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Modeling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests
Nousu, J. P., Lafaysse, M., Mazzotti, G., Ala-Aho, P., Marttila, H., Cluzet, B., … Launiainen, S. (2024). Modeling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests. Cryosphere, 18(1), 231-263. https://doi.org/10.5194/tc-18-231-2024
The benefits of homogenising snow depth series - impacts on decadal trends and extremes for Switzerland
Buchmann, M., Resch, G., Begert, M., Brönnimann, S., Chimani, B., Schöner, W., & Marty, C. (2023). The benefits of homogenising snow depth series - impacts on decadal trends and extremes for Switzerland. Cryosphere, 17(2), 653-671. https://doi.org/10.5194/tc-17-653-2023
Temporospatial variability of snow's thermal conductivity on Arctic sea ice
MacFarlane, A. R., Löwe, H., Gimenes, L., Wagner, D. N., Dadic, R., Ottersberg, R., … Schneebeli, M. (2023). Temporospatial variability of snow's thermal conductivity on Arctic sea ice. Cryosphere, 17(12), 5417-5434. https://doi.org/10.5194/tc-17-5417-2023
Impact of the sampling procedure on the specific surface area of snow measurements with the IceCube
Martin, J., & Schneebeli, M. (2023). Impact of the sampling procedure on the specific surface area of snow measurements with the IceCube. Cryosphere, 17(4), 1723-1734. https://doi.org/10.5194/tc-17-1723-2023
Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice
Nandan, V., Willatt, R., Mallett, R., Stroeve, J., Geldsetzer, T., Scharien, R., … Hoppmann, M. (2023). Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice. Cryosphere, 17(6), 2211-2229. https://doi.org/10.5194/tc-17-2211-2023
Predicting ocean-induced ice-shelf melt rates using deep learning
Rosier, S. H. R., Bull, C. Y. S., Woo, W. L., & Gudmundsson, G. H. (2023). Predicting ocean-induced ice-shelf melt rates using deep learning. Cryosphere, 17(2), 499-518. https://doi.org/10.5194/tc-17-499-2023
An evaluation of a physics-based firn model and a semi-empirical firn model across the Greenland Ice Sheet (1980–2020)
Thompson-Munson, M., Wever, N., Stevens, C. M., Lenaerts, J. T. M., & Medley, B. (2023). An evaluation of a physics-based firn model and a semi-empirical firn model across the Greenland Ice Sheet (1980–2020). Cryosphere, 17(5), 2185-2209. https://doi.org/10.5194/tc-17-2185-2023
Wind conditions for snow cornice formation in a wind tunnel
Yu, H., Li, G., Walter, B., Lehning, M., Zhang, J., & Huang, N. (2023). Wind conditions for snow cornice formation in a wind tunnel. Cryosphere, 17(2), 639-951. https://doi.org/10.5194/tc-17-639-2023
Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods
Buchmann, M., Coll, J., Aschauer, J., Begert, M., Brönnimann, S., Chimani, B., … Marty, C. (2022). Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods. Cryosphere, 16(6), 2147-2161. https://doi.org/10.5194/tc-16-2147-2022
GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient
Capelli, A., Koch, F., Henkel, P., Lamm, M., Appel, F., Marty, C., & Schweizer, J. (2022). GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient. Cryosphere, 16(2), 505-531. https://doi.org/10.5194/tc-16-505-2022
Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network
Cluzet, B., Lafaysse, M., Deschamps-Berger, C., Vernay, M., & Dumont, M. (2022). Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network. Cryosphere, 16(4), 1281-1298. https://doi.org/10.5194/tc-16-1281-2022
Long-term firn and mass balance modelling for Abramov Glacier in the data-scarce Pamir Alay
Kronenberg, M., Van Pelt, W., Machguth, H., Fiddes, J., Hoelzle, M., & Pertziger, F. (2022). Long-term firn and mass balance modelling for Abramov Glacier in the data-scarce Pamir Alay. Cryosphere, 16(12), 5001-5022. https://doi.org/10.5194/tc-16-5001-2022
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps
Lievens, H., Brangers, I., Marshall, H. P., Jonas, T., Olefs, M., & De Lannoy, G. (2022). Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps. Cryosphere, 16(1), 159-177. https://doi.org/10.5194/tc-16-159-2022
Brief communication: a continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles
Picard, G., Löwe, H., & Mätzler, C. (2022). Brief communication: a continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles. Cryosphere, 16(9), 3861-3866. https://doi.org/10.5194/tc-16-3861-2022
Natural climate variability is an important aspect of future projections of snow water resources and rain-on-snow events
Schirmer, M., Winstral, A., Jonas, T., Burlando, P., & Peleg, N. (2022). Natural climate variability is an important aspect of future projections of snow water resources and rain-on-snow events. Cryosphere, 16(9), 3469-3488. https://doi.org/10.5194/tc-16-3469-2022
Elements of future snowpack modeling - Part 1: a physical instability arising from the nonlinear coupling of transport and phase changes
Schürholt, K., Kowalski, J., & Löwe, H. (2022). Elements of future snowpack modeling - Part 1: a physical instability arising from the nonlinear coupling of transport and phase changes. Cryosphere, 16(3), 903-923. https://doi.org/10.5194/tc-16-903-2022
Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)
Stroeve, J., Nandan, V., Willatt, R., Dadic, R., Rostosky, P., Gallagher, M., … Schneebeli, M. (2022). Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate). Cryosphere, 16(10), 4223-4250. https://doi.org/10.5194/tc-16-4223-2022
Snowfall and snow accumulation during the MOSAiC winter and spring seasons
Wagner, D. N., Shupe, M. D., Cox, C., Persson, O. G., Uttal, T., Frey, M. M., … Lehning, M. (2022). Snowfall and snow accumulation during the MOSAiC winter and spring seasons. Cryosphere, 16(6), 2373-2402. https://doi.org/10.5194/tc-16-2373-2022
Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements
Buchmann, M., Begert, M., Brönnimann, S., & Marty, C. (2021). Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements. Cryosphere, 15(10), 4625-4636. https://doi.org/10.5194/tc-15-4625-2021
Evaluating a prediction system for snow management
Ebner, P. P., Koch, F., Premier, V., Marin, C., Hanzer, F., Carmagnola, C. M., … Lehning, M. (2021). Evaluating a prediction system for snow management. Cryosphere, 15(8), 3949-3973. https://doi.org/10.5194/tc-15-3949-2021
 

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