Seasonal Arctic sea ice forecasting with probabilistic deep learning.

Autor: Andersson TR; British Antarctic Survey, NERC, UKRI, Cambridge, UK. tomand@bas.ac.uk., Hosking JS; British Antarctic Survey, NERC, UKRI, Cambridge, UK.; The Alan Turing Institute, London, UK., Pérez-Ortiz M; Department of Computer Science, University College London, London, UK., Paige B; The Alan Turing Institute, London, UK.; Department of Computer Science, University College London, London, UK., Elliott A; The Alan Turing Institute, London, UK.; School of Mathematics and Statistics, University of Glasgow, Glasgow, UK., Russell C; Amazon Web Services, Tübingen, Germany., Law S; The Alan Turing Institute, London, UK.; Department of Geography, University College London, London, UK., Jones DC; British Antarctic Survey, NERC, UKRI, Cambridge, UK., Wilkinson J; British Antarctic Survey, NERC, UKRI, Cambridge, UK., Phillips T; British Antarctic Survey, NERC, UKRI, Cambridge, UK., Byrne J; British Antarctic Survey, NERC, UKRI, Cambridge, UK., Tietsche S; European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK., Sarojini BB; European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK., Blanchard-Wrigglesworth E; Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA., Aksenov Y; National Oceanography Centre, Southampton, UK., Downie R; WWF, Woking, UK., Shuckburgh E; British Antarctic Survey, NERC, UKRI, Cambridge, UK.; University of Cambridge, Cambridge, UK.

Publikováno v: Nature communications [Nat Commun] 2021 Aug 26; Vol. 12 (1), pp. 5124. Date of Electronic Publication: 2021 Aug 26.