Articles | Volume 15, issue 6
The Cryosphere, 15, 2857–2871, 2021
The Cryosphere, 15, 2857–2871, 2021
Research article
24 Jun 2021
Research article | 24 Jun 2021

A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

William Gregory et al.

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Cited articles

Aaboe, S., Breivik, L.-A., Sørensen, A., Eastwood, S., and Lavergne, T.: Global sea ice edge and type product user's manual, OSI-403-c & EUMETSAT, 2016. a
Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz, N. T., Phelps, M. W., Posey, P. G., Tilling, R., Ridout, A., and Wallcraft, A. J.: Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system, Adv. Space Res., 62, 1265–1280,, 2018. a
Balan-Sarojini, B., Tietsche, S., Mayer, M., Balmaseda, M., Zuo, H., de Rosnay, P., Stockdale, T., and Vitart, F.: Year-round impact of winter sea ice thickness observations on seasonal forecasts, The Cryosphere, 15, 325–344,, 2021. a
Bishop, C. M.: Pattern recognition and machine learning, Springer, ISBN 978-0387-31073-2, chap. 3, 152–165, 2006. a
Blanchard-Wrigglesworth, E. and Bitz, C. M.: Characteristics of Arctic sea-ice thickness variability in GCMs, J. Climate, 27, 8244–8258,, 2014. a
Short summary
Satellite measurements of radar freeboard allow us to compute the thickness of sea ice from space; however attaining measurements across the entire Arctic basin typically takes up to 30 d. Here we present a statistical method which allows us to combine observations from three separate satellites to generate daily estimates of radar freeboard across the Arctic Basin. This helps us understand how sea ice thickness is changing on shorter timescales and what may be causing these changes.