent study proposed that South Col Glacier is rapidly losing mass. In this study, we examined the glacier thickness change for the period 1984–2017 and found no thickness change. To reconcile these results, we investigate wind erosion and surface energy and mass balance and find that melt is unlikely a dominant process, contrary to previous findings.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
Short summary
Short summary
Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Sara Arioli, Ghislain Picard, Laurent Arnaud, and Vincent Favier
The Cryosphere, 17, 2323–2342, https://doi.org/10.5194/tc-17-2323-2023, https://doi.org/10.5194/tc-17-2323-2023, 2023
Short summary
Short summary
To assess the drivers of the snow grain size evolution during snow drift, we exploit a 5-year time series of the snow grain size retrieved from spectral-albedo observations made with a new, autonomous, multi-band radiometer and compare it to observations of snow drift, snowfall and snowmelt at a windy location of coastal Antarctica. Our results highlight the complexity of the grain size evolution in the presence of snow drift and show an overall tendency of snow drift to limit its variations.
Oscar Dick, Léo Viallon-Galinier, François Tuzet, Pascal Hagenmuller, Mathieu Fructus, Benjamin Reuter, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 17, 1755–1773, https://doi.org/10.5194/tc-17-1755-2023, https://doi.org/10.5194/tc-17-1755-2023, 2023
Short summary
Short summary
Saharan dust deposition can drastically change the snow color, turning mountain landscapes into sepia scenes. Dust increases the absorption of solar energy by the snow cover and thus modifies the snow evolution and potentially the avalanche risk. Here we show that dust can lead to increased or decreased snowpack stability depending on the snow and meteorological conditions after the deposition event. We also show that wet-snow avalanches happen earlier in the season due to the presence of dust.
Ghislain Picard, Marion Leduc-Leballeur, Alison F. Banwell, Ludovic Brucker, and Giovanni Macelloni
The Cryosphere, 16, 5061–5083, https://doi.org/10.5194/tc-16-5061-2022, https://doi.org/10.5194/tc-16-5061-2022, 2022
Short summary
Short summary
Using a snowpack radiative transfer model, we investigate in which conditions meltwater can be detected from passive microwave satellite observations from 1.4 to 37 GHz. In particular, we determine the minimum detectable liquid water content, the maximum depth of detection of a buried wet snow layer and the risk of false alarm due to supraglacial lakes. These results provide information for the developers of new, more advanced satellite melt products and for the users of the existing products.
Dominic Saunderson, Andrew Mackintosh, Felicity McCormack, Richard Selwyn Jones, and Ghislain Picard
The Cryosphere, 16, 4553–4569, https://doi.org/10.5194/tc-16-4553-2022, https://doi.org/10.5194/tc-16-4553-2022, 2022
Short summary
Short summary
We investigate the variability in surface melt on the Shackleton Ice Shelf in East Antarctica over the last 2 decades (2003–2021). Using daily satellite observations and the machine learning approach of a self-organising map, we identify nine distinct spatial patterns of melt. These patterns allow comparisons of melt within and across melt seasons and highlight the importance of both air temperatures and local controls such as topography, katabatic winds, and albedo in driving surface melt.
Ghislain Picard, Henning Löwe, and Christian Mätzler
The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, https://doi.org/10.5194/tc-16-3861-2022, 2022
Short summary
Short summary
Microwave satellite observations used to monitor the cryosphere require radiative transfer models for their interpretation. These models represent how microwaves are scattered by snow and ice. However no existing theory is suitable for all types of snow and ice found on Earth. We adapted a recently published generic scattering theory to snow and show how it may improve the representation of snows with intermediate densities (~500 kg/m3) and/or with coarse grains at high microwave frequencies.
Clement Jean Frédéric Delcourt and Sander Veraverbeke
Biogeosciences, 19, 4499–4520, https://doi.org/10.5194/bg-19-4499-2022, https://doi.org/10.5194/bg-19-4499-2022, 2022
Short summary
Short summary
This study provides new equations that can be used to estimate aboveground tree biomass in larch-dominated forests of northeast Siberia. Applying these equations to 53 forest stands in the Republic of Sakha (Russia) resulted in significantly larger biomass stocks than when using existing equations. The data presented in this work can help refine biomass estimates in Siberian boreal forests. This is essential to assess changes in boreal vegetation and carbon dynamics.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Gauthier Vérin, Florent Domine, Marcel Babin, Ghislain Picard, and Laurent Arnaud
The Cryosphere, 16, 3431–3449, https://doi.org/10.5194/tc-16-3431-2022, https://doi.org/10.5194/tc-16-3431-2022, 2022
Short summary
Short summary
Snow physical properties on Arctic sea ice are monitored during the melt season. As snow grains grow, and the snowpack thickness is reduced, the surface albedo decreases. The extra absorbed energy accelerates melting. Radiative transfer modeling shows that more radiation is then transmitted to the snow–sea-ice interface. A sharp increase in transmitted radiation takes place when the snowpack thins significantly, and this coincides with the initiation of the phytoplankton bloom in the seawater.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, https://doi.org/10.5194/tc-16-3357-2022, 2022
Short summary
Short summary
We compared the snowpack at two sites separated by less than 1 km, one in shrub tundra and the other one within the boreal forest. Even though the snowpack was twice as thick at the forested site, we found evidence that the vertical transport of water vapor from the bottom of the snowpack to its surface was important at both sites. The snow model Crocus simulates no water vapor fluxes and consequently failed to correctly simulate the observed density profiles.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
Short summary
Short summary
The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
Alvaro Robledano, Ghislain Picard, Laurent Arnaud, Fanny Larue, and Inès Ollivier
The Cryosphere, 16, 559–579, https://doi.org/10.5194/tc-16-559-2022, https://doi.org/10.5194/tc-16-559-2022, 2022
Short summary
Short summary
Topography controls the surface temperature of snow-covered, mountainous areas. We developed a modelling chain that uses ray-tracing methods to quantify the impact of a few topographic effects on snow surface temperature at high spatial resolution. Its large spatial and temporal variations are correctly simulated over a 50 km2 area in the French Alps, and our results show that excluding a single topographic effect results in cooling (or warming) effects on the order of 1 °C.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
Short summary
Short summary
The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Florent Veillon, Marie Dumont, Charles Amory, and Mathieu Fructus
Geosci. Model Dev., 14, 7329–7343, https://doi.org/10.5194/gmd-14-7329-2021, https://doi.org/10.5194/gmd-14-7329-2021, 2021
Short summary
Short summary
In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo. Therefore, we have developed the VALHALLA method to optimize snow spectral albedo calculations through the determination of spectrally fixed radiative variables. The development of VALHALLA v1.0 with the use of the snow albedo model TARTES and the spectral irradiance model SBDART indicates a considerable reduction in calculation time while maintaining an adequate accuracy of albedo values.
Maria Belke-Brea, Florent Domine, Ghislain Picard, Mathieu Barrere, and Laurent Arnaud
Biogeosciences, 18, 5851–5869, https://doi.org/10.5194/bg-18-5851-2021, https://doi.org/10.5194/bg-18-5851-2021, 2021
Short summary
Short summary
Expanding shrubs in the Arctic change snowpacks into a mix of snow, impurities and buried branches. Snow is a translucent medium into which light penetrates and gets partly absorbed by branches or impurities. Measurements of light attenuation in snow in Northern Quebec, Canada, showed (1) black-carbon-dominated light attenuation in snowpacks without shrubs and (2) buried branches influence radiation attenuation in snow locally, leading to melting and pockets of large crystals close to branches.
Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, Michaël Ablain, Rémi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, and Samuel Morin
The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, https://doi.org/10.5194/tc-15-4975-2021, 2021
Short summary
Short summary
Since 2020, the Copernicus High Resolution Snow & Ice Monitoring Service has distributed snow cover maps at 20 m resolution over Europe in near-real time. These products are derived from the Sentinel-2 Earth observation mission, with a revisit time of 5 d or less (cloud-permitting). Here we show the good accuracy of the snow detection over a wide range of regions in Europe, except in dense forest regions where the snow cover is hidden by the trees.
Marie Dumont, Frederic Flin, Aleksey Malinka, Olivier Brissaud, Pascal Hagenmuller, Philippe Lapalus, Bernard Lesaffre, Anne Dufour, Neige Calonne, Sabine Rolland du Roscoat, and Edward Ando
The Cryosphere, 15, 3921–3948, https://doi.org/10.5194/tc-15-3921-2021, https://doi.org/10.5194/tc-15-3921-2021, 2021
Short summary
Short summary
The role of snow microstructure in snow optical properties is only partially understood despite the importance of snow optical properties for the Earth system. We present a dataset combining bidirectional reflectance measurements and 3D images of snow. We show that the snow reflectance is adequately simulated using the distribution of the ice chord lengths in the snow microstructure and that the impact of the morphological type of snow is especially important when ice is highly absorptive.
Daniela Krampe, Frank Kauker, Marie Dumont, and Andreas Herber
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-100, https://doi.org/10.5194/tc-2021-100, 2021
Manuscript not accepted for further review
Short summary
Short summary
Reliable and detailed Arctic snow data are limited. Evaluation of the performance of atmospheric reanalysis compared to measurements in northeast Greenland generally show good agreement. Both data sets are applied to an Alpine snow model and the performance for Arctic conditions is investigated: Simulated snow depth evolution is reliable, but vertical snow profiles show weaknesses. These are smaller with an adapted parametrisation for the density of newly fallen snow for harsh Arctic conditions.
Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont
Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, https://doi.org/10.5194/gmd-14-1595-2021, 2021
Short summary
Short summary
In the mountains, the combination of large model error and observation sparseness is a challenge for data assimilation. Here, we develop two variants of the particle filter (PF) in order to propagate the information content of observations into unobserved areas. By adjusting observation errors or exploiting background correlation patterns, we demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy with the PF in an idealised setting.
Christian Vincent, Diego Cusicanqui, Bruno Jourdain, Olivier Laarman, Delphine Six, Adrien Gilbert, Andrea Walpersdorf, Antoine Rabatel, Luc Piard, Florent Gimbert, Olivier Gagliardini, Vincent Peyaud, Laurent Arnaud, Emmanuel Thibert, Fanny Brun, and Ugo Nanni
The Cryosphere, 15, 1259–1276, https://doi.org/10.5194/tc-15-1259-2021, https://doi.org/10.5194/tc-15-1259-2021, 2021
Short summary
Short summary
In situ glacier point mass balance data are crucial to assess climate change in different regions of the world. Unfortunately, these data are rare because huge efforts are required to conduct in situ measurements on glaciers. Here, we propose a new approach from remote sensing observations. The method has been tested on the Argentière and Mer de Glace glaciers (France). It should be possible to apply this method to high-spatial-resolution satellite images and on numerous glaciers in the world.
Alison F. Banwell, Rajashree Tri Datta, Rebecca L. Dell, Mahsa Moussavi, Ludovic Brucker, Ghislain Picard, Christopher A. Shuman, and Laura A. Stevens
The Cryosphere, 15, 909–925, https://doi.org/10.5194/tc-15-909-2021, https://doi.org/10.5194/tc-15-909-2021, 2021
Short summary
Short summary
Ice shelves are thick floating layers of glacier ice extending from the glaciers on land that buttress much of the Antarctic Ice Sheet and help to protect it from losing ice to the ocean. However, the stability of ice shelves is vulnerable to meltwater lakes that form on their surfaces during the summer. This study focuses on the northern George VI Ice Shelf on the western side of the AP, which had an exceptionally long and extensive melt season in 2019/2020 compared to the previous 31 seasons.
Nora Helbig, Yves Bühler, Lucie Eberhard, César Deschamps-Berger, Simon Gascoin, Marie Dumont, Jesus Revuelto, Jeff S. Deems, and Tobias Jonas
The Cryosphere, 15, 615–632, https://doi.org/10.5194/tc-15-615-2021, https://doi.org/10.5194/tc-15-615-2021, 2021
Short summary
Short summary
The spatial variability in snow depth in mountains is driven by interactions between topography, wind, precipitation and radiation. In applications such as weather, climate and hydrological predictions, this is accounted for by the fractional snow-covered area describing the fraction of the ground surface covered by snow. We developed a new description for model grid cell sizes larger than 200 m. An evaluation suggests that the description performs similarly well in most geographical regions.
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, https://doi.org/10.5194/tc-14-4553-2020, 2020
Short summary
Short summary
This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
César Deschamps-Berger, Simon Gascoin, Etienne Berthier, Jeffrey Deems, Ethan Gutmann, Amaury Dehecq, David Shean, and Marie Dumont
The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, https://doi.org/10.5194/tc-14-2925-2020, 2020
Short summary
Short summary
We evaluate a recent method to map snow depth based on satellite photogrammetry. We compare it with accurate airborne laser-scanning measurements in the Sierra Nevada, USA. We find that satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountains.
Cited articles
Arnaud, L., Picard, G., Champollion, N., Domine, F., Gallet, J., Lefebvre, E., Fily, M., and Barnola, J.:
Measurement of vertical profiles of snow specific surface area with a 1 cm resolution using infrared reflectance: instrument description and validation,
J. Glaciol.,
57, 17–29, https://doi.org/10.3189/002214311795306664, 2011. a
ASTER GDEM Validation Team:
ASTER Global DEM Validation Summary Report, meti & nasa edn.,
available at: https://lpdaac.usgs.gov/documents/28/ASTER_GDEM_Validation_1_Summary_Report.pdf (last access: 12 November 2020), 2009. a
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.:
Potential impacts of a warming climate on water availability in snow-dominated regions,
Nature,
438, 303–309, https://doi.org/10.1038/nature04141, 2005. a
Brun, F., Dumont, M., Wagnon, P., Berthier, E., Azam, M. F., Shea, J. M., Sirguey, P., Rabatel, A., and Ramanathan, Al.: Seasonal changes in surface albedo of Himalayan glaciers from MODIS data and links with the annual mass balance, The Cryosphere, 9, 341–355, https://doi.org/10.5194/tc-9-341-2015, 2015. a
Bühler, Y., Meier, L., and Ginzler, C.:
Potential of Operational High Spatial Resolution Near-Infrared Remote Sensing Instruments for Snow Surface Type Mapping,
IEEE Geosci. Remote S.,
12, 821–825, https://doi.org/10.1109/LGRS.2014.2363237, 2015. a
Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, 2016. a
Campagnolo, M. L., Sun, Q., Liu, Y., Schaaf, C., Wang, Z., and Román, M. O.:
Estimating the effective spatial resolution of the operational BRDF, albedo, and nadir reflectance products from MODIS and VIIRS,
Remote Sens. Environ.,
175, 52–64, https://doi.org/10.1016/J.RSE.2015.12.033, 2016. a
Centre national d'études spatiales & Noveltis Kalideos Alpes: Risques Gravitaires,
Cryosphère et Végetation en zones alpines vus de l’espace, available at: https://alpes.kalideos.fr/,
last access: 12 November 2020. a
Chappuis, J.:
Sur le spectre d'absorption de l'ozone,
C.R. Acad. Sci. Paris
91, 985–986, 1880. a
Civco, D. L.:
Topographic Normalization of Landsat Thematic Mapper Digital Imagery,
Photogramm. Eng. Rem. S., 55, 1303–1309,
1989. a
Cohen, J.:
Snow cover and climate,
Weather,
49, 150–156, https://doi.org/10.1002/j.1477-8696.1994.tb05997.x, 1994. a
Colby, J. D.:
Topographic normalization in rugged terrain, in:
Photogramm. Eng. Rem. S., 57, 531–537,
1991. a
Conese, C., Gilabert, M. A., Maselli, F., and Bottai, L.:
Topographic Normalization of Tm Scenes Through the Use of an Atmospheric Correction Method and Digital Terrain Models,
Photogramm. Eng. Rem. S., 59, 1745–1753,
1993a. a
Conese, C., Maracchi, G., and Maselli, F.:
Improvement in Maximum Likelihood Classification performance on highly rugged terrain using Principal Components Analysis,
Int. J. Remote Sens.,
14, 1371–1382, https://doi.org/10.1080/01431169308953963, 1993b. a
Crawford, C. J., Manson, S. M., Bauer, M. E., and Hall, D. K.:
Multitemporal snow cover mapping in mountainous terrain for Landsat climate data record development,
Remote Sens. Environ.,
135, 224–233, https://doi.org/10.1016/j.rse.2013.04.004, 2013. a
Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020. a
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S.:
Remote sensing of snow – a review of available methods,
Int. J. Remote Sens.,
33, 4094–4134, https://doi.org/10.1080/01431161.2011.640964, 2012. a
Dorren, L. K., Maier, B., and Seijmonsbergen, A. C.:
Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification,
Forest Ecol. Manag.,
183, 31–46, https://doi.org/10.1016/S0378-1127(03)00113-0, 2003. a
Dozier, J.:
A clear-sky spectral solar radiation model for snow-covered mountainous terrain,
Water Resour. Res.,
16, 709–718, https://doi.org/10.1029/WR016i004p00709, 1980. a
Dozier, J.:
Snow Reflectance from LANDSAT-4 Thematic Mapper,
IEEE T. Geosci. Remote,
GE-22, 323–328, https://doi.org/10.1109/TGRS.1984.350628, 1984. a
Dozier, J.:
Spectral signature of alpine snow cover from the landsat thematic mapper,
Remote Sens. Environ.,
28, 9–22, https://doi.org/10.1016/0034-4257(89)90101-6, 1989. a, b
Dozier, J. and Frew, J.:
Rapid calculation of terrain parameters for radiation modeling from digital elevation data,
IEEE T. Geosci. Remote,
28, 963–969, https://doi.org/10.1109/36.58986, 1990. a, b
Dozier, J. and Painter, T. H.:
Multispectral and Hyperspectral Remote Sensing of Alpine Snow Properties,
Annu. Rev. Earth Pl. Sc.,
32, 465–494, https://doi.org/10.1146/annurev.earth.32.101802.120404, 2004. a
Dozier, J., Bruno, J., and Downey, P.:
A faster solution to the horizon problem,
Comput. Geosci.,
7, 145–151, https://doi.org/10.1016/0098-3004(81)90026-1, 1981. a, b
Dubayah, R. and Rich, P. M.:
Topographic solar radiation models for GIS,
Int. J. Geogr. Inf. Syst.,
9, 405–419, https://doi.org/10.1080/02693799508902046, 1995. a, b
Duguay, C. and Ledrew, E.:
Estimating surface reflectance and albedo from Landsat-5 Thematic Mapper over rugged terrain,
Photogramm. Eng. Rem. S.,
58, 551–558, 1992. a
Dumont, M., Sirguey, P., Arnaud, Y., and Six, D.: Monitoring spatial and temporal variations of surface albedo on Saint Sorlin Glacier (French Alps) using terrestrial photography, The Cryosphere, 5, 759–771, https://doi.org/10.5194/tc-5-759-2011, 2011. a, b
Dumont, M., Durand, Y., Arnaud, Y., and Six, D.:
Variational assimilation of albedo in a snowpack model and reconstruction of the spatial mass-balance distribution of an alpine glacier,
J. Glaciol.,
58, 151–164, https://doi.org/10.3189/2012jog11j163, 2012a. a
Dumont, M., Gardelle, J., Sirguey, P., Guillot, A., Six, D., Rabatel, A., and Arnaud, Y.: Linking glacier annual mass balance and glacier albedo retrieved from MODIS data, The Cryosphere, 6, 1527–1539, https://doi.org/10.5194/tc-6-1527-2012, 2012b. a, b
Dumont, M., Arnaud, L., Picard, G., Libois, Q., Lejeune, Y., Nabat, P., Voisin, D., and Morin, S.: In situ continuous visible and near-infrared spectroscopy of an alpine snowpack, The Cryosphere, 11, 1091–1110, https://doi.org/10.5194/tc-11-1091-2017, 2017. a
European Centre for Medium-Range Weather Forecasts: ECMWF catalogue, available at:
https://www.ecmwf.int/, last access: 12 November 2020. a
European Space Agency: Copernicus Open Access Hub, available at:
https://scihub.copernicus.eu/dhus/, last access: 12 November 2020. a
Fily, M., Bourdelles, B., Dedieu, J., and Sergent, C.:
Comparison of in situ and Landsat Thematic Mapper derived snow grain characteristics in the alps,
Remote Sens. Environ.,
59, 452–460, https://doi.org/10.1016/S0034-4257(96)00113-7, 1997. a
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., and Tschudi, M. A.:
Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008,
Nat. Geosci.,
4, 151–155, https://doi.org/10.1038/ngeo1062, 2011. a
Frey, H. and Paul, F.:
On the suitability of the SRTM DEM and ASTER GDEM for the compilation of: Topographic parameters in glacier inventories,
Int. J. Appl. Earth Obs.,
18, 480–490, https://doi.org/10.1016/j.jag.2011.09.020, 2012. a
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.: Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data, Earth Syst. Sci. Data, 11, 493–514, https://doi.org/10.5194/essd-11-493-2019, 2019. a, b
Gastellu-Etchegorry, J. P., Martin, E., and Gascon, F.:
DART: a 3D model for simulating satellite images and studying surface radiation budget,
Int. J. Remote Sens.,
25, 73–96, https://doi.org/10.1080/0143116031000115166, 2004. a
GDAL/OGR contributors:
GDAL/OGR Geospatial Data Abstraction software Library,
Open Source Geospatial Foundation, available at:
https://gdal.org (last access: 12 November 2020), 2019. a
Guyomarc'h, G., Bellot, H., Vionnet, V., Naaim-Bouvet, F., Déliot, Y., Fontaine, F., Puglièse, P., Nishimura, K., Durand, Y., and Naaim, M.: A meteorological and blowing snow data set (2000–2016) from a high-elevation alpine site (Col du Lac Blanc, France, 2720 ), Earth Syst. Sci. Data, 11, 57–69, https://doi.org/10.5194/essd-11-57-2019, 2019. a
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K. J.:
MODIS snow-cover products,
Remote Sens. Environ.,
83, 181–194, https://doi.org/10.1016/S0034-4257(02)00095-0, 2002. a
Holben, B. and Justice, C.:
An examination of spectral band ratioing to reduce the topographic effect on remotely sensed data,
Int. J. Remote Sens.,
2, 115–133, https://doi.org/10.1080/01431168108948349, 1981. a
Holben, B. N. and Justice, C. .O:
The Topographic Effect on Spectral Response from Nadir-Pointing Sensors,
Photogramm. Eng. Rem. S., 46, 1191–1200,
1980. a
Horn, B.:
Hill shading and the reflectance map,
P. IEEE,
69, 14–47, https://doi.org/10.1109/PROC.1981.11918, 1981. a
Jamieson, B. and Stethem, C.:
Snow Avalanche Hazards and Management in Canada: Challenges and Progress,
Nat. Hazards,
26, 35–53, https://doi.org/10.1023/A:1015212626232, 2002. a
Klein, A. G. and Stroeve, J.:
Development and validation of a snow albedo algorithm for the MODIS instrument,
Ann. Glaciol.,
34, 45–52, https://doi.org/10.3189/172756402781817662, 2002. a, b
Kokhanovsky, A., Lamare, M., Danne, O., Brockmann, C., Dumont, M., Picard, G., Arnaud, L., Favier, V., Jourdain, B., Le Meur, E., Di Mauro, B., Aoki, T., Niwano, M., Rozanov, V., Korkin, S., Kipfstuhl, S., Freitag, J., Hoerhold, M., Zuhr, A., Vladimirova, D., Faber, A.-K., Steen-Larsen, H. C., Wahl, S., Andersen, J. K., Vandecrux, B., van As, D., Mankoff, K. D., Kern, M., Zege, E., and Box, J. E.:
Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument,
Remote Sens.-Basel,
11, https://doi.org/10.3390/rs11192280, 2019. a
Kokhanovsky, A. A. and Breon, F. M.:
Validation of an analytical snow BRDF model using PARASOL multi-angular and multispectral observations,
IEEE Geosci. Remote S.,
9, 928–932, https://doi.org/10.1109/LGRS.2012.2185775, 2012. a, b
Kokhanovsky, A. A. and Schreier, M.:
The determination of snow albedo using combined aatsr and meris observations,
European Space Agency, (Special Publication) ESA SP, 2008. a
Kokhanovsky, A. A. and Zege, E. P.:
Scattering optics of snow,
Appl. Optics,
43, 1589–1602, https://doi.org/10.1364/AO.43.001589, 2004. a, b, c
König, M., Winther, J.-G., and Isaksson, E.:
Measuring snow and glacier ice properties from satellite,
Rev. Geophys.,
39, 1–27, https://doi.org/10.1029/1999RG000076, 2001. a
Lamare, M.: REDRESS GitHub repository, GitHub, available at: https://github.com/maximlamare/REDRESS, last access:
12 November 2020. a
Larue, F., Picard, G., Arnaud, L., Ollivier, I., Delcourt, C., Lamare, M., Tuzet, F., Revuelto, J., and Dumont, M.: Snow albedo sensitivity to macroscopic surface roughness using a new ray-tracing model, The Cryosphere, 14, 1651–1672, https://doi.org/10.5194/tc-14-1651-2020, 2020. a
Lee, S. and Clarke, K.:
An assessment of differences in algorithms for computing fundamental topographic parameters,
AutoCarto Proceedings Papers, available at: https://cartogis.org/docs/proceedings/2005/lee_clark.pdf (last access: 12 November 2020), 2005. a
Lenoble, J., Brogniez, C., de La Casinière, A., Cabot, T., Buchard, V., and Guirado, F.: Measurements of UV aerosol optical depth in the French Southern Alps, Atmos. Chem. Phys., 8, 6597–6602, https://doi.org/10.5194/acp-8-6597-2008, 2008. a
Leprieur, J., Durand, C., and Peyron, J.:
Influence of topography on forest reflectance using Landsat Thematic Mapper and digital terrain data,
Photogramm. Eng. Rem. S.,
54, 491–496, 1988. a
Li, X., Cheng, G., Chen, X., and Lu, L.:
Modification of solar radiation model over rugged terrain,
Chinese Sci. Bull.,
44, 1345–1349, https://doi.org/10.1007/BF02885977, 1999. a
Lonjou, V., Desjardins, C., Hagolle, O., Petrucci, B., Tremas, T., Dejus, M., Makarau, A., and Auer, S.:
MACCS-ATCOR joint algorithm (MAJA),
in: Remote Sensing of Clouds and the Atmosphere XXI, vol. 10001,
Proc. SPIE,
10001, https://doi.org/10.1117/12.2240935, 2016. a
Maignan, F., Bréon, F.-M., and Lacaze, R.:
Bidirectional reflectance of Earth targets: evaluation of analytical models using a large set of spaceborne measurements with emphasis on the Hot Spot,
Remote Sens. Environ.,
90, 210–220, https://doi.org/10.1016/j.rse.2003.12.006, 2004. a
Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., and Gascon, F.:
Sen2Cor for Sentinel-2,
in: Image and Signal Processing for Remote Sensing XXIII, vol. 10427,
Proc. SPIE,
10427, https://doi.org/10.1117/12.2278218, 2017. a
Malcher, P., Floricioiu, D., and Rott, H.:
Snow mapping in Alpine areas using medium resolution spectrometric sensors,
in: IGARSS 2003, 2003 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (IEEE Cat. No.03CH37477), vol. 4,
IEEE,
https://doi.org/10.1109/IGARSS.2003.1294603, 2835–2837, 2003. a
Masson, T., Dumont, M., Mura, M. D., Sirguey, P., Gascoin, S., Dedieu, J.-P., and Chanussot, J.:
An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data,
Remote Sens.-Basel,
10, 619, https://doi.org/10.3390/rs10040619, 2018. a
Mayer, B., Hoch, S. W., and Whiteman, C. D.: Validating the MYSTIC three-dimensional radiative transfer model with observations from the complex topography of Arizona's Meteor Crater, Atmos. Chem. Phys., 10, 8685–8696, https://doi.org/10.5194/acp-10-8685-2010, 2010. a
Mishra, V., Sharma, J., and Khanna, R.:
Review of topographic analysis methods for the western Himalaya using AWiFS and MODIS satellite imagery,
Ann. Glaciol.,
51, 153–160, https://doi.org/10.3189/172756410791386526, 2010. a
Mousivand, A., Verhoef, W., Menenti, M., and Gorte, B.:
Modeling Top of Atmosphere Radiance over Heterogeneous Non-Lambertian Rugged Terrain,
Remote Sens.-Basel,
7, 8019–8044, https://doi.org/10.3390/rs70608019, 2015. a
NASA/METI/AIST/U.S. & Japan Spacesystems:
ASTER Global Digital Elevation Model V003 [Data set],
NASA EOSDI edn.,
https://doi.org/10.5067/ASTER/ASTGTM.003, 2019. a
Neckel, H. and Labs, D.:
The solar radiation between 3300 and 12 500 Å,
Sol. Phys.,
90, 205–258, 1984. a
Negi, H. S. and Kokhanovsky, A.: Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data, The Cryosphere, 5, 831–847, https://doi.org/10.5194/tc-5-831-2011, 2011. a
Nolan, M., Larsen, C., and Sturm, M.: Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry, The Cryosphere, 9, 1445–1463, https://doi.org/10.5194/tc-9-1445-2015, 2015. a
Nolin, A. W.:
Recent advances in remote sensing of seasonal snow,
J. Glaciol.,
56, 1141–1150, https://doi.org/10.3189/002214311796406077, 2010. a
Olson, M., Rupper, S., and Shean, D. E.:
Terrain Induced Biases in Clear-Sky Shortwave Radiation Due to Digital Elevation Model Resolution for Glaciers in Complex Terrain,
Front. Earth Sci.,
7, 216, https://doi.org/10.3389/feart.2019.00216, 2019. a
Painter, T. H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R. E., and Dozier, J.:
Retrieval of subpixel snow covered area, grain size, and albedo from MODIS,
Remote Sens. Environ.,
113, 868–879, https://doi.org/10.1016/j.rse.2009.01.001, 2009. a
Picard, G., Dumont, M., Lamare, M., Tuzet, F., Larue, F., Pirazzini, R., and Arnaud, L.: Spectral albedo measurements over snow-covered slopes: theory and slope effect corrections, The Cryosphere, 14, 1497–1517, https://doi.org/10.5194/tc-14-1497-2020, 2020. a, b
Poglio, T., Mathieu-Marni, S., Ranchin, T., Savaria, E., and Wald, L.:
OSIrIS: a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution,
Remote Sens. Environ.,
104, 238–246, https://doi.org/10.1016/j.rse.2006.03.017, 2006. a
Pôle de données et de services surfaces continentales Theias: Theia Neige, available at:
https://www.theia-land.fr/product/neige/, last access: 12 November 2020. a
Proy, C., Tanré, D., and Deschamps, P.:
Evaluation of topographic effects in remotely sensed data,
Remote Sens. Environ.,
30, 21–32, https://doi.org/10.1016/0034-4257(89)90044-8, 1989. a, b
Qu, X. and Hall, A.:
Assessing Snow Albedo Feedback in Simulated Climate Change,
J. Climate,
19, 2617–2630, https://doi.org/10.1175/JCLI3750.1, 2006. a
Qu, Y., Liu, Q., Liang, S., Wang, L., Liu, N., and Liu, S.:
Direct-Estimation Algorithm for Mapping Daily Land-Surface Broadband Albedo From MODIS Data,
IEEE T. Geosci. Remote,
52, 907–919, https://doi.org/10.1109/TGRS.2013.2245670, 2014. a
Rahman, H., Pinty, B., and Verstraete, M. M.:
Coupled surface-atmosphere reflectance (CSAR) model: 2. Semiempirical surface model usable with NOAA advanced very high resolution radiometer data,
J. Geophys. Res.-Atmos.,
98, 20791–20801, https://doi.org/10.1029/93JD02072, 1993. a
Richter, R.:
Correction of atmospheric and topographic effects for high spatial resolution satellite imagery,
Int. J. Remote Sens.,
18, 1099–1111, https://doi.org/10.1080/014311697218593, 1997. a
Richter, R. and Schläpfer, D.:
Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction,
Int. J. Remote Sens.,
23, 2631–2649, https://doi.org/10.1080/01431160110115834, 2002. a
Sandmeier, S. and Itten, K.:
A physically-based model to correct atmospheric and illumination effects in optical satellite data of rugged terrain,
IEEE T. Geosci. Remote,
35, 708–717, https://doi.org/10.1109/36.581991, 1997. a, b
Scambos, T. A., Haran, T. M., Fahnestock, M. A., Painter, T. H., and Bohlander, J.:
MODIS-based Mosaic of Antarctica (MOA) data sets: Continent-wide surface morphology and snow grain size,
Remote Sens. Environ.,
111, 242–257, https://doi.org/10.1016/j.rse.2006.12.020, 2007. a, b
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J. P., Lewis, P., Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., D'Entremont, R. P., Hu, B., Liang, S., Privette, J. L., and Roy, D.:
First operational BRDF, albedo nadir reflectance products from MODIS,
Remote Sens. Environ.,
83, 135–148, https://doi.org/10.1016/S0034-4257(02)00091-3, 2002. a
Schaepman-Strub, G., Schaepman, M., Painter, T., Dangel, S., and Martonchik, J.:
Reflectance quantities in optical remote sensing–definitions and case studies,
Remote Sens. Environ.,
103, 27–42, https://doi.org/10.1016/J.RSE.2006.03.002, 2006. a, b, c, d
Schlapfer, D., Richter, R., and Feingersh, T.:
Operational BRDF effects correction for wide-field-of-view optical scanners (BREFCOR),
IEEE T. Geosci. Remote,
53, 1855–1864, https://doi.org/10.1109/TGRS.2014.2349946, 2015. a, b
Sirguey, P.:
Simple correction of multiple reflection effects in rugged terrain,
Int. J. Remote Sens.,
30, 1075–1081, https://doi.org/10.1080/01431160802348101, 2009. a, b
Sirguey, P., Mathieu, R., and Arnaud, Y.:
Subpixel monitoring of the seasonal snow cover with MODIS at 250 m spatial resolution in the Southern Alps of New Zealand: Methodology and accuracy assessment,
Remote Sens. Environ.,
113, 160–181, https://doi.org/10.1016/J.RSE.2008.09.008, 2009. a, b, c, d, e, f, g, h, i, j, k, l, m
Sirguey, P., Still, H., Cullen, N. J., Dumont, M., Arnaud, Y., and Conway, J. P.: Reconstructing the mass balance of Brewster Glacier, New Zealand, using MODIS-derived glacier-wide albedo, The Cryosphere, 10, 2465–2484, https://doi.org/10.5194/tc-10-2465-2016, 2016. a
Sjoberg, R. W. and Horn, B. K. P.:
Atmospheric effects in satellite imaging of mountainous terrain,
Appl. Optics,
22, 1702, https://doi.org/10.1364/AO.22.001702, 1983. a
Stillinger, T., Roberts, D. A., Collar, N. M., and Dozier, J.:
Cloud Masking for Landsat 8 and MODIS Terra Over Snow-Covered Terrain: Error Analysis and Spectral Similarity Between Snow and Cloud,
Water Resour. Res.,
55, 6169–6184, https://doi.org/10.1029/2019WR024932, 2019. a
Stroeve, J., Nolin, A., and Steffen, K.:
Comparison of AVHRR-derived and in situ surface albedo over the greenland ice sheet,
Remote Sens. Environ.,
62, 262–276, https://doi.org/10.1016/S0034-4257(97)00107-7, 1997. a
Stroeve, J. C., Box, J. E., and Haran, T.:
Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet,
Remote Sens. Environ.,
105, 155–171, https://doi.org/10.1016/J.RSE.2006.06.009, 2006. a
Teillet, P., Guindon, B., and Goodenough, D.: On the Slope-Aspect Correction of Multispectral Scanner Data,
Can. J. Remote Sens.,
8, 84–106, https://doi.org/10.1080/07038992.1982.10855028, 1982. a, b
Trujillo, E., Molotch, N. P., Goulden, M. L., Kelly, A. E., and Bales, R. C.:
Elevation-dependent influence of snow accumulation on forest greening,
Nat. Geosci.,
5, 705, https://doi.org/10.1038/ngeo1571, 2012. a
Vermote, E., Tanré, D., Deuzé, J. L., Herman, M., Morcrette, J. J., and Kotchenova, S. Y.:
6S User Guide Version 3,
available at: http://6s.ltdri.org/pages/manual.html (last access: 12 November 2020), 2006. a
Vermote, E. F., Tanré, D., Deuzé, J. L., Herman, M., and Morcrette, J. J.:
Second simulation of the satellite signal in the solar spectrum, 6s: an overview,
IEEE T. Geosci. Remote,
35, 675–686, https://doi.org/10.1109/36.581987, 1997. a
Warren, S. G.:
Optical properties of snow,
Rev. Geophys.,
20, 67–89, https://doi.org/10.1029/RG020i001p00067, 1982. a
Warren, S. G. and Wiscombe, W. J.:
A Model for the Spectral Albedo of Snow. II: Snow Containing Atmospheric Aerosols,
J. Atmos. Sci.,
37, 2734–2745, https://doi.org/10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2, 1980. a
Warren, S. G., Brandt, R. E., and O'Rawe Hinton, P.:
Effect of surface roughness on bidirectional reflectance of Antarctic snow,
J. Geophys. Res.-Planet.,
103, 25789–25807, https://doi.org/10.1029/98JE01898, 1998. a
Williams, C. J., McNamara, J. P., and Chandler, D. G.: Controls on the temporal and spatial variability of soil moisture in a mountainous landscape: the signature of snow and complex terrain, Hydrol. Earth Syst. Sci., 13, 1325–1336, https://doi.org/10.5194/hess-13-1325-2009, 2009.
a
Wilson, R. T.:
Py6S: A Python interface to the 6S radiative transfer model.,
Comput. Geosci., 51, 166–171, https://doi.org/10.1016/j.cageo.2012.08.002, 2013. a
Woodham, R. and Gray, M.:
An Analytic Method for Radiometric Correction of Satellite Multispectral Scanner Data,
IEEE T. Geosci. Remote,
GE-25, 258–271, https://doi.org/10.1109/TGRS.1987.289798, 1987. a
Xin, L., Koike, T., and Guodong, C.:
Retrieval of snow reflectance from Landsat data in rugged terrain,
Ann. Glaciol.,
34, 31–37, https://doi.org/10.3189/172756402781817635, 2002. a
Yang, C. and Vidal, A.:
Combination of digital elevation models with SPOT-1 HRV multispectral imagery for reflectance factor mapping,
Remote Sens. Environ.,
32, 35–45, https://doi.org/10.1016/0034-4257(90)90096-5, 1990. a
Short summary
Terrain features found in mountainous regions introduce large errors into the calculation of the physical properties of snow using optical satellite images. We present a new model performing rapid calculations of solar radiation over snow-covered rugged terrain that we tested over a site in the French Alps. The results of the study show that all the interactions between sunlight and the terrain should be accounted for over snow-covered surfaces to correctly estimate snow properties from space.
Terrain features found in mountainous regions introduce large errors into the calculation of the...