Articles | Volume 18, issue 8
https://doi.org/10.5194/tc-18-3495-2024
© 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-18-3495-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Aug 2024
Research article |
| 08 Aug 2024
| 08 Aug 2024
Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne lidar data
Jordan N. Herbert
CORRESPONDING AUTHOR
Department of Geological Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
Mark S. Raleigh
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
Eric E. Small
Department of Geological Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
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Cited
18 citations as recorded by crossref.
- Leveraging snow probe data, lidar, and machine learning for snow depth estimation in complex-terrain environments D. Liljestrand et al. https://doi.org/10.5194/tc-19-3123-2025
- Airborne lidar and machine learning reveal decreased snow depth in burned forests A. Koshkin & A. Marshall https://doi.org/10.5194/tc-20-3467-2026
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
- High-resolution mountain topography can inform global snow vulnerability estimates A. Marshall et al. https://doi.org/10.1088/1748-9326/ae2d73
- Sensitivity of river flow regime to snow water storage variability across mid˗latitude region in Eastern Europe U. Somorowska https://doi.org/10.1016/j.scitotenv.2025.180160
- Snow monitoring at strategic locations improves water supply forecasting more than basin-wide mapping M. Raleigh et al. https://doi.org/10.1038/s43247-025-02660-z
- Assessment of sentinel-1 c-band SAR snow depth assimilation in operational hydrological modeling of high-alpine catchments P. T et al. https://doi.org/10.1016/j.jhydrol.2026.135888
- A New Method to Simulate the Microwave Effective Snow Grain Size in the Northern Hemisphere Without Using Snow Depth Priors J. Yang et al. https://doi.org/10.1109/JSTARS.2024.3441817
- Improved modelling of mountain snowpacks with spatially distributed precipitation bias correction derived from historical reanalysis M. von Kaenel & S. Margulis https://doi.org/10.5194/tc-19-3309-2025
- Evaluation of the Snow Climate Change Initiative (Snow CCI) snow-covered area product within a mountain snow water equivalent reanalysis H. Sun et al. https://doi.org/10.5194/tc-19-2017-2025
- Impact of current and warmer climate conditions on snow cover loss in burned forests A. Koshkin et al. https://doi.org/10.1126/sciadv.adt9866
- Informing snow measurement site selection with remote sensing and local ecological knowledge: A case study in Oregon H. Steele et al. https://doi.org/10.1016/j.rsase.2026.101912
- Cryosphere Remote Sensing Using Multisource Satellite Data: Sensor Technologies, Current Status, Challenges, and Opportunities J. Bu et al. https://doi.org/10.1109/JSTARS.2026.3690565
- Evaluating the utility of Sentinel-1 in a Data Assimilation System for estimating snow depth in a mountainous basin B. Mirza et al. https://doi.org/10.5194/tc-19-6691-2025
- Advancing snow data assimilation with a dynamic observation uncertainty D. Dunmire et al. https://doi.org/10.5194/tc-20-609-2026
- Scale patterns of the Sentinel-1 SAR-based snow depth product compared with station measurements and airborne LiDAR observations J. Ying et al. https://doi.org/10.5194/tc-20-227-2026
- A New Operational Northern Hemisphere Snow Water Equivalent Retrieval Algorithm for FY-3F/MWRI-II Based on Pixel-Based Regression Coefficients J. Yang et al. https://doi.org/10.1109/TGRS.2024.3479452
- Uncertainty Analysis of Snow Depth Retrieval Products over China via the Triple Collocation Method and Ground-Based Measurements J. Yang et al. https://doi.org/10.3390/rs17173036
18 citations as recorded by crossref.
- Leveraging snow probe data, lidar, and machine learning for snow depth estimation in complex-terrain environments D. Liljestrand et al. https://doi.org/10.5194/tc-19-3123-2025
- Airborne lidar and machine learning reveal decreased snow depth in burned forests A. Koshkin & A. Marshall https://doi.org/10.5194/tc-20-3467-2026
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
- High-resolution mountain topography can inform global snow vulnerability estimates A. Marshall et al. https://doi.org/10.1088/1748-9326/ae2d73
- Sensitivity of river flow regime to snow water storage variability across mid˗latitude region in Eastern Europe U. Somorowska https://doi.org/10.1016/j.scitotenv.2025.180160
- Snow monitoring at strategic locations improves water supply forecasting more than basin-wide mapping M. Raleigh et al. https://doi.org/10.1038/s43247-025-02660-z
- Assessment of sentinel-1 c-band SAR snow depth assimilation in operational hydrological modeling of high-alpine catchments P. T et al. https://doi.org/10.1016/j.jhydrol.2026.135888
- A New Method to Simulate the Microwave Effective Snow Grain Size in the Northern Hemisphere Without Using Snow Depth Priors J. Yang et al. https://doi.org/10.1109/JSTARS.2024.3441817
- Improved modelling of mountain snowpacks with spatially distributed precipitation bias correction derived from historical reanalysis M. von Kaenel & S. Margulis https://doi.org/10.5194/tc-19-3309-2025
- Evaluation of the Snow Climate Change Initiative (Snow CCI) snow-covered area product within a mountain snow water equivalent reanalysis H. Sun et al. https://doi.org/10.5194/tc-19-2017-2025
- Impact of current and warmer climate conditions on snow cover loss in burned forests A. Koshkin et al. https://doi.org/10.1126/sciadv.adt9866
- Informing snow measurement site selection with remote sensing and local ecological knowledge: A case study in Oregon H. Steele et al. https://doi.org/10.1016/j.rsase.2026.101912
- Cryosphere Remote Sensing Using Multisource Satellite Data: Sensor Technologies, Current Status, Challenges, and Opportunities J. Bu et al. https://doi.org/10.1109/JSTARS.2026.3690565
- Evaluating the utility of Sentinel-1 in a Data Assimilation System for estimating snow depth in a mountainous basin B. Mirza et al. https://doi.org/10.5194/tc-19-6691-2025
- Advancing snow data assimilation with a dynamic observation uncertainty D. Dunmire et al. https://doi.org/10.5194/tc-20-609-2026
- Scale patterns of the Sentinel-1 SAR-based snow depth product compared with station measurements and airborne LiDAR observations J. Ying et al. https://doi.org/10.5194/tc-20-227-2026
- A New Operational Northern Hemisphere Snow Water Equivalent Retrieval Algorithm for FY-3F/MWRI-II Based on Pixel-Based Regression Coefficients J. Yang et al. https://doi.org/10.1109/TGRS.2024.3479452
- Uncertainty Analysis of Snow Depth Retrieval Products over China via the Triple Collocation Method and Ground-Based Measurements J. Yang et al. https://doi.org/10.3390/rs17173036
Saved (final revised paper)
Latest update: 17 Jul 2026
Short summary
Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
Automated stations measure snow properties at a single point but are frequently used to validate...
