Articles | Volume 18, issue 2
https://doi.org/10.5194/tc-18-747-2024
© 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-18-747-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17
Siddharth Singh
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
Michael Durand
School of Earth Sciences, Ohio State University, Columbus, Ohio, USA
Edward Kim
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Ana P. Barros
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
Related authors
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Mochi Liao and Ana Barros
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-554, https://doi.org/10.5194/essd-2025-554, 2025
Preprint under review for ESSD
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The StageIV-IRC is the first precipitation dataset developed for extreme precipitation events in the mountains. This dataset strongly suggest the use of Inverse Rainfall Correction (IRC) framework to produce physically-meaningful corrections for precipitation products in the mountains, where precipitation estimation is problematic due to topography blockage. Post-IRC precipitation estimation produces improved hydrological responses, and it shows a good agreement with raingauge observations.
Prabhakar Shrestha and Ana P. Barros
The Cryosphere, 19, 2895–2911, https://doi.org/10.5194/tc-19-2895-2025, https://doi.org/10.5194/tc-19-2895-2025, 2025
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The study presents the first assimilation of snow depth obtained from repeat pass airborne L-band synthetic aperture radar with a snow hydrology model. The assimilation of snow depth was found to be equivalent to the downscaling of precipitation forcing with a bias correction, which improved the snowpack simulation compared to ground-based observations.
Mochi Liao and Ana Barros
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-513, https://doi.org/10.5194/essd-2024-513, 2025
Manuscript not accepted for further review
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This StageIV-IRC is the first rainfall dataset aiming to close the water budget for flood events, consistent with fundamental physics at basin scale, and achieving superior hydrological performance at fine scale (<1hr, <1km) in headwater basins. It shows greatly-enhanced, topography-aligned rainfall spatial variability, yielding a median KGE of 0.86, with flood timing errors <1hr. This dataset can be used in operational hydrology to improve precipitation forecasts, advancing flood forecasting.
Firoz Kanti Borah, Jonas-Fredrick Jans, Zhenming Huang, Leung Tsang, Hans Lievens, and Edward Kim
EGUsphere, https://doi.org/10.5194/egusphere-2024-1825, https://doi.org/10.5194/egusphere-2024-1825, 2024
Preprint archived
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In this paper, we study radar data collected by Sentinel-1 over mountain regions of Alps. Using physical models of snow and soil surface scattering, we show the reasons for the high sensitivity of cross-polarized observations with snow depth. This accurate modelling for cross-pol using physical models can be then used to retrieve snow depth at for very deep snow at mountain regions using the cross-pol signal.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
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We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
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Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Luiz Bacelar, Arezoo ReifeeiNasab, Nathaniel Chaney, and Ana Barros
EGUsphere, https://doi.org/10.5194/egusphere-2023-2088, https://doi.org/10.5194/egusphere-2023-2088, 2023
Preprint archived
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The study explores a computationally efficient probabilistic precipitation forecast approach to generate multiple flood scenarios. It reveals the limitations in predicting flash floods accurately and the need for advanced ensemble methodologies to combine different sources of precipitation forecasts. It highlights the scale-dependency of flood predictions at higher spatial resolutions, shedding light on the relationship between river hydraulics and flood propagation in the river network.
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.
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.
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Short summary
Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is...