Articles | Volume 17, issue 9
https://doi.org/10.5194/tc-17-3915-2023
© Author(s) 2023. 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-17-3915-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Sep 2023
Research article |
| 12 Sep 2023
| 12 Sep 2023
Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland,
College Park, MD, USA
Carrie M. Vuyovich
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Sujay V. Kumar
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Melissa L. Wrzesien
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland,
College Park, MD, USA
Rhae Sung Kim
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Goddard Earth Sciences Technology and Research II, University of
Maryland, Baltimore County, Baltimore, MD, USA
current address: Earth Prediction Innovation Center, National
Oceanic and Atmospheric Administration, Silver Spring, MD, and Science and
Technology Corporation, Hampton, VA, USA
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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
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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.
Eunsang Cho, Megan Verfaillie, Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
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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.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
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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.
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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.
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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.
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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.
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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.
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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).
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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
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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.
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We use model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutant budgets in this area as local anthropogenic emissions drop.
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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.
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The Cryosphere, 18, 5619–5639, https://doi.org/10.5194/tc-18-5619-2024, https://doi.org/10.5194/tc-18-5619-2024, 2024
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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.
Peyman Abbaszadeh, Fadji Zaouna Maina, Chen Yang, Dan Rosen, Sujay Kumar, Matthew Rodell, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-280, https://doi.org/10.5194/hess-2024-280, 2024
Revised manuscript under review for HESS
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To manage Earth's water resources effectively amid climate change, it's crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabrielle J. M. De Lannoy
EGUsphere, https://doi.org/10.2139/ssrn.4974019, https://doi.org/10.2139/ssrn.4974019, 2024
Preprint archived
Short summary
Short summary
This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
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
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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.
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
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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
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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.
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.
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.
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
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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.
Amy McNally, Jossy Jacob, Kristi Arsenault, Kimberly Slinski, Daniel P. Sarmiento, Andrew Hoell, Shahriar Pervez, James Rowland, Mike Budde, Sujay Kumar, Christa Peters-Lidard, and James P. Verdin
Earth Syst. Sci. Data, 14, 3115–3135, https://doi.org/10.5194/essd-14-3115-2022, https://doi.org/10.5194/essd-14-3115-2022, 2022
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The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams described here generate routine estimates of snow, soil moisture, runoff, and other variables useful for tracking water availability. These data are hosted by NASA and USGS data portals for public use.
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022, https://doi.org/10.5194/acp-22-7461-2022, 2022
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This study demonstrates that ozone dry-deposition modeling can be improved by revising the model's dry-deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
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
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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.
Jawairia A. Ahmad, Barton A. Forman, and Sujay V. Kumar
Hydrol. Earth Syst. Sci., 26, 2221–2243, https://doi.org/10.5194/hess-26-2221-2022, https://doi.org/10.5194/hess-26-2221-2022, 2022
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Assimilation of remotely sensed data into a land surface model to improve the spatiotemporal estimation of soil moisture across South Asia exhibits potential. Satellite retrieval assimilation corrects biases that are generated due to an unmodeled hydrologic phenomenon, i.e., irrigation. The improvements in fine-scale, modeled soil moisture estimates by assimilating coarse-scale retrievals indicates the utility of the described methodology for data-scarce regions.
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.
Min Huang, James H. Crawford, Joshua P. DiGangi, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Xiwu Zhan
Atmos. Chem. Phys., 21, 11013–11040, https://doi.org/10.5194/acp-21-11013-2021, https://doi.org/10.5194/acp-21-11013-2021, 2021
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This study evaluates the impact of satellite soil moisture data assimilation on modeled weather and ozone fields at various altitudes above the southeastern US during the summer. It emphasizes the importance of soil moisture in the understanding of surface ozone pollution and upper tropospheric chemistry, as well as air pollutants’ source–receptor relationships between the US and its downwind areas.
Michiel Maertens, Gabriëlle J. M. De Lannoy, Sebastian Apers, Sujay V. Kumar, and Sarith P. P. Mahanama
Hydrol. Earth Syst. Sci., 25, 4099–4125, https://doi.org/10.5194/hess-25-4099-2021, https://doi.org/10.5194/hess-25-4099-2021, 2021
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In this study, we simulated the water balance over the South American Dry Chaco and assessed the impact of land cover changes thereon using three different land surface models. Our simulations indicated that different models result in a different partitioning of the total water budget, but all showed an increase in soil moisture and percolation over the deforested areas. We also found that, relative to independent data, no specific land surface model is significantly better than another.
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
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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
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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.
Yifan Zhou, Benjamin F. Zaitchik, Sujay V. Kumar, Kristi R. Arsenault, Mir A. Matin, Faisal M. Qamer, Ryan A. Zamora, and Kiran Shakya
Hydrol. Earth Syst. Sci., 25, 41–61, https://doi.org/10.5194/hess-25-41-2021, https://doi.org/10.5194/hess-25-41-2021, 2021
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South and Southeast Asia face significant food insecurity and hydrological hazards. Here we introduce a South and Southeast Asia hydrological monitoring and sub-seasonal to seasonal forecasting system (SAHFS-S2S) to help local governments and decision-makers prepare for extreme hydroclimatic events. The monitoring system captures soil moisture variability well in most regions, and the forecasting system offers skillful prediction of soil moisture variability 2–3 months in advance, on average.
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
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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.
Cited articles
Arnold, C. and Dey, C.: Observing-systems simulation experiments: Past,
present, and future, B. Am. Meteor. Soc., 67, 687–695,
https://doi.org/10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2.,
1986.
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, 2005.
Borah, F. K., Tsang, L., Kang, D. K., Kim, E., Siqueira, P., Barros, A., and Durand, M.: Data Analysis and SWE Retrieval of Airborne SAR Data AT X Band and KU Bands, in: 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17–22 July 2022, 4252–4255, https://doi.org/10.1109/IGARSS46834.2022.9884965, 2022.
Bormann, K. J., Brown, R. D., Derksen, C., and Painter, T. H.: Estimating
snow-cover trends from space, Nat. Clim. Change, 8, 924–928, 2018.
Carroll, S. S., Carroll, T. R., and Poston, R. W.: Spatial modeling and
prediction of snow-water equivalent using ground-based, air-borne, and
satellite snow data, J. Geophys. Res., 104,
19623–19629, https://doi.org/10.1029/1999JD900093, 1999.
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.
Cho, E., Tuttle, S. E., and Jacobs, J. M.: Evaluating consistency of snow
water equivalent retrievals from passive microwave sensors over the north
central US: SSM/I vs. SSMIS and AMSR-E vs. AMSR2, Remote Sensing, 9, 465,
https://doi.org/10.3390/rs9050465, 2017.
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,
2020.
Cho, E., Kwon, Y., Kumar, S. V., and Vuyovich, C. M.: Assimilation of airborne gamma observations provides utility for snow estimation in forested environments, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2022-332, in review, 2022a.
Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., Kim, R. S., and Jacobs, J. M.: Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent, Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, 2022b.
Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., and Kim, R. S.: Data for ”Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments”, HydroShare, http://www.hydroshare.org/resource/1ad0d4b62c4440e9bb9267a7470d7b81 (last access: 7 September 2023), 2023.
Crow, W. T., Chan, S. T. K., Entekhabi, D., Houser, P. R., Hsu, A. Y., Jackson,
T. J., Njoku, E. G., O'Neill, P. E., Shi, J., and Zhan, X.: An observing system
simulation experiment for Hydros radiometer-only soil moisture
productsm IEEE T. Geosci. Remote Sens., 43, 1289–1303, 2005.
De Lannoy, G. J. M., Reichle, R. H., Houser, P. R., Arsenault, K. R.,
Verhoest, N. E. C., and Pauwels, V. R. N.: Satellite-Scale Snow Water
Equivalent Assimilation into a HighResolution Land Surface Model, J.
Hydrometeorol., 11, 352–369, https://doi.org/10.1175/2009JHM1192.1, 2010.
Derksen, C., Walker, A., and Goodison, B.: Evaluation of passive microwave
snow water equivalent retrievals across the boreal forest/tundra transition
of western Canada, Remote Sens. Environ., 96, 315–327,
https://doi.org/10.1016/j.rse.2005.02.014, 2005.
Derksen, C., Toose, P., Rees, A., Wang, L., English, M., Walker, A., and
Sturm, M.: Development of a tundra-specific snow water equivalent retrieval
algorithm for satellite passive microwave data, Remote Sens. Environ., 114,
1699–1709, https://doi.org/10.1016/j.rse.2010.02.019, 2010.
Derksen, C., Lemmetyinen, J., King, J., Belair, S., Garnaud, C., Lapointe,
M., Crevier, Y., Burbidge, G., and Siqueira, P.: A DualFrequency Ku-Band
Radar Mission Concept for Seasonal Snow, in: IGARSS 2019–2019 IEEE
International Geoscience and Remote Sensing Symposium, IEEE, Yokohama,
Japan, 28 July–2 August 2019, 5742–5744, https://doi.org/10.1109/IGARSS.2019.8898030,
2019.
Dong, J., Walker, J., and Houser, P.: Factors affecting remotely sensed snow
water equivalent uncertainty, Remote Sens. Environ., 97, 68–82,
https://doi.org/10.1016/j.rse.2005.04.010, 2005.
Dozier, J., Bair, E. H., and Davis, R. E.: Estimating the spatial
distribution of snow water equivalent in the world's mountains, Wiley
Interdisciplinary Reviews: Water, 3, 461–474,
https://doi.org/10.1002/wat2.1140, 2016.
Ek, M. B., Mitchell, K., and Lin, Y.: Implementation of Noah land surface
model advances in the National Centers for Environmental Prediction
operational mesoscale Eta model, J. Geophys. Res., 108, 8851,
https://doi.org/10.1029/2002JD003296, 2003.
Elder, K., Cline, D., Liston, G. E., and Armstrong, R.: NASA Cold Land
Processes Experiment (CLPX 2002/03): field measurements of snowpack
properties and soil moisture, J. Hydrometeorol., 10, 320–329,
https://doi.org/10.1175/2008jhm877.1, 2009.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, 1–33,
https://doi.org/10.1029/2005rg000183, 2007.
Foster, J. L., Sun, C., Walker, J. P., Kelly, R., Chang, A., Dong, J., and
Powell, H.: Quantifying the uncertainty in passive microwave snow water
equivalent observations, Remote Sens. Environ., 94, 187–203, 2005.
Enzminger, T. L., Small, E. E., and Borsa, A. A.: Subsurface water dominates
Sierra Nevada seasonal hydrologic storage, Geophys. Res. Lett., 46,
11993–12001, 2019.
Garnaud, C., Bélair, S., Carrera, M. L., Derksen, C., Bilodeau, B.,
Abrahamowicz, M., Gauthier, N., and Vionnet, V.: Quantifying Snow Mass
Mission Concept Trade-Offs Using an Observing System Simulation Experiment,
J. Hydrometeorol., 20, 155–173, https://doi.org/10.1175/JHM-D-17-0241.1,
2019.
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. K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective
analysis for research and applications, version 2 (MERRA2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017 (data available at: https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/, last access: 1 October 2022).
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A.,
Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R.,
Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R. G.:
High-Resolution Global Maps of 21st-Century Forest Cover Change, Science,
342, 850–853, 2013 (data available at: https://glad.umd.edu/Potapov/TCC_2010/, last access: 1 October 2022).
He, C., Chen, F., Barlage, M., Liu, C., Newman, A., Tang, W., Ikeda, K., and
Rasmussen, R.: Can Convection-Permitting Modeling Provide Decent
Precipitation for Offline High-Resolution Snowpack Simulations Over
Mountains?, J. Geophys. Res.-Atmos., 124, 12631–12654,
https://doi.org/10.1029/2019JD030823, 2019.
Henn, B., Newman, A. J., Livneh, B., Daly, C., and Lundquist, J. D.: An
assessment of differences in gridded precipitation datasets in complex
terrain, J. Hydrol., 556, 1205–1219,
https://doi.org/10.1016/j.jhydrol.2017.03.008, 2018.
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch,
T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M.,
Fernández, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink,
P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter,
T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A.
B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.:
Importance and vulnerability of the world's water towers, Nature, 577,
364–369, https://doi.org/10.1038/s41586-019-1822-y, 2019.
Jordan, R. E.: A one-dimensional temperature model for a snow cover:
Technical documentation for SNTHERM, 89,
https://erdc-library.erdc.dren.mil/jspui/bitstream/11681/11677/1/SR-91-16.pdf (last access: 1 October 2022),
1991.
Kang, D. H., Barros, A. P., and Dery, S. J.: Evaluating passive microwave
radiometry for the dynamical transition from dry to wet snowpacks, IEEE
T. Geosci. Remote Sens., 52, 3–15, 2013.
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.
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.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J.,
Olden, S., Lighty, L., Eastman, J. L., Doty, B., and Dirmeyer, P.: Land
information system: An interoperable framework for high resolution land
surface modeling, Environ. Model. Softw., 21, 1402–1415, 2006 (code available at: https://github.com/NASA-LIS/LISF, last access: 1 July 2023).
Kumar, S. V., Reichle, R. H., Harrison, K. W., Peters-Lidard, C. D.,
Yatheendradas, S., and Santanello, J. A.: A comparison of methods for a
priori bias correction in soil moisture data assimilation, Water Resour.
Res., 48, W03515, https://doi.org/10.1029/2010WR010261, 2012.
Kumar, S. V., Harrison, K. W., Peters-Lidard, C. D., Santanello Jr., J. A.,
and Kirschbaum, D.: Assessing the impact of L-band observations on drought
and flood risk estimation: A decision-theoretic approach in an OSSE
environment, J. Hydrometeorol., 15, 2140–2156, 2014a.
Kumar, S. V., Peters-Lidard, C. D., Mocko, D., Reichle, R., Liu, Y.,
Arsenault, K. R., Xia, Y., Ek, M., Riggs, G., Livneh, B., and Cosh, M.:
Assimilation of remotely sensed soil moisture and snow depth retrievals for
drought estimation, J. Hydrometeorol., 15, 2446–2469, 2014b.
Kumar, S. V., Peters-Lidard, C. D., Arsenault, K. R., Getirana, A., Mocko,
D., and Liu, Y.: Quantifying the added value of snow cover area
observations in passive microwave snow depth assimilation, J.
Hydrometeorol., 16, 1736–1741,
https://doi.org/10.1175/JHM-D-15-0021.1, 2015.
Kwon, Y., Yoon, Y., Forman, B. A., Kumar, S. V., and Wang, L.: Quantifying
the observational requirements of a space-borne LiDAR snow mission, J.
Hydrol., 601, 126709, https://doi.org/10.1016/j.jhydrol.2021.126709, 2021.
Lahmers, T. M., Kumar, S. V., Rosen, D., Dugger, A., Gochis, D. J.,
Santanello, J. A., Gangodagamage, C., and Dunlap, R.: Assimilation of
NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological
Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System, Water
Resour. Res., 58, e2021WR029867, https://doi.org/10.1029/2021WR029867, 2022.
Larue, F., Royer, A., De Sève, D., Langlois, A., Roy, A., and Brucker,
L.: Validation of GlobSnow-2 snow water equivalent over Eastern Canada,
Remote Sens. Environ., 194, 264–277,
https://doi.org/10.1016/j.rse.2017.03.027, 2017.
Le Moigne, J., Dabney, P., de Weck, O., Foreman, V., Grogan, P., Holland,
M., Hughes, S., and Nag, S.: Tradespace analysis tool for designing
constellations (TAT-C), in: 2017 IEEE International Geoscience and Remote
Sensing Symposium (IGARSS), 1181–1184, 2017.
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and
Wood, E. F.: Inroads of remote sensing into hydrologic science during the
WRR era, Water Resour. Res., 51, 7309–7342,
https://doi.org/10.1002/2015WR017616, 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, 2017.
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.
Liston, G. E. and Sturm, M.: Global Seasonal-Snow Classification, Version 1, Boulder, Colorado USA, National Snow and Ice Data Center [data set], https://doi.org/10.5067/99FTCYYYLAQ0, 2021.
Macelloni, G., Paloscia, S., Pampaloni, P., and Tedesco, M.: Microwave
emission from dry snow: A comparison of experimental and model results, IEEE
T. Geosci. Remote Sens., 39, 2649–2656, 2001.
Magagi, R., Bernier, M., and Ung, C. H.: Quantitative analysis of RADARSAT
SAR data over a sparse forest canopy, IEEE T. Geosci. Remote
Sens., 40, 1301–1313, 2002.
Masutani, M., Woollen, J. S., Lord, S. J., Emmitt, G. D., Kleespies, T. J.,
Wood, S. A., Greco, S., Sun, H. B., Terry, J., Kapoor, V., Treadon, R., and
Campana, K. A.: Observing system simulation experiments at the National
Centers for Environmental Prediction, J. Geophys. Res.-Atmos., 115, D07101,
https://doi.org/10.1029/2009JD012528, 2010.
Mätzler, C.: Applications of the interaction of microwaves with the
natural snow cover, Remote Sens. Rev., 2, 259–387,
https://doi.org/10.1080/02757258709532086, 1987.
Minder, J. R., Letcher, T. W., and Skiles, S. M.: An evaluation of
high-resolution regional climate model simulations of snow cover and albedo
over the Rocky Mountains, with implications for the simulated snow-albedo
feedback, J. Geophys. Res.-Atmos., 121, 9069–9088,
https://doi.org/10.1002/2016JD024995, 2016.
Molotch, N. P. and Bales, R. C.: Scaling snow observations from the point to
the grid element: Implications for observation network design, Water Resour.
Res., 41, W11421, https://doi.org/10.1029/2005WR004229, 2005.
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, https://doi.org/10.1109/JSTARS.2015.2417999, 2015.
Nagler, T., Rott, Heidinger, M., Malcher, P., Macelloni, G., Pettinato, S.,
Santi, E., Essery, R., Pulliainen, J., Takal, M., Malnes, E., Storvold, R.,
Johnson, H., Haas, C., and Duguay, C.: Retrieval of physical snow properties
from SAR observations at Ku- and X-band frequencies, Final Report, ESTEC
contract, 20756(56), 07, 2008.
National Research Council: Earth Science and Applications from Space:
National Imperatives for the Next Decade and Beyond, Washington, DC: The
National Academies Press, https://doi.org/10.17226/11820, 2007.
Nearing, G. S., Crow, W. T., Thorp, K. R., Moran, M. S., Reichle, R. H., and
Gupta, H. V.: Assimilating remote sensing observations of leaf area index
and soil moisture for wheat yield estimates: An observing system simulation
experiment, Water Resour. Res., 48, W05525,
https://doi.org/10.1029/2011wr011420, 2012.
Niu, G. Y. and Yang, Z. L.: Effects of frozen soil on snowmelt runoff and
soil water storage at a continental scale, J. Hydrometeorol., 7, 937–952,
https://doi.org/10.1175/JHM538.1, 2006.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale
measurements, J. Geophys. Res.-Atmos., 116, D12109,
https://doi.org/10.1029/2010JD015139, 2011.
Peters-Lidard, C. D., Houser, P. R., Tian, Y., Kumar, S. V., Geiger, J.,
Olden, S., Lighty, L., Doty, B., Dirmeyer, P., Adams, J., and Mitchell, K.:
High-performance Earth system modeling with NASA/GSFC's Land Information
System, Innovations in Systems and Software Engineering, 3, 157–165, 2007.
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.
Raleigh, M. S., Lundquist, J. D., and Clark, M. P.: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework, Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015.
Reichle, R. H., McLaughlin, D. B., and Entekhabi, D.: Hydrologic data
assimilation with the ensemble Kalman filter, Mon. Weather Rev., 130,
103–114, 2002.
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, 2010.
Rutter, N., Essery, R., Pomeroy, J., Altimir, N., Andreadis, K., Baker, I.,
Barr, A., Bartlett, P., Boone, A., Deng, H., Douville, H., Dutra, E., Elder,
K., Ellis, C., Feng, X., Gelfan, A., Goodbody, A., Gusev, Y., Gustafsson,
D., Hellström, R., Hirabayashi, Y., Hirota, T., Jonas, T., Koren, V.,
Kuragina, A., Lettenmaier, D., Li, W.-P., Luce, C., Martin, E., Nasonova,
O., Pumpanen, J., Pyles, R. D., Samuelsson, P., Sandells, M., Schädler,
G., Shmakin, A., Smirnova, T. G., Stähli, M., Stöckli, R., Strasser,
U., Su, H., Suzuki, K., Takata, K., Tanaka, K., Thompson, E., Vesala, T.,
Viterbo, P., Wiltshire, A., Xia, K., Xue, Y., and Yamazaki, T.: Evaluation
of forest snow processes models (SnowMIP2), J. Geophys. Res.-Atmos., 114,
D06111, https://doi.org/10.1029/2008JD011063, 2009.
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.
Santi, E., De Gregorio, L., Pettinato, S., Cuozzo, G., Jacob, A., Notarnicola, C., Günther, D., Strasser, U., Cigna, F., Tapete, D., and Paloscia, S.: On the Use of COSMO-SkyMed X-Band SAR for Estimating Snow Water Equivalent in Alpine Areas: A Retrieval Approach Based on Machine Learning and Snow Models, IEEE T. Geosci. Remote, 60, 1–19, https://doi.org/10.1109/TGRS.2022.3191409, 2022.
Schmucki, E., Marty, C., Fierz, C., and Lehning, M.: Evaluation of modelled
snow depth and snow water equivalent at three contrasting sites in
Switzerland using SNOWPACK simulations driven by different meteorological
data input, Cold Reg. Sci. Technol., 99, 27–37,
https://doi.org/10.1016/j.coldregions.2013.12.004, 2014.
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., Goldstein, M. A., and Parr, C.: Water and life from snow:
A trillion dollar science question, Water Resour. Res., 53,
3534–3544, 2017.
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 spaceborne radiometer data and groundbased measurements, Remote Sens.
Environ., 115, 3517–3529, 2011.
Tsang, L., Durand, M., Derksen, C., Barros, A. P., Kang, D.-H., Lievens, H., Marshall, H.-P., Zhu, J., Johnson, J., King, J., Lemmetyinen, J., Sandells, M., Rutter, N., Siqueira, P., Nolin, A., Osmanoglu, B., Vuyovich, C., Kim, E., Taylor, D., Merkouriadi, I., Brucker, L., Navari, M., Dumont, M., Kelly, R., Kim, R. S., Liao, T.-H., Borah, F., and Xu, X.: Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing, The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, 2022.
Tedesco, M., Kim, E. J., Gasiewski, A., Klein, M., and Stankov, B.:
Analysis of multiscale radiometric data collected during the Cold Land
Processes Experiment-1 (CLPX-1), Geophys. Res. Lett., 32, https://doi.org/10.1029/2005GL023006, 2005.
USGS Earth Resources Observation and Science (EROS) Center: Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global, [data set],
https://doi.org/10.5066/F7PR7TFT, 2018.
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.
Vuyovich, C. M., Jacobs, J. M., and Daly, S. F.: Comparison of passive
microwave and modeled estimates of total watershed SWE in the continental
United States, Water Resour. Res., 50, 9088–9102,
https://doi.org/10.1002/2013WR014734, 2014.
Walker, A. and Goodison, B.: Discrimination of a wet snow cover using
passive microwave satellite data, Ann. Glaciol., 17, 307–311, 1993.
Wrzesien, M. L., Kumar, S., Vuyovich, C., Gutmann, E. D., Kim, R. S.,
Forman, B. A., Durand, M., Raleigh, M. S., Webb, R., and Houser, P.:
Development of a “nature run” for observing system simulation experiments
(OSSEs) for snow mission development, J. Hydrometeorol., 23,
351–375, 2022.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo,
L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V.,
Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy
flux analysis and validation for the North American Land Data Assimilation
System project phase 2 (NLDAS-2): 1. Intercomparison and application of
model products, J. Geophys. Res., 117, D03109,
https://doi.org/10.1029/2011JD016048, 2012.
Yang, Z.-L. and Dickinson, R. E.: Description of the BiosphereAtmosphere
Transfer Scheme (BATS) for the soil moisture workshop and evaluation of its
performance, Global Planet. Change, 13, 117–134, 1996.
Yang, Z. L., Niu, G. Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res.-Atmos., 116, D12, https://doi.org/10.1029/2010JD015139, 2011.
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.
Zeng, X., Broxton, P., 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.
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.
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.
As a future snow mission concept, active microwave sensors have the potential to measure snow...
