Articles | Volume 12, issue 12
Research article
21 Dec 2018
Research article |  | 21 Dec 2018

Estimation of sea ice parameters from sea ice model with assimilated ice concentration and SST

Siva Prasad, Igor Zakharov, Peter McGuire, Desmond Power, and Martin Richard

Related authors

Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region
Nazanin Asadi, Philippe Lamontagne, Matthew King, Martin Richard, and K. Andrea Scott
The Cryosphere, 16, 3753–3773,,, 2022
Short summary

Related subject area

Discipline: Sea ice | Subject: Data Assimilation
Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)
Emma K. Fiedler, Matthew J. Martin, Ed Blockley, Davi Mignac, Nicolas Fournier, Andy Ridout, Andrew Shepherd, and Rachel Tilling
The Cryosphere, 16, 61–85,,, 2022
Short summary
A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations
William Gregory, Isobel R. Lawrence, and Michel Tsamados
The Cryosphere, 15, 2857–2871,,, 2021
Short summary
Estimating parameters in a sea ice model using an ensemble Kalman filter
Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy S. Collins, Timothy J. Hoar, Kevin D. Raeder, and Edward Blanchard-Wrigglesworth
The Cryosphere, 15, 1277–1284,,, 2021
Short summary
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system
Sindre Fritzner, Rune Graversen, Kai H. Christensen, Philip Rostosky, and Keguang Wang
The Cryosphere, 13, 491–509,,, 2019
Short summary
Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis
Jiping Xie, François Counillon, and Laurent Bertino
The Cryosphere, 12, 3671–3691,,, 2018
Short summary

Cited articles

Banzon, V., Smith, T. M., Chin, T. M., Liu, C., and Hankins, W.: A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies, Earth Syst. Sci. Data, 8, 165–176,, 2016. a
Barré, H. M., Duesmann, B., and Kerr, Y. H.: SMOS: The mission and the system, IEEE T. Geosci. Remote, 46, 587–593, 2008. a
Bell, W.: A preprocessor for SSMIS radiances scientific description, Met Office, UK, 2006. a
Bouzinac, C.: CryoSat product handbook, ESA User Manual, ESA, ESRIN, Italy, 2014. a
Carsey, F. D.: Microwave remote sensing of sea ice, American Geophysical Union, 1992. a
Short summary
A numerical sea ice model, CICE, was used along with data assimilation to derive sea ice parameters in the region of Baffin Bay, Hudson Bay and Labrador Sea. The modelled ice parameters were compared with parameters estimated from remote-sensing data. The ice concentration, thickness and freeboard estimates from the model assimilated with both ice concentration and SST were found to be within the uncertainty of the observations except during March.