Articles | Volume 13, issue 4
https://doi.org/10.5194/tc-13-1283-2019
© Author(s) 2019. 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-13-1283-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Apr 2019
Research article |
| 18 Apr 2019
| 18 Apr 2019
Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data
Lise Kilic
CORRESPONDING AUTHOR
Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, Paris, France
Rasmus Tage Tonboe
Danish Meteorological Institute, Copenhagen, Denmark
Catherine Prigent
Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, Paris, France
Georg Heygster
Institute of Environmental Physics, University of Bremen, Bremen, Germany
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34 citations as recorded by crossref.
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- Toward Daily Snow Depth Estimation on Arctic Sea Ice During the Whole Winter Season From Passive Microwave Radiometer Data L. He et al. 10.1109/TGRS.2024.3358340
- Inter-comparisons of Arctic snow depth products M. Li et al. 10.1080/17538947.2024.2376286
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- Inter-Sensor Calibration between HY-2B and AMSR2 Passive Microwave Data in Land Surface and First Result for Snow Water Equivalent Retrieval S. Gao et al. 10.3390/s19225023
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- Improved snow depth retrieval over Arctic sea ice from FY-3 satellites using machine learning and its future scenario projection C. Ke et al. 10.1016/j.jhydrol.2024.132105
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- Development of ANN-Based Algorithm to Estimate Wintertime Sea Ice Temperature Profile Over the Arctic Ocean S. Baek et al. 10.1109/TGRS.2023.3293137
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- Impact of microwave observations on the estimation of Arctic sea surface temperatures P. Nielsen-Englyst et al. 10.1016/j.rse.2023.113949
- A Multiscale Dual Attention Network for the Automatic Classification of Polar Sea Ice and Open Water Based on Sentinel-1 SAR Images Z. Zhang et al. 10.1109/JSTARS.2024.3354912
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- A New Bias Correction Approach for Better Assimilation of Microwave Sounding Data Over Winter Sea Ice in the Korean Integrated Model J. Kim et al. 10.1109/TGRS.2023.3335930
- Retrieval of Snow Depths on Arctic Sea Ice in the Cold Season from FY-3D/MWRI Data Q. Yin et al. 10.3390/rs16050821
- Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements: Uncertainty Analyses X. Lu et al. 10.3389/frsen.2022.891481
- Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements H. Shi et al. 10.5194/tc-14-3761-2020
- Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network J. Liu et al. 10.3390/rs11232864
34 citations as recorded by crossref.
- A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval J. Anheuser et al. 10.5194/tc-16-4403-2022
- Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval L. Zhou et al. 10.5194/tc-15-345-2021
- A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era J. Anheuser et al. 10.5194/tc-17-2871-2023
- Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI L. Li et al. 10.3390/rs13081457
- Snow depth product over Antarctic sea ice from 2002 to 2020 using multisource passive microwave radiometers X. Shen et al. 10.5194/essd-14-619-2022
- A deep learning approach to retrieve cold-season snow depth over Arctic sea ice from AMSR2 measurements H. Li et al. 10.1016/j.rse.2021.112840
- Quantifying the influence of snow over sea ice morphology on L-band passive microwave satellite observations in the Southern Ocean L. Zhou et al. 10.5194/tc-18-4399-2024
- A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data Z. Dong et al. 10.3390/rs14041041
- Toward Daily Snow Depth Estimation on Arctic Sea Ice During the Whole Winter Season From Passive Microwave Radiometer Data L. He et al. 10.1109/TGRS.2024.3358340
- Inter-comparisons of Arctic snow depth products M. Li et al. 10.1080/17538947.2024.2376286
- Step-by-Step Validation of Antarctic ASI AMSR-E Sea-Ice Concentrations by MODIS and an Aerial Image Q. Shi et al. 10.1109/TGRS.2020.2989037
- Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network A. Braakmann-Folgmann & C. Donlon 10.5194/tc-13-2421-2019
- Sensitivity of Passive Microwave Satellite Observations to Snow Density and Grain Size Over Arctic Sea Ice Y. Kwon et al. 10.1109/TGRS.2023.3322401
- Inter-Sensor Calibration between HY-2B and AMSR2 Passive Microwave Data in Land Surface and First Result for Snow Water Equivalent Retrieval S. Gao et al. 10.3390/s19225023
- A D-InSAR method to improve snow depth estimation accuracy Y. Zhang et al. 10.1360/TB-2022-0679
- Estimation of summer pan-Arctic ice draft from satellite passive microwave observations J. Kim et al. 10.1016/j.rse.2023.113662
- Improving snow depth simulations on Arctic Sea ice by assimilating a passive microwave-derived record H. Li et al. 10.1016/j.coldregions.2023.103929
- Improved snow depth retrieval over Arctic sea ice from FY-3 satellites using machine learning and its future scenario projection C. Ke et al. 10.1016/j.jhydrol.2024.132105
- On the Synergy of SMAP and AMSR2 for Estimating Snow Depth on Arctic Sea Ice L. He et al. 10.1109/LGRS.2022.3188001
- Development of ANN-Based Algorithm to Estimate Wintertime Sea Ice Temperature Profile Over the Arctic Ocean S. Baek et al. 10.1109/TGRS.2023.3293137
- Triple Collocation-Based Merging of Winter Snow Depth Retrievals on Arctic Sea Ice Derived From Three Different Algorithms Using AMSR2 L. He et al. 10.1109/TGRS.2023.3290073
- Snowfall events in the Cantabrian Mountains of northwestern Spain: WRF multiphysics ensemble assessment based on ground and multi-satellite observations A. Melón-Nava et al. 10.1016/j.atmosres.2023.106719
- High-Resolution Snow Depth on Arctic Sea Ice From Low-Altitude Airborne Microwave Radar Data A. Jutila et al. 10.1109/TGRS.2021.3063756
- Arctic Sea Ice Surface Temperature Inversion Using FY-3D/MWRI Brightness Temperature Data X. Meng et al. 10.3390/rs16030490
- Year-round sea ice and snow characterization from combined passive and active microwave observations and radiative transfer modeling C. Soriot et al. 10.1016/j.rse.2022.113061
- Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval J. Ruiz et al. 10.1109/TGRS.2022.3223760
- Impact of microwave observations on the estimation of Arctic sea surface temperatures P. Nielsen-Englyst et al. 10.1016/j.rse.2023.113949
- A Multiscale Dual Attention Network for the Automatic Classification of Polar Sea Ice and Open Water Based on Sentinel-1 SAR Images Z. Zhang et al. 10.1109/JSTARS.2024.3354912
- Simulated Geophysical Noise in Sea Ice Concentration Estimates of Open Water and Snow-Covered Sea Ice R. Tonboe et al. 10.1109/JSTARS.2021.3134021
- A New Bias Correction Approach for Better Assimilation of Microwave Sounding Data Over Winter Sea Ice in the Korean Integrated Model J. Kim et al. 10.1109/TGRS.2023.3335930
- Retrieval of Snow Depths on Arctic Sea Ice in the Cold Season from FY-3D/MWRI Data Q. Yin et al. 10.3390/rs16050821
- Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements: Uncertainty Analyses X. Lu et al. 10.3389/frsen.2022.891481
- Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements H. Shi et al. 10.5194/tc-14-3761-2020
- Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network J. Liu et al. 10.3390/rs11232864
Latest update: 14 Dec 2024
Short summary
In this study, we develop and present simple algorithms to derive the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice. This is achieved using satellite observations collocated with buoy measurements. The errors of the retrieved parameters are estimated and compared with independent data. These parameters are useful for sea ice concentration mapping, understanding sea ice properties and variability, and for atmospheric sounding applications.
In this study, we develop and present simple algorithms to derive the snow depth, the snow–ice...
