Book contents
- Sea Ice Analysis and Forecasting
- Sea Ice Analysis and Forecasting
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Acknowledgements
- Abbreviations
- 1 Introduction
- 2 Sea Ice Physics and Modelling
- 3 Sea Ice Observations
- 4 Sea Ice Data Assimilation
- 5 Automated Sea Ice Prediction Systems
- 6 System Evaluation
- 7 Current Ice Services and Their Expected Evolution
- Index
- References
7 - Current Ice Services and Their Expected Evolution
Published online by Cambridge University Press: 12 October 2017
Book contents
- Sea Ice Analysis and Forecasting
- Sea Ice Analysis and Forecasting
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Acknowledgements
- Abbreviations
- 1 Introduction
- 2 Sea Ice Physics and Modelling
- 3 Sea Ice Observations
- 4 Sea Ice Data Assimilation
- 5 Automated Sea Ice Prediction Systems
- 6 System Evaluation
- 7 Current Ice Services and Their Expected Evolution
- Index
- References
Summary
Techniques employed at NIIS are well established but remain a largely manual process that has benefited from the greatly improved availability of satellite imagery. A number of external factors will affect the evolution of ice services over the coming years including greatly increased volumes of satellite SAR imagery, increasing client demands and improving guidance from sea ice APS. The use of APS output should evolve from providing supporting information to becoming the backbone of an expanded suite of ice information products. This will allow ice anallysts to focus on areas where they may have the most effective impact while also enabling clients to access much more diverse information that is better suited to their individual needs.
- Type
- Chapter
- Information
- Sea Ice Analysis and ForecastingTowards an Increased Reliance on Automated Prediction Systems, pp. 174 - 199Publisher: Cambridge University PressPrint publication year: 2017
References
7.5 References
Buehner, M, Caya, A., Carrieres, T. and Pogson, L. (2016). Assimilation of SSMIS and ASCAT data and the replacement of highly uncertain estimates in the Environment Canada Regional Ice Prediction System. Quarterly Journal of the Royal Meteorological Society, 142, 562–573. doi: 10.1002/qj.2408Google Scholar
Carrieres, T., Greenan, B., Prinsenberg, S. and Peterson, I.K. (1996). Comparison of Canadian daily ice charts with surface observations off Newfoundland, winter 1992. Atmosphere-Ocean, 34(1), 207–226. doi: 10.1080/07055900.1996.9649563Google Scholar
Eastwood, S., Karvonen, J., Dinessen, F., Fleming, A., Hamre, T., Toudal Pedersen, L., Saldo, R., Buus-Hinkler, J., Hackett, B., Ardhuin, F. and Kreiner, M.B. (2016). Quality Information Document for OSI TAC Sea Ice Products 011-001, -002, -003, -004, -005, -006, -007, -009, -010, -011, -012. System documentation report for the Copernicus Marine Environment Monitoring Service http://cmems-resources.cls.fr/documents/QUID/CMEMS-OSI-QUID-011-001to007-009to012.pdfGoogle Scholar
Hebert, D.A., Allard, R.A., Metzger, E.J., Posey, P.G., Preller, R.H., Wallcraft, A.J., Phelps, M.W. and Smedstad, O.M. (2015). Short-term sea ice forecasting: an assessment of ice concentration and ice drift forecasts using the U.S. Navy’s Arctic Cap Nowcast/Forecast System. Journal of Geophysical Research: Oceans, 120. doi: 10.1002/2015JC011283Google Scholar
Shlyaeva, A., Buehner, M., Caya, A., Lemieux, J.F., Smith, G.C., Roy, F., Dupont, F. and Carrieres, T. (2016). Towards ensemble data assimilation for the Environment Canada Regional Ice Prediction System. Quarterly Journal of the Royal Meteorological Society, 142, 1090–1099, doi: 10.1002/qj.2712Google Scholar
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