Edge displacement scores

Melsom, Arne

doi:https://doi.org/10.5194/tc-15-3785-2021

Articles | Volume 15, issue 8

https://doi.org/10.5194/tc-15-3785-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-15-3785-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 8

Research article

|

17 Aug 2021

Research article |

| 17 Aug 2021

Edge displacement scores

Arne Melsom

Related authors

MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic

Cyril Palerme, Johannes Röhrs, Thomas Lavergne, Jozef Rusin, Are Frode Kvanum, Atle Macdonald Sørensen, Arne Melsom, Julien Brajard, Martina Idžanović, Marina Durán Moro, and Malte Müller

EGUsphere, https://doi.org/10.5194/egusphere-2025-2001,https://doi.org/10.5194/egusphere-2025-2001, 2025

Short summary

Improving short-term sea ice concentration forecasts using deep learning

Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller

The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024,https://doi.org/10.5194/tc-18-2161-2024, 2024

Short summary

MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic

Cyril Palerme, Johannes Röhrs, Thomas Lavergne, Jozef Rusin, Are Frode Kvanum, Atle Macdonald Sørensen, Arne Melsom, Julien Brajard, Martina Idžanović, Marina Durán Moro, and Malte Müller

EGUsphere, https://doi.org/10.5194/egusphere-2025-2001,https://doi.org/10.5194/egusphere-2025-2001, 2025

Short summary

Improving short-term sea ice concentration forecasts using deep learning

Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller

The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024,https://doi.org/10.5194/tc-18-2161-2024, 2024

Short summary

Cited articles

Aksenov, Y., Popova, E. E., Yool, A., Nurser, A. G., Williams, T. D., and Bertino, L.: On the future navigability of Arctic sea routes: High-resolution projections of the Arctic Ocean and sea ice, Mar. Policy, 75, 300–317, https://doi.org/10.1016/j.marpol.2015.12.027, 2017. a

Anderson, J. L., A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Climate, 9, 1518–1530, https://doi.org/10.1175/1520-0442(1996)009<1518:AMFPAE>2.0.CO;2, 1996. a

Bitz, C. M., Holland, M. M., and Hunke, E. C.: Maintenance of the Sea-Ice Edge, J. Climate, 18, 2903–2921, https://doi.org/10.1175/JCLI3428.1, 2005. a

Budgell, P. W.: Numerical simulation of ice-ocean variability in the Barents Sea region: Towards dynamical downscaling, Ocean Dynam., 55, 370–387, https://doi.org/10.1007/s10236-005-0008-3, 2005. a

Cheng, A., Casati, B., Tivy, A., Zagon, T., Lemieux, J.-F., and Tremblay, L. B.: Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2, The Cryosphere, 14, 1289–1310, https://doi.org/10.5194/tc-14-1289-2020, 2020. a

Davis, C. A., Brown, B. G., and Bullock, R. G.: Object-based verification of precipitation forecasts, Part I: Methodology and application to mesoscale rain areas, Mon. Weather Rev., 134, 1772–1784, https://doi.org/10.1175/MWR3145.1, 2006. a

Dukhovskoy, D. S., Ubnoske, J., Blanchard-Wrigglesworth, E., Hiester, H. R., and Proshutinsky, A.: Skill metrics for evaluation and comparison of sea ice models, J. Geophys. Res.-Oceans, 120, 5910–5931, https://doi.org/10.1002/2015JC010989, 2015. a, b

Ebert, E. E. and McBride, J. L.: Verification of precipitation in weather systems: determination of systematic errors, J. Hydrol., 239, 179–202, https://doi.org/10.1016/S0022-1694(00)00343-7, 2000. a, b

Goessling, H. F. and Jung, T.: A probabilistic verification score for contours: Methodology and application to Arctic ice-edge forecast, Q. J. Roy. Meteor. Soc., 144, 735–743, https://doi.org/10.1002/qj.3242, 2018. a

Goessling, H. F., Tietsche, S., Day, J. J., Hawkins, E., and Jung, T.: Predictability of the Arctic sea ice edge, Geophys. Res. Lett., 43, 1642–1650, https://doi.org/10.1002/2015GL067232, 2016. a, b

Haidvogel, D. B., Arango, H. G., Budgell, P. W., and 17 coauthors: Ocean forecasting in terrain-following coordinates: Formulation and skill assessment of the Regional Ocean Modeling System, J. Comput. Phys., 227, 3595–3624, https://doi.org/10.1016/j.jcp.2007.06.016, 2008. a

Häkkinen, S., and Mellor, G. L.: Modelling the seasonal variability of a coupled arctic ice-ocean system, J. Geophys. Res., 97, 20.285–20.304, https://doi.org/10.1029/92JC02037, 1992. a

Hamill, T. M.: Interpretation of Rank Histograms for Verifying Ensemble Forecasts, Mon. Weather Rev., 129, 550–560, https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2, 2001. a

Hunke, E. C.: Viscous-plastic sea ice dynamics with the EVP model: linearization issues, J. Comput. Phys., 170, 18–38, https://doi.org/10.1006/jcph.2001.6710, 2001. a

Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea ice dynamics, J. Phys. Oceanogr., 27, 1849–1867, https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997. a

Lavergne, T., Eastwood, S., Teffah, Z., and Schyberg, H.: Sea ice motion from low resolution satellite sensors: An alternative method and its validation in the Arctic, J. Geophys. Res., 115, C10032, https://doi.org/10.1029/2009JC005958, 2010. a

Leese, J. A., Novak, C. S., and Clark, B. B.: An Automated Technique for Obtaining Cloud Motion from Geosynchronous Satellite Data Using Cross Correlation, J. Appl. Meteorol., 10, 118–132, https://doi.org/10.1175/1520-0450(1971)010<0118:AATFOC>2.0.CO;2, 1971. a

Li, Q., Steven, G. P., and Xie, Y. M.: A simple checkerboard suppression algorithm for evolutionary structural optimization, Struct. Optimization, 22, 230–239, https://doi.org/10.1007/s001580100140, 2001. a

Lien, V. S., Gusdal, Y., Albretsen, J., Melsom, A., and Vikebø, F. B.: Evaluation of a Nordic Seas 4 km numerical ocean model hindcast archive (SVIM), Fisken og Havet, 2013–2017, Bergen, Norway, 80 pp., 2011. a

Mellor, G. L. and Kantha, L.: An ice ocean coupled model, J. Geophys. Res., 94, 10.937–10.954, https://doi.org/10.1029/JC094iC08p10937, 1989. a

Melsom, A.: Edge metrics software v.1.0 [Software], Zenodo [code], https://doi.org/10.5281/zenodo.4545686, 2021. a

Melsom, A., Palerme, C., and Müller, M.: Validation metrics for ice edge position forecasts, Ocean Sci., 15, 615–630, https://doi.org/10.5194/os-15-615-2019, 2019. a, b, c, d, e

The NCAR Command Language (Version 6.4.0) [Software], UCAR/NCAR/CISL/TDD [code], Boulder, Colorado, https://doi.org/10.5065/D6WD3XH5, 2017. a

Norwegian Meteorological Institute, Institute of Marine Research: SVIM ocean hindcast archive, National Infrastructure for Research Data [data set], https://doi.org/10.11582/2015.00014, 2015. a, b

Palerme, C., Müller, M., and Melsom, A.: An intercomparison of skill scores for evaluating the sea ice edge position in seasonal forecasts, Geophys. Res. Lett., 46, 4757–4763, https://doi.org/10.1029/2019GL082482, 2019. a

Parkinson, C. L.: Spatially mapped reductions in the length of the Arctic sea ice season, Geophys. Res. Lett., 41, 4316–4322, https://doi.org/10.1002/2014GL060434, 2014. a

Talagrand, O., Vautard R., and Strauss B.: Evaluation of probabilistic prediction systems, Proc. Workshop on Predictability, ECMWF, Reading, UK, 20–22 October 1997, 25 pp., 1997. a

Tokmakian, R., Strub, P. T., and McClean-Padman, J.: Evaluation of the Maximum Cross-Correlation Method of Estimating Sea Surface Velocities from Sequential Satellite Images, J. Atmos. Ocean. Tech., 7, 852–865, https://doi.org/10.1175/1520-0426(1990)007<0852:EOTMCC>2.0.CO;2, 1990. a

Wilks, D.: Indices of Rank Histogram Flatness and Their Sampling Properties, Mon. Weather Rev., 147, 763–769, https://doi.org/10.1175/MWR-D-18-0369.1, 2019. a

WMO: Sea-Ice Information Services in the World, World Meteo. No. 574, World Meteorological Organization, Geneva, Switzerland, 103 pp., 2017. a, b

More articles (25)

Download

Article (2493 KB)

Full-text XML

BibTeX

EndNote

Short summary

This study presents new methods to assess how well observations of sea ice expansion are reproduced by results from models. The aim is to provide information about the quality of forecasts for changes in the sea ice extent to operators in or near ice-infested waters. A test using 2 years of model results demonstrates the practical applicability and usefulness of the methods that are presented.

This study presents new methods to assess how well observations of sea ice expansion are...

Read more

Share