Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-233-2023
© 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/gmd-16-233-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Jan 2023
Development and technical paper |
| 10 Jan 2023
| 10 Jan 2023
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Department of Geography, University of Utah, Salt Lake City, UT, USA
John Horel
Department of Atmospheric Sciences, University of Utah, Salt Lake
City, UT, USA
Patrick Kormos
Colorado Basin River Forecast Center NOAA National Weather Service,
Salt Lake City, UT, USA
Andrew Hedrick
Northwest Watershed Research Center, USDA Agricultural Research
Service, Boise, ID, USA
Ernesto Trujillo
Department of Geosciences, Boise State University, Boise, ID, USA
Northwest Watershed Research Center, USDA Agricultural Research
Service, Boise, ID, USA
S. McKenzie Skiles
Department of Geography, University of Utah, Salt Lake City, UT, USA
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
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Seasonally accumulated snow in the mountains forms a natural water reservoir which is challenging to measure in the rugged and remote terrain. Here, we use overlapping aerial images that model surface elevations using software to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the utility of aerial images to improve our ability to capture the amount of water held as snow in remote and inaccessible locations.
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Snow that accumulates seasonally in mountains forms a natural water reservoir and is difficult to measure in the rugged and remote landscapes. Here, we use modern software that models surface elevations from overlapping aerial images to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the potential value of aerial images for understanding the amount of water held as snow in remote and inaccessible locations.
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
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Snow grain size is important to determine the age and structure of snow, but it is difficult to measure. Snow grain size can be found from airborne and spaceborne observations by measuring near-infrared energy reflected from snow. In this study, we use the SNICAR radiative transfer model and a Monte Carlo model to examine how snow grain size measurements change with snow structure and solar zenith angle. We show that improved understanding of these variables improves snow grain size precision.
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Christopher Donahue, S. McKenzie Skiles, and Kevin Hammonds
The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, https://doi.org/10.5194/tc-16-43-2022, 2022
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The amount of water within a snowpack is important information for predicting snowmelt and wet-snow avalanches. From within a controlled laboratory, the optimal method for measuring liquid water content (LWC) at the snow surface or along a snow pit profile using near-infrared imagery was determined. As snow samples melted, multiple models to represent wet-snow reflectance were assessed against a more established LWC instrument. The best model represents snow as separate spheres of ice and water.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Joachim Meyer, McKenzie Skiles, Jeffrey Deems, Kat Boremann, and David Shean
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-281, https://doi.org/10.5194/hess-2021-281, 2021
Revised manuscript not accepted
Short summary
Short summary
Seasonally accumulated snow in the mountains forms a natural water reservoir which is challenging to measure in the rugged and remote terrain. Here, we use overlapping aerial images that model surface elevations using software to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the utility of aerial images to improve our ability to capture the amount of water held as snow in remote and inaccessible locations.
Joachim Meyer, S. McKenzie Skiles, Jeffrey Deems, Kat Bormann, and David Shean
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-34, https://doi.org/10.5194/tc-2021-34, 2021
Manuscript not accepted for further review
Short summary
Short summary
Snow that accumulates seasonally in mountains forms a natural water reservoir and is difficult to measure in the rugged and remote landscapes. Here, we use modern software that models surface elevations from overlapping aerial images to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the potential value of aerial images for understanding the amount of water held as snow in remote and inaccessible locations.
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Short summary
Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction...
