Articles | Volume 16, issue 9
https://doi.org/10.5194/tc-16-3531-2022
© Author(s) 2022. 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-16-3531-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
02 Sep 2022
Review article |
| 02 Sep 2022
| 02 Sep 2022
Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing
Department of Electrical Engineering and Computer Science,
University of Michigan, Ann Arbor, MI 48109, USA
Michael Durand
Byrd Polar and Climate Research
Center, School of Earth Sciences, The Ohio State University, Columbus, OH 43210, USA
Chris Derksen
Climate Research Division, Environment and Climate Change Canada,
Toronto, Canada
Ana P. Barros
Civil and Environmental Engineering, University of Illinois at
Urbana-Champaign, Urbana, IL, USA
Do-Hyuk Kang
ESSIC, University of Maryland, College Park, MD 20740, USA
Hans Lievens
Division of Soil and Water Management, KU Leuven, Leuven, Belgium
Hans-Peter Marshall
Department of Geoscience, Boise State University, Boise, Idaho, USA
Jiyue Zhu
Department of Electrical Engineering and Computer Science,
University of Michigan, Ann Arbor, MI 48109, USA
Joel Johnson
Department of Electrical and Computer Engineering, The Ohio State
University, Columbus, OH 43212 USA
Joshua King
Climate Research Division, Environment and Climate Change Canada,
Toronto, Canada
Juha Lemmetyinen
Arctic Research Centre, Finnish Meteorological Institute, Helsinki,
Finland
Melody Sandells
Geography and Environmental Sciences, Northumbria University,
Newcastle, UK
Nick Rutter
Geography and Environmental Sciences, Northumbria University,
Newcastle, UK
Paul Siqueira
Electrical and Computer Engineering, University of Massachusetts,
Amherst, MA, USA
Anne Nolin
Department of Geography, University of Nevada-Reno, Reno, NV, USA
Batu Osmanoglu
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Carrie Vuyovich
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Edward Kim
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Drew Taylor
Remote Sensing Center, University of Alabama, Tuscaloosa, AL, USA
Ioanna Merkouriadi
Arctic Research Centre, Finnish Meteorological Institute, Helsinki,
Finland
Ludovic Brucker
Center for Satellite Applications and Research, NOAA/NESDIS, the
US National Ice Center, College Park, MD, USA
Mahdi Navari
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Marie Dumont
Centre d'Etudes de la Neige, Météo-France, Grenoble, France
Richard Kelly
Department of Geography and Environmental Management, University
of Waterloo, Waterloo, Canada
Rhae Sung Kim
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Tien-Hao Liao
Jet Propulsion Laboratory, California Institute of Technology, Pasedena, CA, USA
Firoz Borah
Department of Electrical Engineering and Computer Science,
University of Michigan, Ann Arbor, MI 48109, USA
Xiaolan Xu
Jet Propulsion Laboratory, California Institute of Technology, Pasedena, CA, USA
Related authors
Haokui Xu, Leung Tsang, Julie Miller, Brooke Medley, and Jeol Johnson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2395, https://doi.org/10.5194/egusphere-2024-2395, 2025
Short summary
Short summary
This paper provides a physical model to analyze the brightness temperature time series over the firn aquifer in Greenland and Antarctica. The model can match the V and H SMAP brightness temperature time series well. This model provides a potential to study the aquifer liquid water content with radiometry.
Firoz Kanti Borah, Jonas-Fredrick Jans, Zhenming Huang, Leung Tsang, Hans Lievens, and Edward Kim
EGUsphere, https://doi.org/10.5194/egusphere-2024-1825, https://doi.org/10.5194/egusphere-2024-1825, 2024
Preprint archived
Short summary
Short summary
In this paper, we study radar data collected by Sentinel-1 over mountain regions of Alps. Using physical models of snow and soil surface scattering, we show the reasons for the high sensitivity of cross-polarized observations with snow depth. This accurate modelling for cross-pol using physical models can be then used to retrieve snow depth at for very deep snow at mountain regions using the cross-pol signal.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
Short summary
Short summary
Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Haokui Xu, Brooke Medley, Leung Tsang, Joel T. Johnson, Kenneth C. Jezek, Marco Brogioni, and Lars Kaleschke
The Cryosphere, 17, 2793–2809, https://doi.org/10.5194/tc-17-2793-2023, https://doi.org/10.5194/tc-17-2793-2023, 2023
Short summary
Short summary
The density profile of polar ice sheets is a major unknown in estimating the mass loss using lidar tomography methods. In this paper, we show that combing the active radar data and passive radiometer data can provide an estimation of density properties using the new model we implemented in this paper. The new model includes the short and long timescale variations in the firn and also the refrozen layers which are not included in the previous modeling work.
Marco Brogioni, Mark J. Andrews, Stefano Urbini, Kenneth C. Jezek, Joel T. Johnson, Marion Leduc-Leballeur, Giovanni Macelloni, Stephen F. Ackley, Alexandra Bringer, Ludovic Brucker, Oguz Demir, Giacomo Fontanelli, Caglar Yardim, Lars Kaleschke, Francesco Montomoli, Leung Tsang, Silvia Becagli, and Massimo Frezzotti
The Cryosphere, 17, 255–278, https://doi.org/10.5194/tc-17-255-2023, https://doi.org/10.5194/tc-17-255-2023, 2023
Short summary
Short summary
In 2018 the first Antarctic campaign of UWBRAD was carried out. UWBRAD is a new radiometer able to collect microwave spectral signatures over 0.5–2 GHz, thus outperforming existing similar sensors. It allows us to probe thicker sea ice and ice sheet down to the bedrock. In this work we tried to assess the UWBRAD potentials for sea ice, glaciers, ice shelves and buried lakes. We also highlighted the wider range of information the spectral signature can provide to glaciological studies.
Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
The Cryosphere, 19, 2949–2962, https://doi.org/10.5194/tc-19-2949-2025, https://doi.org/10.5194/tc-19-2949-2025, 2025
Short summary
Short summary
Measuring snow mass from radar measurements is possible with information on snow and a radar model to link the measurements to snow. A key variable in a retrieval is the number of snow layers, with more layers yielding richer information but at increased computational cost. Here, we show the capabilities of a new method for simplifying a complex snowpack while preserving the scattering behavior of the snowpack and conserving its mass.
Prabhakar Shrestha and Ana P. Barros
The Cryosphere, 19, 2895–2911, https://doi.org/10.5194/tc-19-2895-2025, https://doi.org/10.5194/tc-19-2895-2025, 2025
Short summary
Short summary
The study presents the first assimilation of snow depth obtained from repeat pass airborne L-band synthetic aperture radar with a snow hydrology model. The assimilation of snow depth was found to be equivalent to the downscaling of precipitation forcing with a bias correction, which improved the snowpack simulation compared to ground-based observations.
Kajsa Holland-Goon, Randall Bonnell, Daniel McGrath, W. Brad Baxter, Tate Meehan, Ryan Webb, Chris Larsen, Hans-Peter Marshall, Megan Mason, and Carrie Vuyovich
EGUsphere, https://doi.org/10.5194/egusphere-2025-2435, https://doi.org/10.5194/egusphere-2025-2435, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
As part of the NASA SnowEx23 campaign, we conducted detailed snowpack experiments in Alaska’s boreal forests and Arctic tundra. We collected ground-penetrating radar measurements of snow depth along 44 short transects. We then excavated the snowpack from below the transects and measured snow depth, noting any vegetation and void spaces. We used the detailed in situ measurements to evaluate uncertainties in ground-penetrating radar and airborne lidar methods for snow depth retrieval.
Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
EGUsphere, https://doi.org/10.5194/egusphere-2025-3396, https://doi.org/10.5194/egusphere-2025-3396, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Edward H. Bair, Dar A. Roberts, David R. Thompson, Philip G. Brodrick, Brenton A. Wilder, Niklas Bohn, Christopher J. Crawford, Nimrod Carmon, Carrie M. Vuyovich, and Jeff Dozier
The Cryosphere, 19, 2315–2320, https://doi.org/10.5194/tc-19-2315-2025, https://doi.org/10.5194/tc-19-2315-2025, 2025
Short summary
Short summary
Key to the success of future satellite missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that an artifact, called the hook, is often mistaken for soot or dust. Instead, it is caused by three amplifying effects: (1) background reflectance that is too dark, (2) an assumption of level terrain, and (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are presented.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, https://doi.org/10.5194/gmd-18-3583-2025, 2025
Short summary
Short summary
How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Benoit Montpetit, Julien Meloche, Vincent Vionnet, Chris Derksen, Georgina Wooley, Nicolas R. Leroux, Paul Siqueira, J. Max Adams, and Mike Brady
EGUsphere, https://doi.org/10.5194/egusphere-2025-2317, https://doi.org/10.5194/egusphere-2025-2317, 2025
Short summary
Short summary
This paper presents the workflow to retrieve snow water equivalent from radar measurements for the future Canadian radar satellite mission, TSMM. The workflow is validated by using airborne radar data collected at Trail Valley Creek, Canada, during winter 2018–19. We detail important considerations to have in the context of an Earth Observation mission over a vast region such as Canada. The results show that it is possible to achieve the desired accuracy for TSMM, over an Arctic environment.
Haorui Sun, Yiwen Fang, Steven A. Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen
The Cryosphere, 19, 2017–2036, https://doi.org/10.5194/tc-19-2017-2025, https://doi.org/10.5194/tc-19-2017-2025, 2025
Short summary
Short summary
The European Space Agency's Snow Climate Change Initiative (Snow CCI) developed a high-quality snow cover extent and snow water equivalent (SWE) climate data record. However, gaps exist in complex terrain due to challenges in using passive microwave sensing and in situ measurements. This study presents a methodology to fill the mountain SWE gap using Snow CCI snow cover fraction within a Bayesian SWE reanalysis framework, with potential applications in untested regions and with other sensors.
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.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Julien Meloche, Benoit Montpetit, Nicolas R. Leroux, Richard Essery, Gabriel Hould Gosselin, and Philip Marsh
EGUsphere, https://doi.org/10.5194/egusphere-2025-1498, https://doi.org/10.5194/egusphere-2025-1498, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The impact of uncertainties in the simulation of snow density and SSA by the snow model Crocus (embedded within the Soil, Vegetation and Snow version 2 land surface model) on the simulation of snow backscatter (13.5 GHz) using the Snow Microwave Radiative Transfer model were quantified. The simulation of SSA was found to be a key model uncertainty. Underestimated SSA values lead to high errors in the simulation of snow backscatter, reduced by implementing a minimum SSA value (8.7 m2 kg-1).
Annett Bartsch, Rodrigue Tanguy, Helena Bergstedt, Clemens von Baeckmann, Hans Tømmervik, Marc Macias-Fauria, Juha Lemmentiynen, Kimmo Rautiainen, Chiara Gruber, and Bruce C. Forbes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1358, https://doi.org/10.5194/egusphere-2025-1358, 2025
Short summary
Short summary
We identified similarities between sea ice dynamics and conditions on land across the Arctic. Significant correlations north of 60°N was more common for snow water equivalent and permafrost ground temperature than for the vegetation parameters. Changes across all the different parameters could be specifically determined for Eastern Siberia. The results provide a baseline for future studies on common drivers of essential climate parameters and causative effects across the Arctic.
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.
Rainey Aberle, Ellyn Enderlin, Shad O'Neel, Caitlyn Florentine, Louis Sass, Adam Dickson, Hans-Peter Marshall, and Alejandro Flores
The Cryosphere, 19, 1675–1693, https://doi.org/10.5194/tc-19-1675-2025, https://doi.org/10.5194/tc-19-1675-2025, 2025
Short summary
Short summary
Tracking seasonal snow on glaciers is critical for understanding glacier health. Yet previous work has not directly compared machine learning algorithms for snow classification across satellite image products. To address this, we developed a new automated workflow for tracking seasonal snow on glaciers using several image products and machine learning models. Applying this method can help provide insights into glacier health, water resources, and the effects of climate change on snow cover.
Kimmo Rautiainen, Manu Holmberg, Juval Cohen, Arnaud Mialon, Mike Schwank, Juha Lemmetyinen, Antonio de la Fuente, and Yann Kerr
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-68, https://doi.org/10.5194/essd-2025-68, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
The SMOS Soil Freeze Thaw State product uses satellite data to monitor seasonal soil freezing and thawing globally, with a focus on high latitude regions. This is important for understanding greenhouse gas emissions, as frozen soil is associated with methane release. The product provides accurate data on key events such as the first day of soil freezing in autumn, helping scientists to study climate change, ecosystem dynamics and its impact on our planet.
Adrien Damseaux, Heidrun Matthes, Victoria R. Dutch, Leanne Wake, and Nick Rutter
The Cryosphere, 19, 1539–1558, https://doi.org/10.5194/tc-19-1539-2025, https://doi.org/10.5194/tc-19-1539-2025, 2025
Short summary
Short summary
Models often underestimate the role of snow cover in permafrost regions, leading to soil temperatures and permafrost dynamics inaccuracies. Through the use of a snow thermal conductivity scheme better adapted to this region, we mitigated soil temperature biases and permafrost extent overestimation within a land surface model. Our study sheds light on the importance of refining snow-related processes in models to enhance our understanding of permafrost dynamics in the context of climate change.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
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.
Haokui Xu, Leung Tsang, Julie Miller, Brooke Medley, and Jeol Johnson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2395, https://doi.org/10.5194/egusphere-2024-2395, 2025
Short summary
Short summary
This paper provides a physical model to analyze the brightness temperature time series over the firn aquifer in Greenland and Antarctica. The model can match the V and H SMAP brightness temperature time series well. This model provides a potential to study the aquifer liquid water content with radiometry.
Charlotte Crevier, Alexandre Langlois, Chris Derksen, and Alexandre Roy
EGUsphere, https://doi.org/10.5194/egusphere-2024-3580, https://doi.org/10.5194/egusphere-2024-3580, 2025
Short summary
Short summary
A multisensor C-Band SAR near-daily time series in an Arctic environment was developed to create a high-resolution freeze/thaw algorithm with an accuracy of 96 %. The FT detection was highly correlated to near-surface state as measured by soil temperature. Small but significant FT date differences were identified for different Arctic ecotypes, showing the spatial variability of freeze/thaw process in Arctic environment.
David Brodylo, Lauren V. Bosche, Ryan R. Busby, Elias J. Deeb, Thomas A. Douglas, and Juha Lemmetyinen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3936, https://doi.org/10.5194/egusphere-2024-3936, 2025
Short summary
Short summary
We combined field-based snow depth and snow water equivalent (SWE) measurements, remote sensing data, and machine learning to estimate snow depth and SWE over a 10 km2 local scale area in Sodankylä, Finland. Associations were found for snow depth and SWE with carbon- and mineral-based forest surface soils, alongside dry and wet peatbogs. This approach to upscale field-based snow depth and SWE measurements to a local scale can be used in regions that regularly experience snowfall.
Zachary Fair, Carrie Vuyovich, Thomas Neumann, Justin Pflug, David Shean, Ellyn M. Enderlin, Karina Zikan, Hannah Besso, Jessica Lundquist, Cesar Deschamps-Berger, and Désirée Treichler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3992, https://doi.org/10.5194/egusphere-2024-3992, 2025
Short summary
Short summary
Lidar is commonly used to measure snow over global water reservoirs. However, ground-based and airborne lidar surveys are expensive, so satellite-based methods are needed. In this review, we outline the latest research using satellite-based lidar to monitor snow. Best practices for lidar-based snow monitoring are given, as is a discussion on challenges in this field of research.
Lawrence Mudryk, Colleen Mortimer, Chris Derksen, Aleksandra Elias Chereque, and Paul Kushner
The Cryosphere, 19, 201–218, https://doi.org/10.5194/tc-19-201-2025, https://doi.org/10.5194/tc-19-201-2025, 2025
Short summary
Short summary
We evaluate and rank 23 different datasets on their ability to accurately estimate historical snow amounts. The evaluation uses new a set of surface snow measurements with improved spatial coverage, enabling evaluation across both mountainous and nonmountainous regions. Performance measures vary tremendously across the products: while most perform reasonably in nonmountainous regions, accurate representation of snow amounts in mountainous regions and of historical trends is much more variable.
Yanghai Yu, Yang Lei, Paul Siqueira, Xiaotong Liu, Denuo Gu, Anmin Fu, Yong Pang, Wenli Huang, and Jiancheng Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-596, https://doi.org/10.5194/essd-2024-596, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper presents a global-to-local method to improve forest height estimates by fusing InSAR and GEDI data. The large-scale ability was tested on open-access ALOS-1 data, where a two-fold solution is used to address temporal gap between GEDI and ALOS data. Produced products of 30 m gridded forest height mosaics for the northeastern U.S. and China show improved accuracy at 3–4 m/ha and 20 % enhancement over interpolated GEDI maps. The prototype is promising to fuse GEDI and future NISAR data.
Mochi Liao and Ana Barros
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-513, https://doi.org/10.5194/essd-2024-513, 2025
Manuscript not accepted for further review
Short summary
Short summary
This StageIV-IRC is the first rainfall dataset aiming to close the water budget for flood events, consistent with fundamental physics at basin scale, and achieving superior hydrological performance at fine scale (<1hr, <1km) in headwater basins. It shows greatly-enhanced, topography-aligned rainfall spatial variability, yielding a median KGE of 0.86, with flood timing errors <1hr. This dataset can be used in operational hydrology to improve precipitation forecasts, advancing flood forecasting.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamond Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
The Cryosphere, 18, 5685–5711, https://doi.org/10.5194/tc-18-5685-2024, https://doi.org/10.5194/tc-18-5685-2024, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of the snow model Crocus (embedded within the Soil, Vegetation, and Snow version 2 land surface model) and evaluated at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and specific surface area featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift, and increase viscosity in basal layers.
Colleen Mortimer, Lawrence Mudryk, Eunsang Cho, Chris Derksen, Mike Brady, and Carrie Vuyovich
The Cryosphere, 18, 5619–5639, https://doi.org/10.5194/tc-18-5619-2024, https://doi.org/10.5194/tc-18-5619-2024, 2024
Short summary
Short summary
Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of large-scale estimates from satellites and climate models. We compare two types of measurements – snow courses and airborne gamma SWE estimates – and analyze how measurement type impacts the accuracy assessment of gridded SWE products. We use this analysis to produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications.
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall
The Cryosphere, 18, 5407–5430, https://doi.org/10.5194/tc-18-5407-2024, https://doi.org/10.5194/tc-18-5407-2024, 2024
Short summary
Short summary
This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar-derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and nine automated snow monitoring stations.
Victoria Vanthof, Sylvain Ferrant, Romain Walcker, and Richard Kelly
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 565–570, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-565-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-565-2024, 2024
Aleksandra Elias Chereque, Paul J. Kushner, Lawrence Mudryk, Chris Derksen, and Colleen Mortimer
The Cryosphere, 18, 4955–4969, https://doi.org/10.5194/tc-18-4955-2024, https://doi.org/10.5194/tc-18-4955-2024, 2024
Short summary
Short summary
We look at three commonly used snow depth datasets that are produced through a combination of snow modelling and historical measurements (reanalysis). When compared with each other, these datasets have differences that arise for various reasons. We show that a simple snow model can be used to examine these inconsistencies and highlight issues. This method indicates that one of the complex datasets should be excluded from further studies.
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.
Jeffrey J. Welch and Richard E. J. Kelly
EGUsphere, https://doi.org/10.5194/egusphere-2024-2928, https://doi.org/10.5194/egusphere-2024-2928, 2024
Short summary
Short summary
Snow density plays an important role in natural and human systems but current methods for estimating snow density are limited, especially in the Arctic. This work presents a new method using satellite data to estimate snow density in remote areas. An experiment was conducted in the Canadian Arctic to evaluate this method and it appears to replicate density estimates from manual sampling well. With more work this method could be applied to estimate snow density across large areas of the Arctic.
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.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
Short summary
Short summary
This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
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.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
Short summary
Short summary
Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Johnny Rutherford, Nick Rutter, Leanne Wake, and Alex Cannon
EGUsphere, https://doi.org/10.5194/egusphere-2024-2445, https://doi.org/10.5194/egusphere-2024-2445, 2024
Short summary
Short summary
The Arctic winter is vulnerable to climate warming and ~1700 Gt of carbon stored in high latitude permafrost ecosystems is at risk of degradation in the future due to enhanced microbial activity. Poorly represented cold season processes, such as the simulation of snow thermal conductivity in Land Surface Models (LSMs), causes uncertainty in projected carbon emission simulations. Improved snow conductivity parameterization in CLM5.0 significantly increases predicted winter CO2 emissions to 2100.
Firoz Kanti Borah, Jonas-Fredrick Jans, Zhenming Huang, Leung Tsang, Hans Lievens, and Edward Kim
EGUsphere, https://doi.org/10.5194/egusphere-2024-1825, https://doi.org/10.5194/egusphere-2024-1825, 2024
Preprint archived
Short summary
Short summary
In this paper, we study radar data collected by Sentinel-1 over mountain regions of Alps. Using physical models of snow and soil surface scattering, we show the reasons for the high sensitivity of cross-polarized observations with snow depth. This accurate modelling for cross-pol using physical models can be then used to retrieve snow depth at for very deep snow at mountain regions using the cross-pol signal.
Tate G. Meehan, Ahmad Hojatimalekshah, Hans-Peter Marshall, Elias J. Deeb, Shad O'Neel, Daniel McGrath, Ryan W. Webb, Randall Bonnell, Mark S. Raleigh, Christopher Hiemstra, and Kelly Elder
The Cryosphere, 18, 3253–3276, https://doi.org/10.5194/tc-18-3253-2024, https://doi.org/10.5194/tc-18-3253-2024, 2024
Short summary
Short summary
Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. We combined high-spatial-resolution snow depth information with ground-based radar measurements to solve for snow density. Extrapolated density estimates over our study area resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation and were utilized in the calculation of SWE.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
Short summary
Short summary
To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
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.
Ian E. McDowell, Kaitlin M. Keegan, S. McKenzie Skiles, Christopher P. Donahue, Erich C. Osterberg, Robert L. Hawley, and Hans-Peter Marshall
The Cryosphere, 18, 1925–1946, https://doi.org/10.5194/tc-18-1925-2024, https://doi.org/10.5194/tc-18-1925-2024, 2024
Short summary
Short summary
Accurate knowledge of firn grain size is crucial for many ice sheet research applications. Unfortunately, collecting detailed measurements of firn grain size is difficult. We demonstrate that scanning firn cores with a near-infrared imager can quickly produce high-resolution maps of both grain size and ice layer distributions. We map grain size and ice layer stratigraphy in 14 firn cores from Greenland and document changes to grain size and ice layer content from the extreme melt summer of 2012.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
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.
Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros
The Cryosphere, 18, 747–773, https://doi.org/10.5194/tc-18-747-2024, https://doi.org/10.5194/tc-18-747-2024, 2024
Short summary
Short summary
Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
Short summary
Short summary
We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024, https://doi.org/10.5194/hess-28-631-2024, 2024
Short summary
Short summary
Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
Short summary
Short summary
The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024, https://doi.org/10.5194/tc-18-575-2024, 2024
Short summary
Short summary
We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
Short summary
Short summary
Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Carolina Voigt, Nick Rutter, Paul Mann, Jean-Daniel Sylvain, and Alexandre Roy
Biogeosciences, 20, 5087–5108, https://doi.org/10.5194/bg-20-5087-2023, https://doi.org/10.5194/bg-20-5087-2023, 2023
Short summary
Short summary
We present an analysis of soil CO2 emissions in boreal and tundra regions during the non-growing season. We show that when the soil is completely frozen, soil temperature is the main control on CO2 emissions. When the soil is around the freezing point, with a mix of liquid water and ice, the liquid water content is the main control on CO2 emissions. This study highlights that the vegetation–snow–soil interactions must be considered to understand soil CO2 emissions during the non-growing season.
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.
Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 27, 4039–4056, https://doi.org/10.5194/hess-27-4039-2023, https://doi.org/10.5194/hess-27-4039-2023, 2023
Short summary
Short summary
An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
Short summary
Short summary
Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
Luiz Bacelar, Arezoo ReifeeiNasab, Nathaniel Chaney, and Ana Barros
EGUsphere, https://doi.org/10.5194/egusphere-2023-2088, https://doi.org/10.5194/egusphere-2023-2088, 2023
Preprint archived
Short summary
Short summary
The study explores a computationally efficient probabilistic precipitation forecast approach to generate multiple flood scenarios. It reveals the limitations in predicting flash floods accurately and the need for advanced ensemble methodologies to combine different sources of precipitation forecasts. It highlights the scale-dependency of flood predictions at higher spatial resolutions, shedding light on the relationship between river hydraulics and flood propagation in the river network.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
Short summary
Short summary
As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
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.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Jennifer L. Baltzer, Christophe Kinnard, and Alexandre Roy
Biogeosciences, 20, 2941–2970, https://doi.org/10.5194/bg-20-2941-2023, https://doi.org/10.5194/bg-20-2941-2023, 2023
Short summary
Short summary
This review supports the integration of microwave spaceborne information into carbon cycle science for Arctic–boreal regions. The microwave data record spans multiple decades with frequent global observations of soil moisture and temperature, surface freeze–thaw cycles, vegetation water storage, snowpack properties, and land cover. This record holds substantial unexploited potential to better understand carbon cycle processes.
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.
Haokui Xu, Brooke Medley, Leung Tsang, Joel T. Johnson, Kenneth C. Jezek, Marco Brogioni, and Lars Kaleschke
The Cryosphere, 17, 2793–2809, https://doi.org/10.5194/tc-17-2793-2023, https://doi.org/10.5194/tc-17-2793-2023, 2023
Short summary
Short summary
The density profile of polar ice sheets is a major unknown in estimating the mass loss using lidar tomography methods. In this paper, we show that combing the active radar data and passive radiometer data can provide an estimation of density properties using the new model we implemented in this paper. The new model includes the short and long timescale variations in the firn and also the refrozen layers which are not included in the previous modeling work.
Jack Tarricone, Ryan W. Webb, Hans-Peter Marshall, Anne W. Nolin, and Franz J. Meyer
The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, https://doi.org/10.5194/tc-17-1997-2023, 2023
Short summary
Short summary
Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently measure the amount of water in mountain snowpacks from satellites. In this research, we test the ability of an experimental snow remote sensing technique from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found that the method worked better than expected for estimating important snowpack properties.
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.
E. Macorps, M. Jo, B. Osmanoglu, and R. A. Albayrak
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 175–182, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-175-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-175-2023, 2023
B. Osmanoglu, S. A. Huang, C. A. Jones, B. Scheuchl, A. Khazendar, J. Sauber, K. Tymofyeyeva, and M. J. Jo
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 225–232, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-225-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-225-2023, 2023
Chris Derksen and Lawrence Mudryk
The Cryosphere, 17, 1431–1443, https://doi.org/10.5194/tc-17-1431-2023, https://doi.org/10.5194/tc-17-1431-2023, 2023
Short summary
Short summary
We examine Arctic snow cover trends through the lens of climate assessments. We determine the sensitivity of change in snow cover extent to year-over-year increases in time series length, reference period, the use of a statistical methodology to improve inter-dataset agreement, version changes in snow products, and snow product ensemble size. By identifying the sensitivity to the range of choices available to investigators, we increase confidence in reported Arctic snow extent changes.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
Short summary
Short summary
Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Marco Brogioni, Mark J. Andrews, Stefano Urbini, Kenneth C. Jezek, Joel T. Johnson, Marion Leduc-Leballeur, Giovanni Macelloni, Stephen F. Ackley, Alexandra Bringer, Ludovic Brucker, Oguz Demir, Giacomo Fontanelli, Caglar Yardim, Lars Kaleschke, Francesco Montomoli, Leung Tsang, Silvia Becagli, and Massimo Frezzotti
The Cryosphere, 17, 255–278, https://doi.org/10.5194/tc-17-255-2023, https://doi.org/10.5194/tc-17-255-2023, 2023
Short summary
Short summary
In 2018 the first Antarctic campaign of UWBRAD was carried out. UWBRAD is a new radiometer able to collect microwave spectral signatures over 0.5–2 GHz, thus outperforming existing similar sensors. It allows us to probe thicker sea ice and ice sheet down to the bedrock. In this work we tried to assess the UWBRAD potentials for sea ice, glaciers, ice shelves and buried lakes. We also highlighted the wider range of information the spectral signature can provide to glaciological studies.
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.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
Short summary
Short summary
While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
Short summary
Short summary
Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Sara Modanesi, Christian Massari, Michel Bechtold, Hans Lievens, Angelica Tarpanelli, Luca Brocca, Luca Zappa, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 4685–4706, https://doi.org/10.5194/hess-26-4685-2022, https://doi.org/10.5194/hess-26-4685-2022, 2022
Short summary
Short summary
Given the crucial impact of irrigation practices on the water cycle, this study aims at estimating irrigation through the development of an innovative data assimilation system able to ingest high-resolution Sentinel-1 radar observations into the Noah-MP land surface model. The developed methodology has important implications for global water resource management and the comprehension of human impacts on the water cycle and identifies main challenges and outlooks for future research.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
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.
Wanshu Nie, Sujay V. Kumar, Kristi R. Arsenault, Christa D. Peters-Lidard, Iliana E. Mladenova, Karim Bergaoui, Abheera Hazra, Benjamin F. Zaitchik, Sarith P. Mahanama, Rachael McDonnell, David M. Mocko, and Mahdi Navari
Hydrol. Earth Syst. Sci., 26, 2365–2386, https://doi.org/10.5194/hess-26-2365-2022, https://doi.org/10.5194/hess-26-2365-2022, 2022
Short summary
Short summary
The MENA (Middle East and North Africa) region faces significant food and water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing data sets into an Earth system model to help build an effective drought monitoring system and support risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation conditions into the model as being one of the key processes for a successful application for the region.
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.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
Short summary
Short summary
Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
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.
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
Short summary
Short summary
To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, https://doi.org/10.5194/hess-25-6283-2021, 2021
Short summary
Short summary
Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Land surface models are not able to correctly simulate irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by irrigation. We equipped a land surface model with an observation operator able to transform Sentinel-1 backscatter observations into realistic vegetation and soil states via data assimilation.
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.
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.
Bin Cheng, Yubing Cheng, Timo Vihma, Anna Kontu, Fei Zheng, Juha Lemmetyinen, Yubao Qiu, and Jouni Pulliainen
Earth Syst. Sci. Data, 13, 3967–3978, https://doi.org/10.5194/essd-13-3967-2021, https://doi.org/10.5194/essd-13-3967-2021, 2021
Short summary
Short summary
Climate change strongly impacts the Arctic, with clear signs of higher air temperature and more precipitation. A sustainable observation programme has been carried out in Lake Orajärvi in Sodankylä, Finland. The high-quality air–snow–ice–water temperature profiles have been measured every winter since 2009. The data can be used to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and parameterization of snow-to-ice transformation in snow/ice models.
Pinja Venäläinen, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala
The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, https://doi.org/10.5194/tc-15-2969-2021, 2021
Short summary
Short summary
Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
Short summary
Short summary
We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
David A. Lilien, Daniel Steinhage, Drew Taylor, Frédéric Parrenin, Catherine Ritz, Robert Mulvaney, Carlos Martín, Jie-Bang Yan, Charles O'Neill, Massimo Frezzotti, Heinrich Miller, Prasad Gogineni, Dorthe Dahl-Jensen, and Olaf Eisen
The Cryosphere, 15, 1881–1888, https://doi.org/10.5194/tc-15-1881-2021, https://doi.org/10.5194/tc-15-1881-2021, 2021
Short summary
Short summary
We collected radar data between EDC, an ice core spanning ~800 000 years, and BELDC, the site chosen for a new
oldest icecore at nearby Little Dome C. These data allow us to identify 50 % older internal horizons than previously traced in the area. We fit a model to the ages of those horizons at BELDC to determine the age of deep ice there. We find that there is likely to be 1.5 Myr old ice ~265 m above the bed, with sufficient resolution to preserve desired climatic information.
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.
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.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
Short summary
Short summary
High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
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.
Joshua King, Stephen Howell, Mike Brady, Peter Toose, Chris Derksen, Christian Haas, and Justin Beckers
The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, https://doi.org/10.5194/tc-14-4323-2020, 2020
Short summary
Short summary
Physical measurements of snow on sea ice are sparse, making it difficulty to evaluate satellite estimates or model representations. Here, we introduce new measurements of snow properties on sea ice to better understand variability at distances less than 200 m. Our work shows that similarities in the snow structure are found at longer distances on younger ice than older ice.
Maxim Lamare, Marie Dumont, Ghislain Picard, Fanny Larue, François Tuzet, Clément Delcourt, and Laurent Arnaud
The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, https://doi.org/10.5194/tc-14-3995-2020, 2020
Short summary
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.
Paul Donchenko, Joshua King, and Richard Kelly
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-283, https://doi.org/10.5194/tc-2020-283, 2020
Publication in TC not foreseen
Short summary
Short summary
Estimating Arctic sea ice surface elevation from the CryoSat-2 instrument may not fully compensate for the incomplete penetration of radar through the snow cover and overestimate the ice thickness. This study investigates the accuracy of the ice surface measurement and how it is affected by the properties snow and ice properties. It was found that deep or salty snow, and rough ice can make the surface appear higher, but including these properties in the calculation may improve the estimate.
Miguel A. Aguayo, Alejandro N. Flores, James P. McNamara, Hans-Peter Marshall, and Jodi Mead
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-451, https://doi.org/10.5194/hess-2020-451, 2020
Manuscript not accepted for further review
Anne Sophie Daloz, Marian Mateling, Tristan L'Ecuyer, Mark Kulie, Norm B. Wood, Mikael Durand, Melissa Wrzesien, Camilla W. Stjern, and Ashok P. Dimri
The Cryosphere, 14, 3195–3207, https://doi.org/10.5194/tc-14-3195-2020, https://doi.org/10.5194/tc-14-3195-2020, 2020
Short summary
Short summary
The total of snow that falls globally is a critical factor governing freshwater availability. To better understand how this resource is impacted by climate change, we need to know how reliable the current observational datasets for snow are. Here, we compare five datasets looking at the snow falling over the mountains versus the other continents. We show that there is a large consensus when looking at fractional contributions but strong dissimilarities when comparing magnitudes.
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.
Julie Z. Miller, David G. Long, Kenneth C. Jezek, Joel T. Johnson, Mary J. Brodzik, Christopher A. Shuman, Lora S. Koenig, and Ted A. Scambos
The Cryosphere, 14, 2809–2817, https://doi.org/10.5194/tc-14-2809-2020, https://doi.org/10.5194/tc-14-2809-2020, 2020
Cited articles
Abaza, M., Fortin, V., Gaborit, E., Belair, S., and Garnaud, C.: Assessing
32-Day hydrological ensemble forecasts in the Lake Champlain–Richelieu
River watershed, J. Hydrol. Eng., 25, 04020045,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001983, 2020.
Anttila, K., Manninen, T., Karjalainen, T., Lahtinen, P., Riihelä, A.,
and Siljamo, N.: The temporal and spatial variability in submeter scale
surface roughness of seasonal snow in Sodankylä Finnish Lapland in
2009–2010, J. Geophys. Res.-Atmos., 119, 9236–9252,
https://doi.org/10.1002/2014jd021597, 2014.
Arnold, E., Leuschen, C., Rodriguez-Morales, F., Li, J., Paden, J., Hale, R., and Keshmiri, S.:
CReSIS airborne radars and platforms for ice and snow sounding,
Ann. Glaciol., 61, 1–10, https://doi.org/10.1017/aog.2019.37, 2019.
Attema, E. P. W. and Ulaby, F. T.: Vegetation modeled as a water cloud, Radio
Sci., 13, 357–364, 1978.
Bader, H., Haefeli, R., Bucher, E., Neher, J., Eckel, C., and Thams, C.: Der
Schnee und seine Metamorphose, Beitr. Geol. Schweiz, Geotechn.
Ser. Hydrol, 3, 1–313, 1939.
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.
Bateni, S. M., Huang, C., Margulis, S. A., Podest, E., and McDonald, K.:
Feasibility of characterizing snowpack and the freeze – thaw state of
underlying soil using multifrequency active/passive microwave data, IEEE
T. Geosci. Remote, 51, 4085–4102, 2013.
Bateni, S. M., Margulis, S. A., Podest, E., and McDonald, K. C.: Characterizing
Snowpack and the Freeze–Thaw State of Underlying Soil via Assimilation of
Multifrequency Passive/Active Microwave Data: A Case Study (NASA CLPX 2003),
IEEE T. Geosci. Remote, 53, 173–189, https://doi.org/10.1109/TGRS.2014.2320264, 2015.
Bindlish, R. and Barros, A. P.: Parameterization of vegetation backscatter in radar-based, soil moisture estimation, Remote Sens. Environ., 76, 130–137, https://doi.org/10.1016/S0034-4257(00)00200-5, 2001.
Bindlish, R. and Barros, A. P.: Sub-Pixel Variability of
Remotely-Sensed Soil Moisture – An Intercomparison Study of SAR and ESTAR, IEEE T. Geosci. Remote, 40, 326–337, https://doi.org/10.1109/36.992792, 2002.
Biskaborn, B. K., Smith, S. L., Noetzli, J., et al.: Permafrost is warming at a
global scale, Nat. Commun., 10, 264,
https://doi.org/10.1038/s41467-018-08240-4, 2019.
Bourassa, M. A. and McBeth Ford, K.: Uncertainty in scatterometer-derived
vorticity, J. Atmos. Ocean. Tech., 27, 594–603,
2010.
Brun, E.: Investigation on wet-snow metamorphism in respect of liquid-water
content, Ann. Glaciol., 13, 22–26, 1989.
Brun, E., David, P., Sudul, M., and Brunot, G.: A numerical model to
simulate snow-cover stratigraphy for operational avalanche forecasting,
J. Glaciol., 38, 13–22, 1992.
Cao, Y. and Barros, A. P.: Weather-Dependent Nonlinear Microwave behavior of
Seasonal High-Elevation Snowpacks, Remote Sensing, 12, 3422, https://doi.org/10.3390/rs12203422, 2020.
Carrera, M. L., Bilodeau, B., Bélair, S., Abrahamowicz, M., Russell, A.,
and Wang, X.: Assimilation of passive L-band microwave brightness
temperatures in the Canadian land data assimilation system: Impacts on
short-range warm season numerical weather prediction, J.
Hydrometeorol., 20, 1053–1079, 2019.
Chabot, M., Lindsay, J., Rowlandson, T., and Berg, A.: Comparing the Use of
Terrestrial LiDAR Scanners and Pin Profilers for Deriving Agricultural
Roughness Statistics, Can. J. Remote Sens., 44, 153–168, https://doi.org/10.1080/07038992.2018.1461559, 2018.
Chang, T. C., Gloersen, P., Schmugge, T., Wilheit, T. T., and Zwally, H. J.:
Microwave emission from snow and glacier ice, J. Glaciol., 16,
23–39, 1976.
Chang, W., Tan, S., Lemmetyinen, J., Tsang, L., Xu, X., and Yueh, S. H.:
Dense media radiative transfer applied to SnowScat and SnowSAR, IEEE J. Sel. Top. Appl., 7,
3811–3825, 2014.
Chang, W., Ding, K. H., Tsang, L., and Xu, X.: Microwave scattering
and medium characterization for terrestrial snow with QCA–Mie and
bicontinuous models: Comparison studies, IEEE T. Geosci.
Remote, 54, 3637–3648, 2016.
Chen, K. S., Wu, T. D., Tsang, L., Li, Q., Shi, J., and Fung, A. K.:
Emission of rough surfaces calculated by the integral equation method with
comparison to three-dimensional moment method simulations, IEEE T.
Geosci. Remote, 41, 90–101, 2003.
Cline, D., Yueh, S., Chapman, B., Stankov, B., Gasiewski, A., Masters, D., Elder, K. J., Kelly, R., Painter, T. H., Miller, S., Katzberg, S., and Mahrt, L.: NASA cold land processes experiment (CLPX 2002/03):
Airborne remote sensing, J. Hydrometeorol., 10, 338–346, 2009.
Coccia, A., Trampuz, C., Imbembo, E., and Meta, A.: First results of
snowSAR, the new X-and Ku-Band polarimetric airborne SAR sensor supporting
the CoReH2O mission, in: Workshop on Advanced RF Sensors and Remote Sensing
Instruments, 2011.
Cohen, J., Lemmetyinen, J., Pulliainen, J., Heinilä, K., Montomoli, F.,
Seppänen, J., and Hallikainen, M. T.: The effect of boreal forest canopy
in satellite snow mapping – A multisensor analysis, IEEE T.
Geosci. Remote, 53, 6593–6607, 2015.
Colbeck, S. C.: An overview of seasonal snow metamorphism, Rev. Geophys.,
20, 45– 61, https://doi.org/10.1029/RG020i001p00045, 1982.
Cui, Y., Xiong, C., Lemmetyinen, J., Shi, J., Jiang, L., Peng, B., Li, H., Zhao, T., Ji, D., and Hu, T.:
Estimating snow water equivalent with backscattering at X and Ku band based
on absorption loss, Remote Sensing, 8, 505, https://doi.org/10.3390/RS8060505, 2016.
Dall, J.: InSAR Elevation bias caused by penetration into uniform volumes,
IEEE T. Geosci. Remote, 45, 2319–2324, 2007.
Deeb, E. J., Forster, R. R., and Kane, D. L.: Monitoring snowpack evolution
using interferometric synthetic aperture radar on the North Slope of Alaska,
USA, Int. J. Rem. Sens., 32, 3985–4003, 2011.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow
depth: a review, J. Glaciol., 59, 467–479, 2013.
De Lannoy, G., Reichle, R., Houser, P., Arsenault, K., Verhoest, N., and
Pauwels, V.: Satellite-scale snow water equivalent assimilation into a
high-resolution land surface model, J. Hydrometeorol., 11,
352–369, 2010.
Denoth, A.: An Electronic Device for Long-Term Snow Wetness Recording,
Ann. Glaciol., 19,
104–106, https://doi.org/10.3189/S0260305500011058, 1994.
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.
Derksen, C., King, J., Belair, S., Garnaud, C., Vionnet, V., Fortin, V., Lemmetyinen, J., Crevier, Y., Plourde, P., Lawrence, B., van Mierlo, H., Burbidge, G., and Siqueira, P.: Development of the Terrestrial Snow Mass
Mission, in: International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 11–16 July 2021, https://doi.org/10.1109/IGARSS47720.2021.9553496, 2021.
Ding, K. H., Xu, X., and Tsang, L.: Electromagnetic scattering by
bicontinuous random microstructures with discrete permittivities, IEEE
T. Geosci. Remote, 48, 3139–3151, 2010.
Domine, F., Albert, M., Huthwelker, T., Jacobi, H.-W., Kokhanovsky, A. A., Lehning, M., Picard, G., and Simpson, W. R.: Snow physics as relevant to snow photochemistry, Atmos. Chem. Phys., 8, 171–208, https://doi.org/10.5194/acp-8-171-2008, 2008.
Drinkwater, M. R., Long, D. G., and Bingham, A. W.: Greenland snow
accumulation estimates from satellite radar scatterometer data, J. Geophys. Res.-Atmos., 106, 33935–33950, 2001.
Elfouhaily, T. M. and Johnson, J. T.: A new model for rough surface
scattering, IEEE T. Geosci. Remote, 45,
2300–2308, 2007.
ESA: Report for Mission Selection: CoReH2O, ESA SP-1324/2, 3 volume series,
European Space Agency, Noordwijk, the Netherlands, https://earth.esa.int/eogateway/documents/20142/37627 (last access: 3 August 2022), 2012.
Fassnacht, S. R., Stednick, J. D., Deems, J. S., and Corrao, M. V.: Metrics for assessing snow surface roughness from digital imagery, Water Resour. Res., 45, W00D31, https://doi.org/10.1029/2008WR006986, 2009.
Ferrazzoli, P. and Guerriero, L.: Radar sensitivity to tree geometry and
woody volume: A model analysis, IEEE T. Geosci. Remote, 33, 360–371, 1995.
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., McClung,
D. M., Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The
International Classification for Seasonal Snow on the Ground, IHP-VII
Technical Documents in Hydrology No.83, IACS Contribution No.1, UNESCO-IHP,
Paris, https://unesdoc.unesco.org/ark:/48223/pf0000186462 (last access: 10 August 2022), 2009.
Gallet, J.-C., Domine, F., Zender, C. S., and Picard, G.: Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm, The Cryosphere, 3, 167–182, https://doi.org/10.5194/tc-3-167-2009, 2009.
Ferrazzoli, P., Guerriero, L., and Schiavon, G.: Experimental and model investigation on radar classification capability, IEEE T. Geosci. Remote, 37, 960–968, https://doi.org/10.1109/36.752214, 1999.
Frolking, S., Milliman, T., McDonald, K., Kimball, J., Zhao, M., and
Fahnestock, M.: Evaluation of the SeaWinds scatterometer for regional
monitoring of vegetation phenology, J. Geophys. Res.-Atmos., 111, D17302, https://doi.org/10.1029/2005JD006588, 2006.
Fung, A. K., Chen, K. S., and Chen, K. S.: Microwave Scattering and Emission Models for Users, Artech House, https://books.google.com/books?id=Dd2StgAACAAJ (last access: 10 August 2022), 2010.
Grünewald, T., Schirmer, M., Mott, R., and Lehning, M.: Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment, The Cryosphere, 4, 215–225, https://doi.org/10.5194/tc-4-215-2010, 2010.
Gu, W., Tsang, L., Colliander, A., and Yueh, S.: Wave Propagation in
Vegetation Field Using a Hybrid Method, IEEE T. Antenn.
Propag., 69, 6752–6761, 2021.
Gu, W., Tsang, L., Colliander, A., and Yueh, S.: Multifrequency Full-Wave
Simulations of Vegetation Using a Hybrid Method, IEEE T.
Microw. Theory, 70, 275–285, 2022.
Gubler, H. and Hiller, M.: The use of microwave FMCW radar in snow and
avalanche research, Cold Reg. Sci. Technol., 9, 109–119, 1984.
Guneriussen, T., Hogda, K. A., Johnson, H., and Lauknes, I.: InSAR for
estimating changes in snow water equivalent of dry snow, IEEE T. Geosci.
Remote, 39, 2101–2108, 2001.
Hall, D. K., Chang, A. T. C., and Foster, J. L.: Detection of the depth-hoar
layer in the snow-pack of the Arctic coastal plain of Alaska, USA, using
satellite data, J. Glaciol., 32, 87–94, 1986.
Hallikainen, M. T., Ulaby, F. T., and Van Deventer, T. E.: Extinction
behavior of dry snow in the 18-to 90-GHz range, IEEE T.
Geosci. Remote, GE-25, 737–745, 1987.
Hallikainen, M. T., Halme, P., Takala, M., and Pulliainen, J.: Combined
active and passive microwave remote sensing of snow in Finland, in: 2003 IEEE International Geoscience and Remote Sensing Symposium,
Proceedings, IEEE Cat. No. 03CH37477, Vol. 2, 830–832, https://doi.org/10.1109/IGARSS.2003.1293934, 2003.
Huang, S., Tsang, L., Njoku, E. G., and Chan, K. S.: Backscattering
coefficients, coherent reflectivities, and emissivities of randomly rough
soil surfaces at L-band for SMAP applications based on numerical solutions
of Maxwell equations in three-dimensional simulations, IEEE T.
Geosci. Remote, 48, 2557–2568, 2010.
Huang, S. and Tsang, L.: Electromagnetic scattering of randomly rough soil
surfaces based on numerical solutions of Maxwell equations in
three-dimensional simulations using a hybrid UV/PBTG/SMCG method, IEEE
T. Geosci. Remote, 50, 4025–4035, 2012.
Huang, C., Margulis, S. A., Durand, M. T., and Musselman, K. N.:
Assessment of Snow Grain-Size Model and Stratigraphy Representation Impacts
on Snow Radiance Assimilation: Forward Modeling Evaluation, IEEE
T. Geosci. Remote, 50, 4551–4564,
https://doi.org/10.1109/tgrs.2012.2192480, 2012.
Huang, H., Tsang, L., Njoku, E. G., Colliander, A., Liao, T. H., and Ding,
K. H.: Propagation and scattering by a layer of randomly distributed
dielectric cylinders using Monte Carlo simulations of 3D Maxwell equations
with applications in microwave interactions with vegetation, IEEE Access, 5,
11985–12003, 2017.
Huang, H., Tsang, L., Colliander, A., and Yueh, S. H.: Propagation of Waves
in Randomly Distributed Cylinders Using Three-Dimensional Vector Cylindrical
Wave Expansions in Foldy–Lax Equations, IEEE Journal on Multiscale and
Multiphysics Computational Techniques, 4, 214–226, 2019.
Ishimaru, A.: Wave propagation and scattering in random media, vol. 2, Academic Press, New York, 336–393, ISBN 10 0123747023, ISBN 13 9780123747020, 1978.
Johnson, J. T., Warnick, K. F., and Xu, P.: On the geometrical optics
(Hagfors' law) and physical optics approximations for scattering from
exponentially correlated surfaces, IEEE T. Geosci.
Remote, 45, 2619–2629, 2007.
Jordan, R. E.: A One-dimensional temperature model for a snow cover: technical documentation for SNTHERM.89, No. CRREL-SR-91-16, Cold Regions Research
and Engineering Lab Hanover, NH, http://hdl.handle.net/11681/11677 (last access: 10 August 2022), 1991.
Karam M. A., Fung, A. K., Lang, R. H., and Chauhan, N. S.: A microwave
scattering model for layered vegetation, IEEE T. Geosci. Remote, 30, 767–784, 1992.
Kelly, R. E. J. and Chang, A. T. C.: Development of a Passive Microwave Global
Snow Depth Retrieval Algorithm for Special Microwave Imager (SSM/I) and
Advanced Microwave Scanning Radiometer- EOS (AMSR-E) data, Radio Sci.,
38, 8076, https://doi.org/10.1029/2002RS002648, 2003.
Kerbrat, M., Pinzer, B., Huthwelker, T., Gäggeler, H. W., Ammann, M., and Schneebeli, M.: Measuring the specific surface area of snow with X-ray tomography and gas adsorption: comparison and implications for surface smoothness, Atmos. Chem. Phys., 8, 1261–1275, https://doi.org/10.5194/acp-8-1261-2008, 2008.
Kim, S. B., Tsang, L., Johnson, J. T., Huang, S., van Zyl, J. J., and Njoku,
E. G.: Soil moisture retrieval using time-series radar observations over
bare surfaces, IEEE T. Geosci. Remote, 50,
1853–1863, 2012.
Kim, S. B., Moghaddam, M., Tsang, L., Burgin, M., Xu, X., and Njoku, E. G.:
Models of L-band radar backscattering coefficients over global terrain for
soil moisture retrieval, IEEE T. Geosci. Remote,
52, 1381–1396, 2014.
Kim S.-B., Van Zyl, J. J., Johnson, J. T., Moghaddam, M., Tsang, L., Colliander, A., Dunbar, R. S., Jackson, T. J., Jaruwatanadilok, S., West, R., Berg, A., Caldwell, T., Cosh, M. H., Goodrich, D. C., Livingston, S., Lopez-Baeza, E., Rowlandson, T., Thibeault, M., Walker, J. P., Entekhabi, D., Njoku, E. G.,O'Neill, P. E., and Yueh, S. H.: Surface Soil Moisture Retrieval Using the L-Band
Synthetic Aperture Radar Onboard the Soil Moisture Active–Passive Satellite
and Evaluation at Core Validation Sites, IEEE T. Geosci. Remote, 55, 1897–1914, 2017.
Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., Durand, M., Barros, A., Kim, E. J., Forman, B. A., Gutmann, E. D., Wrzesien, M. L., Garnaud, C., Sandells, M., Marshall, H.-P., Cristea, N., Pflug, J. M., Johnston, J., Cao, Y., Mocko, D., and Wang, S.: Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling, The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, 2021.
Kinar, N. J. and Pomeroy, J. W.: Measurement of the physical properties of
the snowpack, Rev. Geophys., 53, 481–544,
https://doi.org/10.1002/2015RG000481, 2015.
King, J., Kelly, R., Kasurak, A., Duguay, C., Gunn, G., Rutter, N., Watts, T., and
Derksen, C.: Spatio-temporal influence of tundra snow properties on Ku-band
(17.2 GHz) backscatter, J. Glaciol., 61, 267–279, 2015.
King, J., Derksen, C., Toose, P., Langlois, A., Larsen, C., Lemmetyinen, J., Marsh, P., Montpetit, B., Roy, A., Rutter, N., and Sturm, M.: The influence of snow microstructure on dual-frequency radar
measurements in a tundra environment, Remote Sens. Environ., 215,
242–254, https://doi.org/10.1016/j.rse.2018.05.028, 2018.
King, J., Derksen, C., Toose, P., Montpetit, B., and Siqueira, P.: Seasonal
Ku-band (13.5 GHz) SAR measurements in a snow-covered tundra basin, The ASAR
workshop 2019, Montreal, Canada, 1–3 October 2019.
King, J. M., Kelly, R., Kasurak, A., Duguay, C., Gunn, G., and Mead, J. B.:
UW-Scat: A ground-based dual-frequency scatterometer for observation of snow
properties, IEEE Geosci. Remote S., 10, 528–532,
2012.
Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M.,
Schweizer, J., and Mauser, W.: Retrieval of Snow Water Equivalent, Liquid
Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal
Attenuation and Time Delay, Water Resour. Res., 55, 4465–4487, https://doi.org/10.1029/2018WR024431, 2019.
Kontu, A., Lemmetyinen, J., Vehviläinen, J., Leppänen, L., and
Pulliainen, J.: Coupling SNOWPACK-modeled grain size parameters with the HUT
snow emission model, Remote Sens. Environ., 194, 33–47, 2017.
Kugler, F., Schulze, D., Hajnsek, I., Pretzsch, H., and Papathanassiou, K.:
TanDEM-X Pol-InSAR performance for forest height estimation, IEEE T.
Geosci. Remote, 52, 6404–6421, 2014.
Kugler, F., Lee, S. K., Hajnsek, I., and Papathanassiou, K. P.:
Forest height estimation by means of Pol-InSAR data inversion: The role of
the vertical wavenumber, IEEE T. Geosci. Remote, 53, 5294–5311, 2015.
Kurt, S. and Tavli, B.: Path-Loss Modeling for Wireless Sensor Networks: A
review of models and comparative evaluations, IEEE Antenn. Propag.
M., 59, 18–37, 2017.
Kwok, R.: Near zero replenishment of the Arctic multiyear sea ice cover at
the end of 2005 summer, Geophys. Res. Lett., 34, L05501, https://doi.org/10.1029/2006GL028737, 2007.
Lang, R. H. and Sighu, J. S.: Electromagnetic Backscattering from a Layer of Vegetation: A Discrete Approach, IEEE T. Geosci. Remote, GE-21, 62–71, https://doi.org/10.1109/TGRS.1983.350531, 1983.
Langlois, A., Royer, A., Derksen, C., Montpetit, B., Dupont, F., and
Goïta, K.: Coupling the snow thermodynamic model SNOWPACK with the
microwave emission model of layered snowpacks for subarctic and arctic snow
water equivalent retrievals, Water Resour. Res., 48, W12524, https://doi.org/10.1029/2012WR012133, 2012.
Larue, F., Royer, A., De Sève, D., Roy, A., and Cosme, E.: Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada, Hydrol. Earth Syst. Sci., 22, 5711–5734, https://doi.org/10.5194/hess-22-5711-2018, 2018.
Legagneux, L., Cabanes, A., and Dominé, F.: Measurement of the specific
surface area of 176 snow samples using methane adsorption at 77 K, J. Geophys. Res.-Atmos., 107, 4335, https://doi.org/10.1029/2001JD001016, 2002.
Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: A
physical SNOWPACK model for the Swiss avalanche warning: Part II. Snow
microstructure, Cold Reg. Sci. Technol., 35, 147–167, 2002.
Lei, Y., Siqueira, P., and Treuhaft, R.: A dense medium electromagnetic
scattering model for the InSAR correlation of snow, Radio Sci., 51,
461–480, 2016.
Leinss, S., Löwe, H., Proksch, M., and Kontu, A.: Modeling the evolution of the structural anisotropy of snow, The Cryosphere, 14, 51–75, https://doi.org/10.5194/tc-14-51-2020, 2020.
Lemmetyinen, J., Pulliainen, J., Rees, A., Kontu, A., Qiu, Y., and Derksen,
C.: Multiple-layer adaptation of HUT snow emission model: Comparison with
experimental data, IEEE T. Geosci. Remote,
48, 2781–2794, 2010.
Lemmetyinen, J., Pulliainen, J., Kontu, A., Wiesmann, A., Mätzler, C.,
Rott, H., Volgmeier, K., Nagler, T., Meta, A., Coccia, A., Schneebeli, M., Proksch, M., Davidson, M., Schuettemeyer, D., Lin, C.-C., and Kern, M.: Observations of seasonal snow cover at X and Ku
bands during the NoSREx campaign, EUSAR 2014, 10th European
Conference on Synthetic Aperture Radar, Berlin
Germany, 3–5 June 2014.
Lemmetyinen, J., Kontu, A., Pulliainen, J., Vehviläinen, J., Rautiainen, K., Wiesmann, A., Mätzler, C., Werner, C., Rott, H., Nagler, T., Schneebeli, M., Proksch, M., Schüttemeyer, D., Kern, M., and Davidson, M. W. J.: Nordic Snow Radar Experiment, Geosci. Instrum. Method. Data Syst., 5, 403–415, https://doi.org/10.5194/gi-5-403-2016, 2016.
Lemmetyinen, J., Derksen, C., Rott, H., Macelloni, G., King, J., Schneebeli,
M., Wiesmann, A., Leppänen, L., Kontu, A., and Pulliainen, J.: Retrieval of effective correlation length and
snow water equivalent from radar and passive microwave measurements, Remote
Sensing, 10, 170, https://doi.org/10.3390/rs10020170, 2018.
Lemmetyinen, J., Cohen, J., Kontu, A., Vehviläinen, J., Hannula, H.-R., Leppänen, L., Merkouriadi, I., Scheiblauer, S., Rott, H., Nagler, T., Ripper, E., Elder, K., Marshall, H.-P., Fromm, R., Adams, M. S., Derksen, C., King, J., Toose, P., Siliis, A., Rutter, N., Meta, A., and Coccia, A.: Airborne SnowSAR data at X- and Ku- bands over boreal forest, alpine and tundra snow cover, PANGAEA [data set], https://doi.pangaea.de/10.1594/PANGAEA.933255, DOI registration in progress, 2021.
Leppänen, M., Korpi, A., Yli-Pirilä, P., Lehto, M., Wolff, H.,
Kosma, V. M., Alenius, H., and Pasanen, P.: Negligible respiratory irritation and
inflammation potency of pigmentary TiO2 in mice, Inhal. Toxicol.,
27, 378–386, 2015.
Li, D., Wrzesien, M. L., Durand, M., Adam, J., and Lettenmaier, D. P.: How
much runoff originates as snow in the western United States, and how will
that change in the future?, Geophys. Res. Lett., 44, 6163–6172,
https://doi.org/10.1002/2017gl073551, 2017.
Li, Q., Kelly, R., Lemmetyinen, J., and Pan, J.: Simulating the influence of
temperature on microwave transmissivity of trees during winter observed by
spaceborne microwave radiometery, IEEE J. Sel. Top. Appl., 13, 4816–4824, 2020.
Liang, D., Xu, X., Tsang, L., Andreadis, K. M., and Josberger, E. G.: The
effects of layers in dry snow on its passive microwave emissions using dense
media radiative transfer theory based on the quasicrystalline approximation
(QCA/DMRT), IEEE T. Geosci. Remote, 46,
3663–3671, 2008.
Liao, T.-H., Kim, S.-B., Tan, S., Tsang, L., Su, C., and Jackson, T. J.:
Multiple Scattering Effects With Cyclical Correction in Active
Remote Sensing of Vegetated Surface Using Vector Radiative Transfer
Theory, IEEE J. Sel. Top. Appl., 9, 1414–1429, https://doi.org/10.1109/jstars.2015.2505638, 2016.
Lievens, H., Demuzere, M., Marshall, H. P., Reichle, R. H., Brucker, L.,
Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W., Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schöber, J., and De Lannoy, G. J. M.: Snow depth variability in
the Northern Hemisphere mountains observed from space, Nat.
Commun., 10, 4629, https://doi.org/10.1038/s41467-019-12566-y,
2019.
Lievens, H., Brangers, I., Marshall, H.-P., Jonas, T., Olefs, M., and De Lannoy, G.: Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps, The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, 2022.
Lindsay, R., Wensnahan,
M., Schweiger, A., and Zhang, J.: Evaluation of seven different atmospheric
reanalysis products in the arctic, J. Climate, 27, 2588–2606,
https://doi.org/10.1175/JCLI-D-13-00014.1, 2014.
Ling, H., Chou, R. C., and Lee, S. W.: Shooting and bouncing rays:
Calculating the RCS of an arbitrarily shaped cavity, IEEE T.
Antenn. Propag., 37, 194–205, 1989.
Liston, G. E. and Sturm, M.: A snow-transport model for complex terrain,
J. Glaciol., 44, 498–516, 1998.
Long, D. G. and Brodzik, M. J.: Optimum Image Formation for
Spaceborne Microwave Radiometer Products, IEEE T. Geosci. Remote, 54, 2763–2779,
https://doi.org/10.1109/tgrs.2015.2505677, 2016.
López-Moreno, J. I., Revuelto, J., Gilaberte, M., Morán-Tejeda, E.,
Pons, M., Jover, E., Esteban, P., García, C., and Pomeroy, J. W.: The effect of slope aspect on the
response of snowpack to climate warming in the Pyrenees, Theor. Appl. Climatol., 117, 207–219,
https://doi.org/10.1007/s00704-013-0991-0, 2014.
Löwe, H. and Picard, G.: Microwave scattering coefficient of snow in MEMLS and DMRT-ML revisited: the relevance of sticky hard spheres and tomography-based estimates of stickiness, The Cryosphere, 9, 2101–2117, https://doi.org/10.5194/tc-9-2101-2015, 2015.
Löwe, H., Riche, F., and Schneebeli, M.: A general treatment of snow microstructure exemplified by an improved relation for thermal conductivity, The Cryosphere, 7, 1473–1480, https://doi.org/10.5194/tc-7-1473-2013, 2013.
Lundberg, A., Thunehed, H., and Bergström, J.: Impulse radar snow
surveys – influence of snow density, Nordic Hydrol., 31, 1–14, https://doi.org/10.2166/nh.2000.0001, 2000.
Lundquist, J., Hughes, M., Gutmann, E., and Kpnick, S.: Our skill in
modleing mountain rain and snmow is bypassing the skill of our observational
netwrks, B. Am. Meteorol. Soc., 100, 2473–2490,
https://doi.org/10.1175/BAMS-D-19-0001.1, 2019.
Lundy, C. C., Edens, M. Q., and Brown, R. L.: Measurement of snow density
and microstructure using computed tomography, J. Glaciol., 48,
312–316, https://doi.org/10.3189/172756502781831485, 2002.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Mortimer, C., Derksen, C., Mudryk, L., Moisander, M., Hiltunen, M., Smolander, T., Ikonen, J., Cohen, J., Salminen, M., Norberg, J., Veijola, K., and Venäläinen, P.: GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset, Scientific Data, 8, 163, https://doi.org/10.1038/s41597-021-00939-2, 2021.
Manickam, S. and Barros, A. P.: Parsing Synthetic Aperture Radar
Measurements of Snow in Complex Terrain: Scaling Behavior and Sensitivity to
Snow Wetness and Landcover, Remote Sensing, 12, 483, https://doi.org/10.3390/rs12030483, 2020.
Marsh, P., Bartlett, P., MacKay, M., Pohl, S., and Lantz, T.: Snowmelt
energetics at a shrub tundra site in the western Canadian Arctic,
Hydrol. Process., 24, 3603–3620, 2010.
Marshall, H. P. and Koh, G.: FMCW radars for snow research, Cold
Reg. Sci. Technol., 52, 118–131, 2008.
Marshall, H. P., Deeb, E., Forster, R., Vuyovich, C., Elder, K., Hiemstra,
C., and Lund, J.: L-band InSAR depth retrieval during the NASA SnowEx
2020 campaign: Grand mesa, Colorado, in: Proceedings of the IEEE International
Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium, 11–16 July 2021, 625–627, https://doi.org/10.1109/IGARSS47720.2021.9553852, 2021.
Mätzler, C.: Improved Born approximation for scattering of radiation in
a granular medium, J. Appl. Phys., 83, 6111–6117, 1998.
Mätzler, C.: Relation between grain-size and correlation length of snow,
J. Glaciol., 48, 461–466, 2002.
McGrath, D., Webb, R., Shean, D., Bonnell, R., Marshall, H. P., Painter, T. H.,
Molotch, N. P., Elder, K., Hiemstra, C., and Brucker, L.: Spatially
extensive ground-penetrating radar snow depth observations during NASA's
2017 SnowEx campaign: Comparison to in situ, airborne, and satellite
observations, Water Resour. Res., 55, 10026–10036, https://doi.org/10.1029/2019WR024907, 2019.
Meehan, T. G., Marshall, H. P., Bradford, J. H., Hawley, R. L., Overly, T. B.,
Lewis, G., Graeter, K., Osterburg, E., and McCarthy, F.: Reconstruction of historical surface mass balance, 1984–2017 from GreenTrACS multi-offset ground-penetrating radar, J. Glaciol.,
67, 219–228, 2021.
Meloche, J., Royer, A., Langlois, A., Rutter, N., and Sasseville, V.:
Improvement of microwave emissivity parameterization of frozen Arctic soils
using roughness measurements derived from photogrammetry, Int.
J. Digit. Earth, 14, 1380–1396, https://doi.org/10.1080/17538947.2020.1836049, 2020.
Merkouriadi, I., Lemmetyinen, J., Liston, G. E., and Pulliainen, J.: Solving challenges of assimilating microwave remote sensing signatures with a physical model to estimate snow water equivalent, Water Resour. Res., 57, e2021WR030119, https://doi.org/10.1029/2021WR030119, 2021.
Meta, A., Imbembo, E., Trampuz, C., Coccia, A., and De Luca, G.: A selection
of meta sensing airborne campaigns at L-, X-and Ku-band, in: 2012 IEEE
International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012, 4571–4574, https://doi.org/10.1109/IGARSS.2012.6350452,
2012.
Meyer, J., Skiles, S. M., Deems, J., Bormann, K., and Shean, D.: Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-34, 2021.
Mironov, V. L., Dobson, M. C., Kaupp, V. H., Komarov, S. A., and
Kleshchenko, V. N.: Generalized refractive mixing dielectric model for moist
soils, IEEE T. Geosci. Remote, 42, 773–785,
2004.
Moller, D., Andreadis, K. M., Bormann, K. J., Hensley, S., and Painter, T.
H.: Mapping snow depth from Ka-band interferometry: Proof of concept and
comparison with scanning lidar retrievals, IEEE Geosci. Remote
S., 14, 886–890, 2017.
Montomoli, F., Macelloni, G., Brogioni, M., Lemmetyinen, J., Cohen, J., and
Rott, H.: Observations and simulation of multifrequency SAR data over a
snow-covered boreal forest, IEEE J. Sel. Top. Appl., 9, 1216–1228, 2016.
Montpetit, B., Royer, A., Langlois, A., Cliche, P., Roy, A., Champollion,
N., Picard, G., Domine, F., and Obbard, R.: New shortwave infrared albedo measurements for snow
specific surface area retrieval, J. Glaciol., 58, 941–952, https://doi.org/10.3189/2012JoG11J248,
2012.
Morin, S., Domine, F., Dufour, A., Lejeune, Y., Lesaffre, B., Willemet, J.
M., Carmagnola, C. M., and Jacobi, H. W.: Measurements and modeling of the vertical profile
of specific surface area of an alpine snowpack, Adv. Water Resour.,
55, 111–120, https://doi.org/10.1016/j.advwatres.2012.01.010, 2013.
Mousavi, S., De Roo, R., Sarabandi, K., and England, A. W.: Retrieval of
Snow or Ice Pack Thickness Variation Within a Footprint of Correlation
Radiometers, IEEE Geosci. Remote S., 17, 1218–1222,
2019.
Mudryk, L., Derksen, C., Kushner, P., and Brown, R.: Characterization of
Northern Hemisphere snow water equivalent datasets, 1981–2010, J.
Climate, 28, 8037–8051, 2015.
Mudryk, L., Santolaria-Otín, M., Krinner, G., Ménégoz, M., Derksen, C., Brutel-Vuilmet, C., Brady, M., and Essery, R.: Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble, The Cryosphere, 14, 2495–2514, https://doi.org/10.5194/tc-14-2495-2020, 2020.
Naderpour, R., Schwank, M., Houtz, D., Werner, C., and Mätzler, C.: Wideband Backscattering From Alpine Snow Cover: A Full-Season Study, IEEE T. Geosci. Remote, 60, 4302215, https://doi.org/10.1109/TGRS.2021.3112772, 2022.
Nagler, T., Roth, H., Ripper, E., Bippus, G., and Hetzenecker, M.:
Advancements for snowmelt monitoring by means of Sentinel-1 SAR, Remote
Sensing, 8, 348, https://doi.org/10.3390/rs8040348, 2016.
Natali, S. M., Watts, J. D., Rogers, B. M. et al.: Large loss of CO2 in winter
observed across the northern permafrost region, Nat. Clim. Change, 9,
852–857, https://doi.org/10.1038/s41558-019-0592-8, 2019.
Nolin, A. W. and Dozier, J.: A hyperspectral method for remotely sensing
the grain size of snow, Remote Sens. Environ., 74, 207–216, 2000.
Obu, J., Westermann, S., Bartsch, A., Berdnikov, N., Christiansen, H. H.,
Dashtseren, A., Delaloye, R., Elberling, B., Etzelmüller, B., Kholodov, A.,
Khomutov, A., Kääb, A., Leibman, M. O., Lewkowicz, A. G., Panda, S. K.,
Romanovsky, V., Way, R. G., Westergaard-Nielsen, A., Wu, T., Yamkhin, J., and Zou, D.: Northern Hemisphere Permafrost Map Based on TTOP Modelling for
2000–2016 at 1 km2 Scale, Earth-Sci. Rev., 193,
299–316, https://doi.org/10.1016/j.earscirev.2019.04.023, 2019.
Oh, Y. and Kay, Y. C.: Condition for precise measurement of soil
surface roughness, IEEE T. Geosci. Remote, 36, 691–695, 1998.
Oh, Y., Sarabandi, K., and Ulaby, F. T.: An empirical model and an inversion
technique for radar scattering from bare soil surfaces, IEEE T.
Geosci. Remote, 30, 370–381, 1992.
Pan, J., Durand, M. T., Vander Jagt, B. J., and Liu, D.: Application of a Markov
Chain Monte Carlo algorithm for snow water equivalent retrieval from passive
microwave measurements, Remote Sens. Environ., 192, 150–165, 2017.
Panzer, B., Gomez-Garcia, D., Leuschen, C., Paden, J., Rodriguez-Morales, F., Patel, A., Markus, T., Holt, B., and Gogineni, P.: An ultra-wideband, microwave
radar for measuring snow thickness on sea ice and mapping near-surface
internal layers in polar firn, J. Glaciol., 59, 244–254,
https://doi.org/10.3189/2013JoG12J128, 2013.
Peplinski, N. R., Ulaby, F. T., and Dobson, M. C.: Dielectric properties of
soils in the 0.3–1.3-GHz range, IEEE T. Geosci. Remote, 33, 803–807, 1995.
Picard, G., Brucker, L., Roy, A., Dupont, F., Fily, M., Royer, A., and Harlow, C.: Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model, Geosci. Model Dev., 6, 1061–1078, https://doi.org/10.5194/gmd-6-1061-2013, 2013.
Picard, G., Sandells, M., and Löwe, H.: SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0), Geosci. Model Dev., 11, 2763–2788, https://doi.org/10.5194/gmd-11-2763-2018, 2018.
Pomeroy, J., Stewart, R., and Whitfield, P.: The 2013 flood event in the
South Saskatchewan and Elk River basins: Causes, assessment and damages,
Can. Water Resour. J., 41, 105–117, https://doi.org/10.1080/07011784.2015.1089190, 2016.
Pomeroy, J. W., Gray, D. M., and Landine, P. G.: The Prairie Blowing
Snow Model: Characteristics, validation, operation, J. Hydrol., 144, 164–192, 1993.
Proksch, M., Mätzler, C., Wiesmann, A., Lemmetyinen, J., Schwank, M., Löwe, H., and Schneebeli, M.: MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering, Geosci. Model Dev., 8, 2611–2626, https://doi.org/10.5194/gmd-8-2611-2015, 2015a.
Proksch, M., Löwe, H., and Schneebeli, M.: Density, specific surface
area, and correlation length of snow measured by high-resolution
penetrometry, J. Geophys. Res.-Earth Surf., 120,
346–362, 2015b.
Proksch, M., Rutter, N., Fierz, C., and Schneebeli, M.: Intercomparison of snow density measurements: bias, precision, and vertical resolution, The Cryosphere, 10, 371–384, https://doi.org/10.5194/tc-10-371-2016, 2016.
Pulliainen, J.: Mapping of snow water equivalent and snow depth in
boreal and sub-arctic zones by assimilating space-borne microwave radiometer
data and ground-based observations, Remote Sens. Environ., 101, 257–269,
https://doi.org/10.1016/j.rse.2006.01.002, 2006.
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., and Norberg, J.: Patterns and trends of Northern
Hemisphere snow mass from 1980 to 2018, Nature, 581, 294–298, 2020.
Qin, Y., Abatzoglou, J. T., Siebert, S., Huning, L. S., AghaKouchak, A.,
Mankin, J. S., Hong, C., Tong, D., Davis, S. J., and Mueller, N. D.:
Agricultural risks from changing snowmelt, Nat. Clim. Change, 10, 459–465,
https://doi.org/10.1038/s41558-020-0746-8, 2020.
Raleigh, M. S. and Small, E. E.: Snowpack density modeling is the primary
source of uncertainty when mapping basin-wide SWE with lidar, Geophys. Res.
Lett., 44, 3700–3709, https://doi.org/10.1002/2016GL071999, 2017.
Reigber, A. and Moreira, A.: First demonstration of airborne SAR tomography
using multibaseline L-band data, IEEE T. Geosci. Remote, 38, 2142–2152, 2000.
Rekioua, B., Davy, M., Ferro-Famil, L., and Tebaldini, S.: Snowpack
permittivity profile retrieval from tomographic SAR data, C. R.
Phys., 18, 57–65, 2017.
Rott, H., Yueh, S. H., Cline, D. W., Duguay, C., Essery, R., Haas, C., Hélière, F., Kern, M., Macelloni, G., Malnes, E., Nagler, T., Pulliainen, J., Rebhan, H., and Thompson, A.: Cold regions hydrology high-resolution observatory for snow and cold
land processes, Proc. IEEE, 98, 752–765, https://doi.org/10.1109/JPROC.2009.2038947, 2010.
Rott, H., Cline, D. W., Duguay, C., Essery, R., Etchevers, P., Macelloni, G.,
Hajnsek, I., Kern, M., Malnes, E., Pulliainen J., and Yueh, S. H.: CoReH2O, a
Candidate ESA Earth Explorer Mission for snow and ice observations, in: Proc. of
the Earth Observation and Cryosphere Science Conference, Frascati, Italy,
November 2012, ESA SP-712, European Space Agency, Noordwijk, the Netherlands, 2013.
Rott, H., Scheiblauer, S., Wuite, J., Krieger, L., Floricioiu, D., Rizzoli, P., Libert, L., and Nagler, T.: Penetration of interferometric radar signals in Antarctic snow, The Cryosphere, 15, 4399–4419, https://doi.org/10.5194/tc-15-4399-2021, 2021.
Roy, A., Leduc-Leballeur, M., Picard, G., Royer, A., Toose, P., Derksen, C.,
Lemmetyinen, J., Berg, A., Rowlandson, T., and Schwank, M.: Modelling the
L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie
Environment, Remote Sensing, 10, 1451, https://doi.org/10.3390/rs10091451, 2018.
Rutter, N., Sandells, M. J., Derksen, C., King, J., Toose, P., Wake, L., Watts, T., Essery, R., Roy, A., Royer, A., Marsh, P., Larsen, C., and Sturm, M.: Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals, The Cryosphere, 13, 3045–3059, https://doi.org/10.5194/tc-13-3045-2019, 2019.
Sandells, M., Löwe, H., Picard, G., Dumont, M., Essery, R., Floury, N.,
Kontu, A., Lemmetyinen, J., Maslanka, W., Morin, S., Wiesmann, A., and
Mätzler, C.: X-Ray Tomography-Based Microstructure Representation in the
Snow Microwave Radiative Transfer Model, IEEE T. Geosci.
Remote, 60, 1–15, https://doi.org/10.1109/TGRS.2021.3086412, 2021.
Schneebeli, M. and Sokratov, S. A.: Tomography of temperature gradient
metamorphism of snow and associated changes in heat conductivity,
Hydrol. Process., 18, 3655–3665, https://doi.org/10.1002/hyp.5800, 2004.
Shah, R., Xu, X., Yueh, S., Chae, C. S., Elder, K., Starr, B., and Kim, Y.:
Remote sensing of snow water equivalent using P-band coherent reflection,
IEEE Geosci. Remote S., 14, 309–313, 2017.
Shi, J. and Dozier, J.: Estimation of snow water equivalence using
SIR-C/X-SAR. II. Inferring snow depth and particle size, IEEE T.
Geosci. Remote, 38, 2475–2488, https://doi.org/10.1109/36.885196,
2000.
Shi, J., Xiong, C., and Jiang, L.: Review of snow water equivalent microwave
remote sensing, Science China Earth Sciences, 59, 731–745, 2016.
Sihvola, A. and Tiuri, M.: Snow fork for field determniation of the density
and wetness profiles of a snow pack, IEEE T. Geosci.
Remote, 24, 717–721, 1986.
Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., and Wilheit, T.: The Global Precipitation Measurement
(GPM) mission for science and society, B. Am.
Meteorol. Soc., 98, 1679–1695, 2017.
Smith, C. D., Kontu, A., Laffin, R., and Pomeroy, J. W.: An assessment of two automated snow water equivalent instruments during the WMO Solid Precipitation Intercomparison Experiment, The Cryosphere, 11, 101–116, https://doi.org/10.5194/tc-11-101-2017, 2017.
Sospedra-Alfonso, R. and Merryfield, W.: Influences of temperature
and precipitation on historical and future snowpack variability over the
Northern Hemisphere in the Second Generation Canadian Earth System Model,
J. Climate, 30, 4633–4656, https://doi.org/10.1175/JCLI-D-16-0612.1, 2017.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and CloudSat Science Team 2002: The CloudSat mission and the A-Train: A
new dimension of space-based observations of clouds and
precipitation, B. Am. Meteorol. Soc., 83,
1771–1790, 2002.
Stiles, W. H. and Ulaby, F. T.: The active and passive microwave response
to snow parameters: 1. Wetness, J. Geophys. Res.-Oceans,
85, 1037–1044, 1980.
Sturm, M.: The role of thermal convection in the heat and mass transport in
the subarctic snow cover, PhD thesis, University
of Alaska, http://hdl.handle.net/11122/9351 (last access: 10 August 2022), 1989.
Sturm, M. and Benson, C. S.: Vapor transport, grain growth and depth-hoar
development in the subarctic snow, J. Glaciol., 43, 42–59,
1997.
Sturm, M. and Holmgren, J.: An Automatic Snow Depth Probe for Field
Validation Campaigns, Water Resour. Res., 54, 9695–9701, https://doi.org/10.1029/2018wr023559, 2018.
Sturm, M. and Liston, G. E.: Revisiting the Global Seasonal Snow
Classification: An Updated Dataset for Earth System Applications, J. Hydrometeorol., 22, 2917–2938,
https://doi.org/10.1175/jhm-d-21-0070.1, 2021.
Sturm, M., Holmgren, J., and Liston, G. E.: A seasonal snow cover
classification system for local to global applications, J. Climate,
8, 1261–1283, 1995.
Sturm, M., Goldstein, M. A., and Parr, C.: Water and life from snow: A
trillion dollar science question, Water Resour. Res., 53,
3534–3544, 2017.
Swan, A. M. and Long, D. G.: Multiyear Arctic sea ice classification using
QuikSCAT, IEEE T. Geosci. Remote, 50,
3317–3326, 2012.
Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.-P., Koskinen, J., and Bojkov, B.: Estimating northern hemisphere
snow water equivalent for climate research through assimilation of
space-borne radiometer data and ground-based measurements, Remote Sens.
Environ., 115, 3517–3529, https://doi.org/10.1016/j.rse.2011.08.014, 2011.
Tan, S., Chang, W., Tsang, L., Lemmetyinen, J., and Proksch, M.: Modeling
both active and passive microwave remote sensing of snow using dense media
radiative transfer (DMRT) theory with multiple scattering and backscattering
enhancement, IEEE J. Sel. Top. Appl., 8, 4418–4430, 2015.
Tan, S., Xiong, C., Xu, X., and Tsang, L.: Uniaxial Effective Permittivity of
Anisotropic Bicontinuous Random Media Using NMM3D, IEEE Geosci.
Remote Sens., 13, 1168–1172, https://doi.org/10.1109/LGRS.2016.2574759, 2016.
Tan, S., Zhu, J., Tsang, L., and Nghiem, S. V.: Microwave signatures of snow
cover using numerical Maxwell equations based on discrete dipole
approximation in bicontinuous media and half-space dyadic green's
function, IEEE J. Sel. Top. Appl., 10, 4686–4702, 2017.
Tape, K. D., Rutter, N., Marshall, H. P., Essery, R., and Sturm, M.:
Recording microscale variations in snowpack layering using near-infrared
photography, J. Glaciol., 56, 75–80, https://doi.org/10.3189/002214310791190938, 2010.
Taylor, D., Yan, J., O'Neill, C., Gogineni, S., Gurbuz, S., Aslan, B.,
Larson, J., Elluru, D., Kolpuke, S., Li, L., Mahjabeen, F., Nunn, J.,
Rahman, M., Reyhanigalangashi, O., Simpson, C., Thomas, R., Wattal, S.,
Blake, J., Boyle, C., Glidden, J., and Higgs, M.: Airborne dual-band
microwave radar system for snow thickness measurement, in: 2020 IEEE
International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 26 September–2 October 2020, https://doi.org/10.1109/IGARSS39084.2020.9323958, 2020.
Tebaldini, S. and Rocca, F.: Multibaseline polarimetric SAR tomography of a
boreal forest at P-and L-bands, IEEE T. Geosci. Remote, 50, 232–246, 2011.
Tedesco, M. and Miller, J.: Observations and statistical analysis of
combined active–passive microwave space-borne data and snow depth at large
spatial scales, Remote Sens. Environ., 111, 382–397,
https://doi.org/10.1016/j.rse.2007.04.019, 2007.
Thompson, A. and Kelly, R.: Observations of coniferous forest at 9.6
and 17.2 GHz: Implications for SWE retrievals, Remote Sensing, 11, 6,
https://doi.org/10.3390/rs11010006, 2019.
Thompson, A. and Kelly, R.: Radar retrieval of snow water
equivalent for mid-latitude agricultural sites, Can. J. Remote Sens., 47, 119–142,
https://doi.org/10.1080/07038992.2021.1898938, 2021a.
Thompson, A. and Kelly, R.: Estimating wind slab thickness in a
tundra snowpack, Remote Sens. Lett., 12, 1123–1135,
https://doi.org/10.1080/2150704X.2021.1961174, 2021b.
Thompson, S. S., Kulessa, B., Essery, R. L. H., and Lüthi, M. P.: Bulk meltwater flow and liquid water content of snowpacks mapped using the electrical self-potential (SP) method, The Cryosphere, 10, 433–444, https://doi.org/10.5194/tc-10-433-2016, 2016.
Treuhaft, R. N. and Siqueira, P. R.: Vertical structure of vegetated land
surfaces from interferometric and polarimetric radar, Radio Sci., 35,
141–177, 2000.
Treuhaft, R. N., Moghaddam, M., and van Zyl, J. J.: Vegetation
characteristics and underlying topography from interferometric radar, Radio
Sci., 31, 1449–1485, 1996.
Tsang, L. and Kong, J. A.: Scattering of Electromagnetic Waves, Volume 3:
Advanced Topics, Wiley-Interscience, New York, NY, USA, ISBN 978-0-471-22427-3, 2001.
Tsang, L., Blanchard, A. J., Newton, R. W., and Kong, J. A.: A simple
relation between active and passive microwave remote sensing measurements of
earth terrain, IEEE T. Geosci. Remote, GE-20,
482–485, 1982.
Tsang, L., Kong, J. A., and Shin, R. T.: Theory of microwave remote sensing, Wiley, ISBN 9780471888604, 1985.
Tsang, L., Ding, K. H., and Wen, B.: Dense media radiative transfer theory
for dense discrete random media with particles of multiple sizes and
permittivities, Progress in Electromagnetic Research, 6, 181–225, 1992.
Tsang, L., Kong, J. A., and Ding, K. H.: Scattering of electromagnetic
waves: theories and applications, vol. 27, John Wiley & Sons, ISBN 9780471387992, 2004.
Tsang, L., Pan, J., Liang, D., Li, Z., Cline, D. W., and Tan, Y.: Modeling
active microwave remote sensing of snow using dense media radiative transfer
(DMRT) theory with multiple-scattering effects, IEEE T.
Geosci. Remote, 45, 990–1004, 2007.
Tsang, L., Tan, S., Xiong, C., and Shi, J.: Optical and Microwave Modeling
of Snow, chap. 5, 85–138, vol. 4, Comprehensive Remote Sensing: Water
Cycle Components over Land, Elsevier, ISBN 9780128032206, 2018.
Ulaby, F. and Long, D.: Microwave radar and radiometric remote sensing,
Artech House, ISBN 9780472119356, 2015.
Ulaby, F. T. and Stiles, W. H.: The active and passive microwave response
to snow parameters: 2. Water equivalent of dry snow, J. Geophys.
Res.-Oceans, 85, 1045–1049, 1980.
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave Remote Sensing:
Active and Passive, vol. 1, 456 p., Addison-Wesley, Reading, MA, ISBN 9780890061923, 1981.
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave remote sensing:
Active and passive, volume 3 – From theory to applications, 1986.
Ulaby, F. T., Sarabandi, K., Mcdonald, K., Whitt, M., and Dobson,
M. C.: Michigan microwave canopy scattering model, Int. J.
Remote Sens., 11, 1223–1253, 1990.
Vander Jagt, B. J., Durand, M. T., Margulis, S. A., Kim, E. J., and Molotch,
N. P.: The effect of spatial variability on the sensitivity of passive
microwave measurements to snow water equivalent, Remote Sens.
Environ., 136, 163–179, 2013.
Vionnet, V., Fortin, V., Gaborit, E., Roy, G., Abrahamowicz, M., Gasset, N., and Pomeroy, J. W.: Assessing the factors governing the ability to predict late-spring flooding in cold-region mountain basins, Hydrol. Earth Syst. Sci., 24, 2141–2165, https://doi.org/10.5194/hess-24-2141-2020, 2020.
Voronovich, A.: Small-slope approximation for electromagnetic wave
scattering at a rough interface of two dielectric half-spaces, Wave.
Random Media, 4, 337–367, 1994.
Werner, C., Wiesmann, A., Strozzi, T., Schneebeli, M., and Mätzler, C.:
The SnowScat ground-based polarimetric scatterometer: Calibration and
initial measurements from Davos Switzerland, in: 2010 IEEE International
Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25–30 July 2010, 2363–2366, https://doi.org/10.1109/IGARSS.2010.5649015, 2010.
West, R., Tsang, L., and Winebrenner, D. P.: Dense medium radiative transfer
theory for two scattering layers with a Rayleigh distribution of particle
sizes. IEEE T. Geosci. Remote, 31, 426–437,
1993.
Wiesmann, A., Mätzler, C., and Weise, T.: Radiometric and
structural measurements of snow samples, Radio Sci., 33, 273–289, 1998.
Wiesmann, A., Caduff, R., Werner, C., Frey, O., Schneebeli, M., Löwe,
H., Jaggi, M., Schwank, M., Naderpour, R., and Fehr, T.: ESA SnowLab Project: 4 Years of Wide Band
Scatterometer Measurements of Seasonal Snow, in: IGARSS 2019–2019 IEEE
International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019, 5745–5748, https://doi.org/10.1109/IGARSS.2019.8898961,
2019.
Wiscombe, W. J. and Warren, S. G.: A model for the spectral albedo of snow.
I: Pure snow, J. Atmos. Sci., 37, 2712–2733, 1980.
Wood, A. W., Hopson, T., Newman, A., Brekke, L., Arnold, J., and Clark, M.: Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and
Climate Prediction Skill, J. Hydrometeorol., 17, 651–668, https://doi.org/10.1175/JHM-D-14-0213.1, 2016.
Wrzesien, M. L., Pavelsky, T. M., Durand, M. T., Dozier, J., and Lundquist,
J. D.: Characterizing biases in mountain snow accumulation from
global data sets, Water Resour. Res., 55, 9873–9891, https://doi.org/10.1029/2019WR025350, 2019a.
Wrzesien, M. L., Durand, M. T., and Pavelsky, T. M.: A Reassessment
of North American River Basin Cool-Season Precipitation: Developments From a
New Mountain Climatology Data Set, Water Resour. Res., 55, 3502–3519,
https://doi.org/10.1029/2018wr024106, 2019b.
Xiong, C. and Shi, J.: The potential for estimating snow depth with
QuikScat data and a snow physical model, IEEE Geosci. Remote S., 14, 1156–1160, https://doi.org/10.1109/LGRS.2017.2701808, 2017.
Xiong, C. and Shi, J.: Seasonal snow water equivalent remote sensing by Ku
band spaceborne scatterometers, in: AGU Fall Meeting Abstracts, vol. 2019,
pp. C33E–1637, 2019.
Xu, X., Tsang, L., and Yueh, S.: Electromagnetic models of co/cross
polarization of bicontinuous/DMRT in radar remote sensing of terrestrial
snow at X-and Ku-band for CoReH2O and SCLP applications, IEEE J.
Sel. Top. Appl., 5,
1024–1032, 2012.
Xu, X., Baldi, C. A., De Bleser, J. W., Lei, Y., Yueh, S., and
Esteban-Fernandez, D.: Multi-Frequency Tomography Radar Observations of Snow
Stratigraphy at Fraser During SnowEx, in: IGARSS 2018–2018 IEEE International
Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018, https://doi.org/10.1109/IGARSS.2018.8519538, 6269–6272, 2018.
Xu, X., Shen, H., Xu, H., and Tsang, L.: Modeling Multi-Frequency
Tomograms for Snow Stratigraphy, in: IGARSS 2020–2020 IEEE International
Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 26 September–2 October 2020, 3436–3439, https://doi.org/10.1109/IGARSS39084.2020.9324184, 2020.
Yan, H., Sun, N., Wigmosta, M., Skaggs, R., Hou, Z., and Leung, R.:
Next-Generation Intensity-Duration-Frequency Curves for Hydrologic Design in
Snow-Dominated Environments, Water Resour. Res., 54, 1093–1108, https://doi.org/10.1002/2017WR021290, 2018.
Yan, J.-B., Gogineni, S., Rodríguez‐Morales, F., Gomez-Garcia, D., Paden, J. D., Li, J., Leuschen, C., Braaten, D., Richter-Menge, J., Farrell, S. L., Brozena, J. M., and Hale, R. D.: Airborne Measurements of Snow Thickness:
Using ultrawide-band frequency-modulated-continuous-wave radars, IEEE
Geoscience and Remote Sensing Magazine, 5, 57–76, 2017.
Yueh, S. H., Dinardo, S. J., Akgiray, A., West, R., Cline, D. W., and Elder,
K.: Airborne Ku-Band Polarimetric Radar Remote Sensing of Terrestrial Snow
Cover. IEEE T. Geosci. Remote, 47,
3347–3364. https://doi.org/10.1109/tgrs.2009.2022945, 2009.
Yueh, S. H., Shah, R., Xu, X., Stiles, B., and Bosch-Lluis, X.: A Satellite
Synthetic Aperture Radar Concept Using P-Band Signals of Opportunity, IEEE
J. Sel. Top. Appl.,
14, 2796–2816, 2021.
Zhang, G. and Tsang, L.: Application of angular correlation function of
clutter scattering and correlation imaging in target detection, IEEE
T. Geosci. Remote, 36, 1485–1493, 1998.
Zhu, J.: Surface and Volume Scattering Model in Microwave Remote Sensing of
Snow and Soil Moisture, PhD thesis, University of Michigan, https://doi.org/10.7302/3871, 2021.
Zhu, J., Tan, S., King, J., Derksen, C., Lemmetyinen, J., and Tsang, L.:
Forward and Inverse Radar Modeling of Terrestrial Snow Using SnowSAR Data,
IEEE T. Geosci. Remote, 56, 7122–7132,
https://doi.org/10.1109/TGRS.2018.2848642, 2018.
Zhu, J., Tan, S., Tsang, L., Kang, D. K., and Kim, E.: Snow Water Equivalent
Retrieval Using Active and Passive Microwave Observations, Water Resour.
Res., 57, e2020WR027563, https://doi.org/10.1029/2020WR027563, 2021a.
Zhu, J., Tsang, L., and Liao, T. H.: Scattering from Random Rough Surfaces
at X and Ku band for Global Remote Sensing of Terrestrial Snow, IEEE
International Symposium on Antennas and Propagation and USNC-URSI Radio
Science Meeting (APS/URSI), Singapore, 4–10 December 2021, 1115–1116, https://doi.org/10.1109/APS/URSI47566.2021.9704233, 2021b.
Zoughi, R., Wu, L. K., and Moore, R. K.: Identification of Major
Backscattering Sources in Trees and Shrubs at 10 GHz, Remote Sens.
Environ., 19, 269–290, https://doi.org/10.1016/0034-4257(86)90057-X, 1986.
Zuanon, N.: IceCube, a portable and reliable instrument for snow specific
surface area measurement in the field, International Snow Science Workshop,
Grenoble – Chamonix Mont-Blanc, 2013.
Zuniga, M. A., Habashy, T. M., and Kong, J. A.: Active remote sensing of
layered random media, IEEE T. Geosci. Elect., 17,
296–302, 1979.
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.
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical...
