On sea-ice forecasting
AuthorFritzner, Sindre Markus
Paper I: Fritzner, S.M., Graversen, R.G., Wang, K. & Christensen, K.H. (2018). Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation. Journal of Glaciology, 64(245), 387–396. Also available in Munin at https://hdl.handle.net/10037/13969.
Paper II: Fritzner, S., Graversen, R., Christensen, K.H., Rostosky, P. & Wang, K. (2019). Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system. The Cryosphere, 13, 491–509. Also available in Munin at https://hdl.handle.net/10037/16412.
Paper III: Fritzner, S., Graversen, R. & Christensen, K.H. Assessment of high-resolution dynamical and machine learning models for prediction of sea-ice concentration in a regional application. (Submitted manuscript).
Related research dataFritzner, S. (2019). Assessment of high-resolution dynamical and statistical models for prediction of sea-ice concentration [Data set]. Norstore. https://doi.org/10.11582/2019.00038.
PublisherUiT Norges arktiske universitet
UiT The Arctic University of Norway
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