Articles | Volume 10, issue 1
https://doi.org/10.5194/essd-10-549-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-10-549-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hourly mass and snow energy balance measurements from Mammoth Mountain, CA USA, 2011–2017
Earth Research Institute, University of California, Santa Barbara, CA
93106-3060, USA
Robert E. Davis
Cold Regions Research and Engineering Laboratory, Hanover, NH 03755,
USA
Jeff Dozier
Bren School of Environmental Science & Management, University of
California, Santa Barbara, CA 93106-5131, USA
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Cited
21 citations as recorded by crossref.
- Direct Insertion of NASA Airborne Snow Observatory‐Derived Snow Depth Time Series Into the iSnobal Energy Balance Snow Model A. Hedrick et al. 10.1029/2018WR023190
- Impact of light-absorbing particles on snow albedo darkening and associated radiative forcing over high-mountain Asia: high-resolution WRF-Chem modeling and new satellite observations C. Sarangi et al. 10.5194/acp-19-7105-2019
- Particle Filter Data Assimilation of Monthly Snow Depth Observations Improves Estimation of Snow Density and SWE E. Smyth et al. 10.1029/2018WR023400
- An Examination of Snow Albedo Estimates From MODIS and Their Impact on Snow Water Equivalent Reconstruction E. Bair et al. 10.1029/2019WR024810
- Fine‐scale landscape phenology revealed through time‐lapse imagery: implications for conservation and management of an endangered migratory herbivore C. John et al. 10.1002/rse2.331
- A random forest approach to quality-checking automatic snow-depth sensor measurements G. Blandini et al. 10.5194/tc-17-5317-2023
- Snow Property Inversion From Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach With Examples From MODIS and Landsat 8 OLI E. Bair et al. 10.1109/TGRS.2020.3040328
- Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar W. Brandt et al. 10.1029/2019WR025331
- Watershed memory amplified the Oroville rain-on-snow flood of February 2017 K. Haleakala et al. 10.1093/pnasnexus/pgac295
- Meteorological control on snow depth evolution and snowpack energy exchanges in an agro-forested environment by a measurement-based approach: A case study in Sainte-Marthe, Eastern Canada V. Dharmadasa et al. 10.1016/j.agrformet.2024.109915
- Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan E. Bair et al. 10.5194/tc-14-331-2020
- Variable effects of forest canopies on snow processes in a valley of the central Spanish Pyrenees A. Sanmiguel‐Vallelado et al. 10.1002/hyp.13721
- Passive Microwave Brightness Temperature Assimilation to Improve Snow Mass Estimation Across Complex Terrain in Pakistan, Afghanistan, and Tajikistan J. Ahmad et al. 10.1109/JSTARS.2021.3102965
- Multi-sensor fusion using random forests for daily fractional snow cover at 30 m K. Rittger et al. 10.1016/j.rse.2021.112608
- Apparatus for dry deposition of aerosols on snow N. Beres & H. Moosmüller 10.5194/amt-11-6803-2018
- An improvement of snow/cloud discrimination from machine learning using geostationary satellite data D. Jin et al. 10.1080/17538947.2022.2152886
- MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area C. Liu et al. 10.3390/rs12060962
- Divergence of apparent and intrinsic snow albedo over a season at a sub-alpine site with implications for remote sensing E. Bair et al. 10.5194/tc-16-1765-2022
- Exploring microplastic distribution in Western North American snow A. Karapetrova et al. 10.1016/j.jhazmat.2024.136126
- Precipitation Estimates and Orographic Gradients Using Snow, Temperature, and Humidity Measurements From a Wireless‐Sensor Network G. Cui et al. 10.1029/2021WR029954
- Eleven years of mountain weather, snow, soil moisture and streamflow data from the rain–snow transition zone – the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA S. Godsey et al. 10.5194/essd-10-1207-2018
21 citations as recorded by crossref.
- Direct Insertion of NASA Airborne Snow Observatory‐Derived Snow Depth Time Series Into the iSnobal Energy Balance Snow Model A. Hedrick et al. 10.1029/2018WR023190
- Impact of light-absorbing particles on snow albedo darkening and associated radiative forcing over high-mountain Asia: high-resolution WRF-Chem modeling and new satellite observations C. Sarangi et al. 10.5194/acp-19-7105-2019
- Particle Filter Data Assimilation of Monthly Snow Depth Observations Improves Estimation of Snow Density and SWE E. Smyth et al. 10.1029/2018WR023400
- An Examination of Snow Albedo Estimates From MODIS and Their Impact on Snow Water Equivalent Reconstruction E. Bair et al. 10.1029/2019WR024810
- Fine‐scale landscape phenology revealed through time‐lapse imagery: implications for conservation and management of an endangered migratory herbivore C. John et al. 10.1002/rse2.331
- A random forest approach to quality-checking automatic snow-depth sensor measurements G. Blandini et al. 10.5194/tc-17-5317-2023
- Snow Property Inversion From Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach With Examples From MODIS and Landsat 8 OLI E. Bair et al. 10.1109/TGRS.2020.3040328
- Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar W. Brandt et al. 10.1029/2019WR025331
- Watershed memory amplified the Oroville rain-on-snow flood of February 2017 K. Haleakala et al. 10.1093/pnasnexus/pgac295
- Meteorological control on snow depth evolution and snowpack energy exchanges in an agro-forested environment by a measurement-based approach: A case study in Sainte-Marthe, Eastern Canada V. Dharmadasa et al. 10.1016/j.agrformet.2024.109915
- Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan E. Bair et al. 10.5194/tc-14-331-2020
- Variable effects of forest canopies on snow processes in a valley of the central Spanish Pyrenees A. Sanmiguel‐Vallelado et al. 10.1002/hyp.13721
- Passive Microwave Brightness Temperature Assimilation to Improve Snow Mass Estimation Across Complex Terrain in Pakistan, Afghanistan, and Tajikistan J. Ahmad et al. 10.1109/JSTARS.2021.3102965
- Multi-sensor fusion using random forests for daily fractional snow cover at 30 m K. Rittger et al. 10.1016/j.rse.2021.112608
- Apparatus for dry deposition of aerosols on snow N. Beres & H. Moosmüller 10.5194/amt-11-6803-2018
- An improvement of snow/cloud discrimination from machine learning using geostationary satellite data D. Jin et al. 10.1080/17538947.2022.2152886
- MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area C. Liu et al. 10.3390/rs12060962
- Divergence of apparent and intrinsic snow albedo over a season at a sub-alpine site with implications for remote sensing E. Bair et al. 10.5194/tc-16-1765-2022
- Exploring microplastic distribution in Western North American snow A. Karapetrova et al. 10.1016/j.jhazmat.2024.136126
- Precipitation Estimates and Orographic Gradients Using Snow, Temperature, and Humidity Measurements From a Wireless‐Sensor Network G. Cui et al. 10.1029/2021WR029954
- Eleven years of mountain weather, snow, soil moisture and streamflow data from the rain–snow transition zone – the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA S. Godsey et al. 10.5194/essd-10-1207-2018
Latest update: 14 Dec 2024
Short summary
The mass and energy balance of the snowpack govern its evolution. Here, we present a fully filtered and model-ready dataset containing a continuous hourly record of selected measurements from three sites on Mammoth Mountain, CA USA. These measurements can be used to run a variety of snow models and complement a previously published dataset. In addition to the hand-weighed snow water equivalent, novel measurements include hourly snow albedo corrected for terrain and other measurement biases.
The mass and energy balance of the snowpack govern its evolution. Here, we present a fully...
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