Articles | Volume 10, issue 5
https://doi.org/10.5194/tc-10-2003-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-2003-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
07 Sep 2016
Research article |
| 07 Sep 2016
Near-real-time Arctic sea ice thickness and volume from CryoSat-2
Rachel L. Tilling
CORRESPONDING AUTHOR
Centre for Polar Observation and Modelling, Department of Earth
Sciences, University College London, London, WC1E6BT, UK
Andy Ridout
Centre for Polar Observation and Modelling, Department of Earth
Sciences, University College London, London, WC1E6BT, UK
Andrew Shepherd
Centre for Polar Observation and Modelling, School of Earth and
Environment, University of Leeds, Leeds, LS29JT, UK
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50 citations as recorded by crossref.
- Estimating the sea ice floe size distribution using satellite altimetry: theory, climatology, and model comparison C. Horvat et al. 10.5194/tc-13-2869-2019
- Walking on snow-covered Arctic sea ice to infer ice thickness D. Reeder et al. 10.1121/10.0016632
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- Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system R. Allard et al. 10.1016/j.asr.2017.12.030
- Ice and Snow Thickness Variability and Change in the High Arctic Ocean Observed by In Situ Measurements C. Haas et al. 10.1002/2017GL075434
- Warm winter, thin ice? J. Stroeve et al. 10.5194/tc-12-1791-2018
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- Snow Property Controls on Modeled Ku-Band Altimeter Estimates of First-Year Sea Ice Thickness: Case Studies From the Canadian and Norwegian Arctic V. Nandan et al. 10.1109/JSTARS.2020.2966432
- The sea ice model component of HadGEM3-GC3.1 J. Ridley et al. 10.5194/gmd-11-713-2018
- Validation of CryoSat-2 SARIn Data over Austfonna Ice Cap Using Airborne Laser Scanner Measurements L. Sandberg Sørensen et al. 10.3390/rs10091354
- Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis J. Xie et al. 10.5194/tc-12-3671-2018
- Recent multivariate changes in the North Atlantic climate system, with a focus on 2005–2016 J. Robson et al. 10.1002/joc.5815
- A Facet-Based Numerical Model for Simulating SAR Altimeter Echoes From Heterogeneous Sea Ice Surfaces J. Landy et al. 10.1109/TGRS.2018.2889763
- 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
- Comparison of Freeboard Retrieval and Ice Thickness Calculation From ALS, ASIRAS, and CryoSat‐2 in the Norwegian Arctic to Field Measurements Made During the N‐ICE2015 Expedition J. King et al. 10.1002/2017JC013233
- Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses F. Cocetta et al. 10.5194/tc-18-4687-2024
- Towards seasonal Arctic shipping route predictions N. Melia et al. 10.1088/1748-9326/aa7a60
- Evaluation of sea-ice thickness reanalysis data from the coupled ocean-sea-ice data assimilation system TOPAZ4 Y. Xiu et al. 10.1017/jog.2020.110
- Sea‐ice loss boosts visual search: fish foraging and changing pelagic interactions in polar oceans T. Langbehn & Ø. Varpe 10.1111/gcb.13797
- 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
- An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC) M. Tschudi et al. 10.5194/tc-14-1519-2020
- Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system Q. Shu et al. 10.1007/s13131-021-1768-4
- Insights on Sea Ice Data Assimilation from Perfect Model Observing System Simulation Experiments Y. Zhang et al. 10.1175/JCLI-D-17-0904.1
- Arctic sea ice thickness variations from CryoSat-2 satellite altimetry data F. Xiao et al. 10.1007/s11430-020-9777-9
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- Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method F. Xiao et al. 10.3390/s20247011
- Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM) E. Fiedler et al. 10.5194/tc-16-61-2022
- Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment A. Shah et al. 10.1080/16000870.2019.1697166
- Exploitation of trans-Arctic maritime transportation Z. Zhang et al. 10.1016/j.jclepro.2018.12.070
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- Data Synergy between Altimetry and L-Band Passive Microwave Remote Sensing for the Retrieval of Sea Ice Parameters—A Theoretical Study of Methodology S. Xu et al. 10.3390/rs9101079
- Retrievals of Arctic Sea‐Ice Volume and Its Trend Significantly Affected by Interannual Snow Variability F. Bunzel et al. 10.1029/2018GL078867
- Local-scale variability of snow density on Arctic sea ice J. King et al. 10.5194/tc-14-4323-2020
- Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness E. Blockley & K. Peterson 10.5194/tc-12-3419-2018
- Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model J. Ono et al. 10.1017/aog.2020.13
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- Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system J. Xie et al. 10.5194/tc-10-2745-2016
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- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
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- Variability of Arctic Sea ice age and its relationship with atmospheric circulation patterns P. Chen et al. 10.3389/fmars.2023.1274665
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- Feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter Z. Dong et al. 10.5194/tc-17-1389-2023
3 citations as recorded by crossref.
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- Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales E. Blanchard-Wrigglesworth et al. 10.1007/s00382-016-3388-9
- Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system R. Allard et al. 10.1016/j.asr.2017.12.030
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Latest update: 19 Nov 2024
Short summary
We use CryoSat-2 satellite data to provide the first near-real-time (NRT) measurements of absolute sea ice thickness across the entire Northern Hemisphere. We analyse our NRT sea-ice-thickness data for one sea ice growth season, from October 2014 to April 2015. Over that time period a NRT thickness measurement was delivered, on average, within 14, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively.
We use CryoSat-2 satellite data to provide the first near-real-time (NRT) measurements of...
