Articles | Volume 18, issue 12
https://doi.org/10.5194/tc-18-5619-2024
© Author(s) 2024. 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-18-5619-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Dec 2024
Research article |
| 05 Dec 2024
| 05 Dec 2024
Use of multiple reference data sources to cross-validate gridded snow water equivalent products over North America
Colleen Mortimer
CORRESPONDING AUTHOR
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Lawrence Mudryk
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Eunsang Cho
Ingram School of Engineering, Texas State University, San Marcos, TX, USA
Chris Derksen
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Mike Brady
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Carrie Vuyovich
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Related authors
Colleen Mortimer and Vincent Vionnet
Earth Syst. Sci. Data, 17, 3619–3640, https://doi.org/10.5194/essd-17-3619-2025, https://doi.org/10.5194/essd-17-3619-2025, 2025
Short summary
Short summary
In situ observations of snow water equivalent (SWE) are critical for climate applications and resource management. NorSWE is a dataset of in situ SWE observations covering North America, Norway, Finland, Switzerland, Russia, and Nepal over the period 1979–2021. It includes more than 11.5 million observations from more than 10 000 different locations compiled from nine different sources. Snow depth and derived bulk snow density are included when available.
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.
Libo Wang, Lawrence Mudryk, Joe R. Melton, Colleen Mortimer, Jason Cole, Gesa Meyer, Paul Bartlett, and Mickaël Lalande
EGUsphere, https://doi.org/10.5194/egusphere-2025-1264, https://doi.org/10.5194/egusphere-2025-1264, 2025
Short summary
Short summary
This study shows that an alternate snow cover fraction (SCF) parameterization significantly improves SCF simulated in the CLASSIC model in mountainous areas for all three choices of meteorological datasets. Annual mean bias, unbiased root mean squared area, and correlation improve by 75 %, 32 %, and 7 % when evaluated with MODIS SCF observations over the Northern Hemisphere. We also link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and SCF.
Pinja Venäläinen, Colleen Mortimer, Kari Luojus, Lawrence Mudryk, Matias Takala, and Jouni Pulliainen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3643, https://doi.org/10.5194/egusphere-2024-3643, 2025
Short summary
Short summary
Satellite data-based estimation of large SWE values can be improved with bias correction. This study updates the bias correction method by using updated snow course data, extending correction to two new months. Additionally, bias correction is expanded from a monthly to a daily time scale. The daily bias correction offers more accurate hemispheric snow mass estimation, aligning well with reanalysis data.
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.
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.
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.
Vincent Vionnet, Colleen Mortimer, Mike Brady, Louise Arnal, and Ross Brown
Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, https://doi.org/10.5194/essd-13-4603-2021, 2021
Short summary
Short summary
Water equivalent of snow cover (SWE) is a key variable for water management, hydrological forecasting and climate monitoring. A new Canadian SWE dataset (CanSWE) is presented in this paper. It compiles data collected by multiple agencies and companies at more than 2500 different locations across Canada over the period 1928–2020. Snow depth and derived bulk snow density are also included when available.
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.
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.
Colleen Mortimer and Vincent Vionnet
Earth Syst. Sci. Data, 17, 3619–3640, https://doi.org/10.5194/essd-17-3619-2025, https://doi.org/10.5194/essd-17-3619-2025, 2025
Short summary
Short summary
In situ observations of snow water equivalent (SWE) are critical for climate applications and resource management. NorSWE is a dataset of in situ SWE observations covering North America, Norway, Finland, Switzerland, Russia, and Nepal over the period 1979–2021. It includes more than 11.5 million observations from more than 10 000 different locations compiled from nine different sources. Snow depth and derived bulk snow density are included when available.
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.
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.
Stephen Howell, Alex Cabaj, David Babb, Jack Landy, Jackie Dawson, Mallik Mahmud, and Mike Brady
EGUsphere, https://doi.org/10.5194/egusphere-2025-2029, https://doi.org/10.5194/egusphere-2025-2029, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The Northwest Passage provides a shorter transit route connecting the Atlantic Ocean to the Pacific Ocean but ever-present sea ice has prevented its practical navigation. Sea ice area in the northern route of the Northwest Passage on September 30, 2024 fell to a minimum of 4x103 km2, the lowest ice area observed since 1960. This paper describes the unique processes that contributed to the record low sea ice area in the northern route of the Northwest Passage in 2024.
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).
Libo Wang, Lawrence Mudryk, Joe R. Melton, Colleen Mortimer, Jason Cole, Gesa Meyer, Paul Bartlett, and Mickaël Lalande
EGUsphere, https://doi.org/10.5194/egusphere-2025-1264, https://doi.org/10.5194/egusphere-2025-1264, 2025
Short summary
Short summary
This study shows that an alternate snow cover fraction (SCF) parameterization significantly improves SCF simulated in the CLASSIC model in mountainous areas for all three choices of meteorological datasets. Annual mean bias, unbiased root mean squared area, and correlation improve by 75 %, 32 %, and 7 % when evaluated with MODIS SCF observations over the Northern Hemisphere. We also link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and SCF.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Amélie Bouchat, Damien Ringeisen, Philippe Blain, Stephen Howell, Mike Brady, Alexander S. Komarov, Béatrice Duval, Lekima Yakuden, and Frédérique Labelle
Earth Syst. Sci. Data, 17, 423–434, https://doi.org/10.5194/essd-17-423-2025, https://doi.org/10.5194/essd-17-423-2025, 2025
Short summary
Short summary
Sea ice forms a thin boundary between the ocean and the atmosphere, with complex, crust-like dynamics and ever-changing networks of sea ice leads and ridges. Statistics of these dynamical features are often used to evaluate sea ice models. Here, we present a new pan-Arctic dataset of sea ice deformations derived from satellite imagery, from 1 September 2017 to 31 August 2023. We discuss the dataset coverage and some limitations associated with uncertainties in the computed values.
Pinja Venäläinen, Colleen Mortimer, Kari Luojus, Lawrence Mudryk, Matias Takala, and Jouni Pulliainen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3643, https://doi.org/10.5194/egusphere-2024-3643, 2025
Short summary
Short summary
Satellite data-based estimation of large SWE values can be improved with bias correction. This study updates the bias correction method by using updated snow course data, extending correction to two new months. Additionally, bias correction is expanded from a monthly to a daily time scale. The daily bias correction offers more accurate hemispheric snow mass estimation, aligning well with reanalysis data.
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.
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.
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.
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.
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.
Eunsang Cho, Megan Verfaillie, Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
EGUsphere, https://doi.org/10.5194/egusphere-2024-1530, https://doi.org/10.5194/egusphere-2024-1530, 2024
Short summary
Short summary
Uncrewed Aerial Systems (UAS) lidar and structure-from-motion (SfM) photogrammetry are effective methods for mapping high-resolution snow depths. However, there are limited studies comparing their performance across different surface features and tracking spatial patterns of snowpack changes over time. Our study found that UAS lidar outperformed SfM photogrammetry. With limited wind effects, the snow spatial structure captured by UAS lidar remained temporally stable throughout the snow season.
Stephen E. L. Howell, David G. Babb, Jack C. Landy, Isolde A. Glissenaar, Kaitlin McNeil, Benoit Montpetit, and Mike Brady
The Cryosphere, 18, 2321–2333, https://doi.org/10.5194/tc-18-2321-2024, https://doi.org/10.5194/tc-18-2321-2024, 2024
Short summary
Short summary
The CAA serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. It is also an important region with respect to navigating the Northwest Passage. Here, we quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage.
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.
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.
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.
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.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere, 17, 3435–3442, https://doi.org/10.5194/tc-17-3435-2023, https://doi.org/10.5194/tc-17-3435-2023, 2023
Short summary
Short summary
This study compares snow depth measurements from two manual instruments in a field and forest. Snow depths measured using a magnaprobe were typically 1 to 3 cm deeper than those measured using a snow tube. These differences were greater in the forest than in the field.
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.
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.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
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.
Stephen E. L. Howell, Mike Brady, and Alexander S. Komarov
The Cryosphere, 16, 1125–1139, https://doi.org/10.5194/tc-16-1125-2022, https://doi.org/10.5194/tc-16-1125-2022, 2022
Short summary
Short summary
We describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using synthetic aperture radar (SAR) images. The ECCC-ASITS was applied to the incoming image streams of Sentinel-1AB and the RADARSAT Constellation Mission from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (i.e., 7 d 25 km and 3 d 6.25 km).
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-7, https://doi.org/10.5194/tc-2022-7, 2022
Manuscript not accepted for further review
Short summary
Short summary
This study compares snow depth measurements from two manual instruments and an airborne platform in a field and forest. The manual instruments’ snow depths differed by 1 to 3 cm. The airborne measurements , which do not penetrate the leaf litter, were consistently shallower than either manual instrument. When combining airborne snow depth maps with manual density measurements, corrections may be required to create unbiased maps of snow properties.
Vincent Vionnet, Colleen Mortimer, Mike Brady, Louise Arnal, and Ross Brown
Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, https://doi.org/10.5194/essd-13-4603-2021, 2021
Short summary
Short summary
Water equivalent of snow cover (SWE) is a key variable for water management, hydrological forecasting and climate monitoring. A new Canadian SWE dataset (CanSWE) is presented in this paper. It compiles data collected by multiple agencies and companies at more than 2500 different locations across Canada over the period 1928–2020. Snow depth and derived bulk snow density are also included when available.
Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho
The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, https://doi.org/10.5194/tc-15-1485-2021, 2021
Short summary
Short summary
This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
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.
Stephen E. L. Howell, Randall K. Scharien, Jack Landy, and Mike Brady
The Cryosphere, 14, 4675–4686, https://doi.org/10.5194/tc-14-4675-2020, https://doi.org/10.5194/tc-14-4675-2020, 2020
Short summary
Short summary
Melt ponds form on the surface of Arctic sea ice during spring and have been shown to exert a strong influence on summer sea ice area. Here, we use RADARSAT-2 satellite imagery to estimate the predicted peak spring melt pond fraction in the Canadian Arctic Archipelago from 2009–2018. Our results show that RADARSAT-2 estimates of peak melt pond fraction can be used to provide predictive information about summer sea ice area within certain regions of the Canadian Arctic Archipelago.
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.
Cited articles
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.
Beaumont, R. T.: Mt. Hood pressure pillow snow gage, J. Appl. Meteorol., 4, 626–631, https://doi.org/10.1175/1520-0450(1965)004<0626:MHPPSG>2.0.CO;2, 1965.
Brown, R., Brasnett, B., and Robinson, D.: Gridded North American monthly snow depth and snow water equivalent for GCM evaluation, Atmos. Ocean, 41, 1–14, https://doi.org/10.3137/ao.410101, 2003.
Brown, R. D., Fang, B., and Mudryk, L.: Update of Canadian historical snow survey data and analysis of snow water equivalent trends, 1967–2016, Atmos. Ocean, 57, 149–156, https://doi.org/10.1080/07055900.2019.1598843, 2019.
Broxton, P., Zeng, X., and Dawson N.: Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/0GGPB220EX6A, 2019.
Brun, E., Vionnet, V., Boone, A., Decharme, B., Peings, Y., Vallette, R., Karbou, F., and Morin, S.: Simulation of northern Eurasian local snow depth, mass, and density using a detailed snowpack model and meteorological reanalyses, J. Hydrometeorol., 14, 203–219, https://doi.org/10.1175/JHM-D-12-012.1, 2013.
Carroll, S. S. and Carroll, T. R.: Effect of uneven snow cover on airborne snow water equivalent estimates obtained by measuring terrestrial gamma radiation, Water Resour. Res., 25, 1505–1510, https://doi.org/10.1029/WR025i007p01505, 1989.
Carroll, T. R.: Airborne Gamma Radiation Snow Survey Program: A user's guide, Version 5.0, National Operational Hydrologic Remote Sensing Center (NOHRSC), Chanhassen, 14, 2001.
Carroll, T. R. and Schaake Jr., J. C.: Airborne snow water equivalent and soil moisture measurement using natural terrestrial gamma radiation, Optical Engineering for Cold Environments, Proc. SPIE 0414, 208–213, https://doi.org/10.1117/12.935888, 1983.
Carroll, T. R. and Vose, G. D.: Airborne snow water equivalent measurements over a forested environment using terrestrial gamma radiation, in: Proceedings of the Eastern Snow Conference, 7–8 June 1984, New Carrolton, Maryland, USA, 29, 101–115, 1984.
Chang, A. T. C., Foster, J. L., Hall, D. K., Rango, A., and Hartline, B. K.: Snow water equivalent estimation by microwave radiometry, Cold Reg. Sci. Technol., 5, 259–267, https://doi.org/10.1016/0165-232X(82)90019-2, 1982.
Chang, A. T. C., Foster, J. L., and Hall, D. K.: Nimubs-7 SMMR derived global snow cover parameters, Ann. Glaciol., 9, 39–44, https://doi.org/10.3189/S0260305500200736, 1987.
Cho, E., Jacobs, J. M., and Vuyovich, C.: The value of long-term (40 years) airborne gamma radiation SWE record for evaluating three observation-based gridded SWE datasets by seasonal snow and land cover classifications, Water Resour. Res., 56, e2019WR025813, https://doi.org/10.1029/2019WR025813, 2019.
Cho, E., Jacobs, J. M., Schroeder, R., Tuttle, S. E., and Olheiser, C.: Improvement of operational airborne gamma radiation using SMAP soil moisture, Remote Sens. Environ., 240, 111668, https://doi.org/10.1016/j.rse.2020.111668, 2020.
Clark, M. P., Hendrix, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Hreinsson, E. O., and Woods, R. A.: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: a review, Water Resour. Res., 47, W07539, https://doi.org/10.1029/2011WR010745, 2011.
Cork, H. F. and Loijens, H. S.: The effect of snow drifting on gamma snow survey results, J. Hydrol., 48, 41–51, https://doi.org/10.1016/0022-1694(80)90064-5, 1980.
Decharme, B. and Barbu, A.: Crocus-ERA5 daily snow product over the Northern Hemisphere at 0.25° resolution (Version 2023), Zenodo [data set], https://doi.org/10.5281/zenodo.10943718, 2024.
De Roo, A. P., Gouweleeuw, B., Thielen, J., Bartholmes, J., Bongioannini-Cerlini, P., Todini, E., Bates, P. D., Horritt, M., Hunter, N., and Beven, K.: Development of a European flood forecasting system, Intl. J. River Basin Management, 1, 49–59, https://doi.org/10.1080/15715124.2003.9635192, 2003.
de Rosnay, P. Browne, P., de Boisséson, E., Fairbairn, D., Hirahara, Y., Ochi, K., Schepers, D., Weston, P., Zuo, H., Alonso-Balmaseda, M., Balsamo, G., Bonavita, M., Borman, N., Brown, A., Chrust, M., Dahoui, M., Chiara, G., English, S., Geer, A., Healy, S., Hersbach, H., Laloyaux, P., Magnusson, L., Massart, S., McNally, A., Pappenberger, F., and Rabier, F.: Coupled data assimilation at ECMWF: current status, challenges and future developments, Q. J. Roy. Meteor. Soc., 148, 2672–2702, https://doi.org/10.1002/qj.4330, 2022.
Dixon, D. and Boon, S.: Comparison of the SnowHydro snow sampler with existing snow tube designs, Hydrol. Process., 26, 2555–2562, https://doi.org/10.1002/hyp.9317, 2012.
Dozier, J., Bair, E. H., and Davis, R.: Estimating the spatial distribution of snow water equivalent in the world's mountains, WIREs Water, 3, 461–474, https://doi.org/10.1002/wat2.1140, 2016.
Durand, Y., Giraud, G., Laternser, M., Etchevers, P., Mèrindol, L., and Lesaffre, B.: Reanalysis of 47 Years of Climate in the French Alps (1958–2005): Climatology and Trends for Snow Cover, J. Appl. Meteorol. Clim., 48, 2487–2512, https://doi.org/10.1175/2009jamc1810.1, 2009.
Ecoregions of North America: NA_Eco_Level1, U.S. Environmental Protection Agency, U.S. EPA Office of Research & Development (ORD) – National Health and Environmental Effects Research Laboratory (NHEERL), Corvallis, OR, 2010, U.S. EPA Office of Research & Development (ORD) – National Health and Environmental Effects Research Laboratory (NHEERL), https://www.epa.gov/eco-research/ecoregions-north-america (last access: November 2021), 2010.
Elias Chereque, A.: B-TIM snow for ERA5, V1, Borealis [data set], https://doi.org/10.5683/SP3/HHIRBU, 2024a.
Elias Chereque, A.: B-TIM snow for JRA55, V1, Borealis [data set], https://doi.org/10.5683/SP3/X5QJ3P, 2024b.
Elias Chereque, A.: B-TIM snow for MERRA2, V1, Borealis [data set], https://doi.org/10.5683/SP3/C5I5HN, 2024c.
Elias Chereque, A., Kushner, P. J., Mudryk, L., Derksen, C., and Mortimer, C.: A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products, The Cryosphere, 18, 4955–4969, https://doi.org/10.5194/tc-18-4955-2024, 2024.
Fang, Y., Liu, Y., and Margulis, S. A.: A western United States snow reanalysis dataset over the Landsat era from water years 1985 to 2021, Sci. Data, 9, 677, https://doi.org/10.1038/s41597-022-01768-7, 2022.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gesch, D. B., Evans, G. A., and Oimoen, M. J.: The National Elevation Dataset, in: Digital elevation model technologies and applications—the DEM users manual, 3rd edn., editted by: Maune, D. and Nayegandhi, A., American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, 2018.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_lnd_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/RKPHT8KC1Y1T (last access: December 2020), 2015.
Grünewald, T., Bühler, Y., and Lehning, M.: Elevation dependency of mountain snow depth, The Cryosphere, 8, 2381–2394, https://doi.org/10.5194/tc-8-2381-2014, 2014.
Hersbach, H., Bell, W., Berrisford, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P. Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47 (last access: December 2020), 2018, last updated 2024.
Japan Meteorological Agency/Japan: JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, https://doi.org/10.5065/D6HH6H41, 2013, updated monthly.
Johnson, J. B.: A theory of pressure sensor performance in snow, Hydrol. Process., 18, 53–64, https://doi.org/10.1002/hyp.1310, 2004.
Kelly, R., Li, Q., and Saberi, N.: The AMSR2 Satellite-Based Microwave Snow Algorithm (SMSA): A New Algorithm for Estimating Global Snow Accumulation, in: IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, 5606–5609, 28 July–2 August 2019, Yokohama, Japan, https://doi.org/10.1109/IGARSS.2019.8898525, 2019.
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.
Kirchner, P. B., Bales, R. C., Molotch, N. P., Flanagan, J., and Guo, Q.: LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California, Hydrol. Earth Syst. Sci., 18, 4261–4275, https://doi.org/10.5194/hess-18-4261-2014, 2014.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 reanalysis: General specifications and basic characteristics, J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Kodama, M., Nakai, K., Kawasaki, S., and Wada, M.: An application of cosmic-ray neutron measurements to the determination of the snow-water equivalent, J. Hydrol., 41, 85–92, https://doi.org/10.1016/0022-1694(79)90107-0, 1979.
Lehning, M., Gruenewald, T., and Schirmer, M.: Mountain snow distribution governed by an altitudinal gradient and terrain roughness, Geophys. Res. Lett., 38, L19504, https://doi.org/10.1029/2011GL048927, 2011.
Li, B., Rodell, M., Kumar, S., Beaudoing, H., Getirana, A., B. Zaitchik, de Goncalves, L., Cossetin, C., Bhanja, S., Mukherjee, A., Tian, S., Tangdamrongsub, N., Long, D., Nanteza, J., Lee, J., Policelli, F., Goni, I., Daira, D., Bila, M., de Lannoy, G., Mocko, D., Steele‐Dunne, S., Save, H., and Bettadpur, S.: Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges, Water Resour. Res., 55, 7564–7586, https://doi.org/10.1029/2018wr024618, 2019.
Li, B., Beaudoing, H., Rodell, M., and NASA/GSFC/HSL: GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree GRACE-DA1 V2.2, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/TXBMLX370XX8, 2020.
Liu, Y., Weerts, A. H., Clark, M., Hendricks Franssen, H.-J., Kumar, S., Moradkhani, H., Seo, D.-J., Schwanenberg, D., Smith, P., van Dijk, A. I. J. M., van Velzen, N., He, M., Lee, H., Noh, S. J., Rakovec, O., and Restrepo, P.: Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities, Hydrol. Earth Syst. Sci., 16, 3863–3887, https://doi.org/10.5194/hess-16-3863-2012, 2012.
Liu, Y., Fang, Y., Li, D., and Margulis, S. A.: How well do global snow products characterize snow water storage in High Mountain Asia?, Geophys. Res. Lett., 49, e2022GL100082, https://doi.org/10.1029/2022GL100082, 2022.
López-Moreno, J. I. and Stähli, M.: Statistical analysis of the snow cover variability in a subalpine watershed: Assessing the role of topography and forest interactions, J. Hydrol., 348(3-4): 379-394, https://doi.org/10.1016/j.jhydrol.2007.10.018, 2008.
López-Moreno, J. I., Fassnacht, S. R., Heath, J. T., Musselman, K. N., Revuelto, J., Latron, J., Morán-Tejeda, E., and Jonas, T.: Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent, Adv. Water Resour., 55, 40–52, https://doi.org/10.1016/j.advwatres.2012.08.010, 2013.
López-Moreno, J. I., Leppänen, L., Luks, B., Holko, L., Picard, G., Sanmiguel-Vallelado, A., Alonso-González, E., Finger, D. C., Arslan, A. N., Gillemot, K., Sensoy, A., Sorman, A., Ertaş, M. C., Fassnacht, S. R., Fierz, C., and Marty, C.: Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers, Hydrol. Process., 34, 3120–3133, https://doi.org/10.1002/hyp.13785, 2020.
Luojus, K., Moisander, M., Pulliainen, J., Takala, M., Lemmetyinen, J., Derksen, C., Mortimer, C., Schwaizer, G., and Nagler, T.: ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979–2018), version 1.0, Centre for Environmental Data Analysis [data set, https://doi.org/10.5285/fa20aaa2060e40cabf5fedce7a9716d0 (last access: April 2020), 2020.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Mortimer, C., Derksen, C., Mudryk, L., Moisander, M., Venäläinen, P., Hiltunen, M., Ikonen, J., Smolander, T., Cohen, J., Salminen, M., Veijola, K., and Norberg, J.: GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset, Sci. Data, 8, 163, https://doi.org/10.1038/s41597-021-00939-2, 2021.
Luojus, K., Moisander, M., Pulliainen, J., Takala, M., Lemmetyinen, J., Derksen, C., Mortimer, C., Schwaizer, G., Nagler, T., and Venäläinen, P.: ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979–2020), version 2.0, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/4647cc9ad3c044439d6c643208d3c494 (last access: November 2021), 2022.
Magnusson, J., Nævdal, G., Matt, F., Burkhart, J. F., and Winstral, A.: Improving hydropower inflow forecasts by assimilating snow data, Hydrol. Res., 51, 226–237, https://doi.org/10.2166/nh.2020.025, 2020.
Meromy, L., Molotch, N. P., Link, T. E., Fassnacht, S. R., and Rice, R.: Subgrid variability of snow water equivalent at operational snow stations in the western USA, Hydrol. Process., 27, 2383–2400, https://doi.org/10.1002/hyp.9355, 2013.
Mortimer, C. and Vionnet, V.: Northern Hemisphere historical in-situ Snow Water Equivalent dataset (1979–2021), version 1, Zenodo [data set], https://doi.org/10.5281/zenodo.10287093, 2024.
Mortimer, C., Mudryk, L., Derksen, C., Luojus, K., Brown, R., Kelly, R., and Tedesco, M.: Evaluation of long-term Northern Hemisphere snow water equivalent products, The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, 2020.
Mortimer, C., Mudryk, L., Derksen, C., Brady, M., Luojus, K., Venäläinen, P., Moisander, M., Lemmetyinen, J., Takala, M., Tanis, C., and Pulliainen, J.: Benchmarking algorithm changes to the Snow CCI+ snow water equivalent product, Remote Sens. Environ., 274, 112988, https://doi.org/10.1016/j.rse.2022.112988, 2022.
Mott, R., Schirmer, M., Bavay, M., Grünewald, T., and Lehning, M.: Understanding snow-transport processes shaping the mountain snow-cover, The Cryosphere, 4, 545–559, https://doi.org/10.5194/tc-4-545-2010, 2010.
Mott, R., Vionnet, V., and Grünewald, T.: The seasonal snow cover dynamics: review on wind-driven coupling processes, Front. Earth Sci., 6, 197, https://doi.org/10.3389/feart.2018.00197, 2018.
Mudryk, L., Elias Chereque, A., Derksen, C., Luojus, K., and Decharme, B.: Terrestrial Snow Cover, Arctic Report Card 2022, in: NOAA technical report OAR ARC, 22-03, edited by: Druckenmiller, M. L., Thoman, R. L., and Moon, T. A., United States National Oceanic and Atmospheric Administration, Office of Oceanic and Atmospheric Research, Global Ocean Monitoring and Observing (U.S.), https://doi.org/10.25923/yxs5-6c72, 2022.
Mudryk, L., Mortimer, C., Derksen, C., Elias Chereque, A., and Kushner, P.: Benchmarking of SWE products based on outcomes of the SnowPEx+ Intercomparison Project, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-3014, 2024.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac (last access: December 2020), 2019.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Ochi, K., de Rosnay, P., and Fairbin, D.: Impact of assimilating ESA CCI Snow Cover on ECMWF Land Reanalysis, 10th EARSeL workshop on Land Ice and Snow, Bern, Switzerland, 6–8 February 2023, http://www.earsel.org/SIG/Snow-Ice/files/ws2023/Poster/1_A_Ochi_POSTER.pdf (last access: October 2023), 2023.
Paquet, E., Laval, M., Basalaev, L. M., Belov, A., Eroshenko, E., Kartyshov, V., Struminsky, A., and Yanke, V.: An Application of Cosmic-Ray Neutron Measurements to the Determination of the Snow Water Equivalent, in: Proceedings of the 30th International Cosmic Ray Conference, Merida, Mexico, 3–11 July 2008, https://indico.nucleares.unam.mx/event/4/session/39/contribution/1000/material/paper/0.pdf (last access: August 2023), 2008.
Pomeroy, J. W., Gray, D. M., Shook, K. R., Toth, B., Essery, R. L. H., Pietroniro, A., and Hedstrom, N.: An evaluation of snow accumulation and ablation processes for land surface modelling, Hydrol. Process., 12, 2339–2367, https://doi.org/10.1002/(SICI)1099-1085(199812)12:15<2339::AID-HYP800>3.0.CO;2-L, 1998.
Pomeroy, J. W., Gray, D. M., Brown, T., Hedstrom, N. R., Quinton, W. L., Granger, R. J., and Carey, S. K.: The cold regions hydrological model: a platform for basing process representation and model structure on physical evidence, Hydrol. Process., 21, 2650–2667, https://doi.org/10.1002/hyp.6787, 2007.
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, https://doi.org/10.1038/s41586-020-2258-0, 2020.
Ralph, F. M., Dettinger, M., White, A., Reynolds, D., Cayan, D., Schneider, T., Cifelli, R., Redmond, K., Anderson, M., Gherke, F., Jones J., Mahoney, K., Johnson, L., Gutman, S., Chandrasekar, V., Lundquist, J., Molotch, N., Brekke, L., Pulwarty, R., Horel, J., Schick, L., Edman, A., Mote, P., Abatzoglou, J., Pierce, R., and Wick, G.: A vision for future observations for western US extreme precipitation and flooding, Journal of Contemporary Water Research & Education, 153, 16–32, https://doi.org/10.1111/j.1936-704X.2014.03176.x, 2014.
Rohrer, M., Braun, L., and Lang, H.: Long-Term Records of Snow Cover Water Equivalent in the Swiss Alps 1. Analysis, Nord. Hydrol., 25, 53–64, 1994.
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.
Snauffer, A. M., Hsieh, W. W., and Cannon, A. J.: Comparison of gridded snow water equivalent products with in situ measurements in British Columbia, Canada, J. Hydrol., 541, 714–729, https://doi.org/10.1016/j.jhydrol.2016.07.027, 2016.
Snethlage, M. A., Geschke, J., Spehn, E. M., Ranipeta, A., Yoccoz, N. G., Körner, Ch., Jetz, W., Fischer, M., and Urbach, D.: A hierarchical inventory of the world's mountains for global comparative mountain science, Sci. Data, 9, 149, https://doi.org/10.1038/s41597-022-01256-y, 2022a.
Snethlage, M. A., Geschke, J., Spehn, E. M., Ranipeta, A., Yoccoz, N. G., Körner, Ch., Jetz, W., Fischer, M., and Urbach, D.: GMBA Mountain Inventory v2, GMBA-EarthEnv. [data set], https://doi.org/10.48601/earthenv-t9k2-1407 (last access: June 2023), 2022b.
Sospedra-Alfonso, R., Mudryk, L., Merryfield, W., and Derksen, C.: Representation of snow in the Canadian seasonal to interannual prediction system. Part I: Initialization, J. Hydrometeorol., 17, 1467–1488, https://doi.org/10.1175/JHM-D-14-0223.1, 2016.
Terzago, S., von Hardenberg, J., Palazzi, E., and Provenzale, A.: Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models, The Cryosphere, 11, 1625–1645, https://doi.org/10.5194/tc-11-1625-2017, 2017.
Turcotte, R., Fortin, L. G., Fortin, V., Fortin, J. P., and Villeneuve, J. P.: Operational analysis of the spatial distribution and the temporal evolution of the snowpack water equivalent in southern Québec, Canada, Nord. Hydrol., 38, 211–234, https://doi.org/10.2166/nh.2007.009, 2007.
Tuttle, S. E., Jacobs, J. M., Vuyovich, C. M., Olheiser, C., and Cho, E.: Intercomparison of snow water equivalent observations in the Northern Great Plains, Hydrol. Process., 32, 817–829, https://doi.org/10.1002/hyp.11459, 2018.
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.
Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020), Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, 2021a.
Vionnet, V., Marsh, C. B., Menounos, B., Gascoin, S., Wayand, N. E., Shea, J., Mukherjee, K., and Pomeroy, J. W.: Multi-scale snowdrift-permitting modelling of mountain snowpack, The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, 2021b.
Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2021) (Version v3), Zenodo [data set], https://doi.org/10.5281/zenodo.5889352, 2022.
WMO (Ed.): Guide to instruments and methods of observation: Volume II – Measurement of Cryospheric Variables, 2018th edn., World Meteorological Organization, Geneva, WMO-No. 8, 52 pp., 2018.
Wood, A. W., T. Hopson, A. Newman, L. Brekke, J. Arnold, 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, 2019.
Zeng, X., P. Broxton, and Dawson, N.: Snowpack Change From 1982 to 2016 Over Conterminous United States, Geophys. Res. Lett., 45, 12940–12947, https://doi.org/10.1029/2018GL079621, 2018.
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.
Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of...
