Articles | Volume 17, issue 10
https://doi.org/10.5194/tc-17-4487-2023
https://doi.org/10.5194/tc-17-4487-2023
Research article
 | 
26 Oct 2023
Research article |  | 26 Oct 2023

Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model

Keguang Wang, Alfatih Ali, and Caixin Wang

Related authors

Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard
Johannes Röhrs, Yvonne Gusdal, Edel S. U. Rikardsen, Marina Durán Moro, Jostein Brændshøi, Nils Melsom Kristensen, Sindre Fritzner, Keguang Wang, Ann Kristin Sperrevik, Martina Idžanović, Thomas Lavergne, Jens Boldingh Debernard, and Kai H. Christensen
Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023,https://doi.org/10.5194/gmd-16-5401-2023, 2023
Short summary
Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system
Pedro Duarte, Jostein Brændshøi, Dmitry Shcherbin, Pauline Barras, Jon Albretsen, Yvonne Gusdal, Nicholas Szapiro, Andreas Martinsen, Annette Samuelsen, Keguang Wang, and Jens Boldingh Debernard
Geosci. Model Dev., 15, 4373–4392, https://doi.org/10.5194/gmd-15-4373-2022,https://doi.org/10.5194/gmd-15-4373-2022, 2022
Short summary
Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution
Caixin Wang, Robert M. Graham, Keguang Wang, Sebastian Gerland, and Mats A. Granskog
The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019,https://doi.org/10.5194/tc-13-1661-2019, 2019
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, https://doi.org/10.5194/tc-13-491-2019,https://doi.org/10.5194/tc-13-491-2019, 2019
Short summary

Related subject area

Discipline: Sea ice | Subject: Data Assimilation
Towards improving short-term sea ice predictability using deformation observations
Anton Korosov, Pierre Rampal, Yue Ying, Einar Ólason, and Timothy Williams
The Cryosphere, 17, 4223–4240, https://doi.org/10.5194/tc-17-4223-2023,https://doi.org/10.5194/tc-17-4223-2023, 2023
Short summary
Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates
Imke Sievers, Till A. S. Rasmussen, and Lars Stenseng
The Cryosphere, 17, 3721–3738, https://doi.org/10.5194/tc-17-3721-2023,https://doi.org/10.5194/tc-17-3721-2023, 2023
Short summary
The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023,https://doi.org/10.5194/tc-17-2509-2023, 2023
Short summary
Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023,https://doi.org/10.5194/tc-17-1735-2023, 2023
Short summary
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, https://doi.org/10.5194/tc-16-61-2022,https://doi.org/10.5194/tc-16-61-2022, 2022
Short summary

Cited articles

Ali, A., Muller, M., Bertino, L., and Melson, A.: A high resolution three-dimensional model of ocean tides for the pan-arctic region, EGU General Assembly 2021, online, 19–30 April 2021, EGU21-2409, https://doi.org/10.5194/egusphere-egu21-2409, 2021. a
Anthes, R. A.: Data Assimilation and Initialization of Hurricane Prediction Models, J. Atmos. Sci., 31, 702–719, https://doi.org/10.1175/1520-0469(1974)031<0702:DAAIOH>2.0.CO;2, 1974. a, b
Berkman, P. A., Fiske, G., Lorenzini, D., Young, O. R., Pletnikoff, K., Grebmeier, J. M., Fernandez, L. M., Divine, L. M., Causey, D., Kapsar, K. E., and Jørgensen, L. L.: Satellite record of pan-Arctic maritime ship traffic, NOAA technical report OAR ARC, 22-10, https://doi.org/10.25923/mhrv-gr76, 2022. a
Bleck, R.: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates, Ocean Model., 37, 55–88, 2002. a, b
Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J., Lea, D. J., Mirouze, I., Peterson, K. A., Sellar, A., and Storkey, D.: Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts, Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, 2014. a, b
Download
Short summary
A simple, efficient. and accurate data assimilation method, local analytical optimal nudging (LAON), is introduced to assimilate high-resolution sea ice concentration in a pan-Arctic high-resolution coupled ocean and sea ice model. The method provides a new vision by nudging the model evolution to the optimal estimate forwardly, continuously, and smoothly. This method is applicable to the general nudging theory and applications in physics, Earth science, psychology, and behavior sciences.