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
6 - System Evaluation
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
Sea ice APS evaluation builds on techniques developed for other applications, but presents some additional challenges due to the nature of sea ice itself and observational limitations.While APS predict ice conditions as continuous, sometimes bounded quantities, verifying observations may only be available as categorical quantities, which necessitates the use of contingency tables. Evaluation of continuous scalars, such as ice concentration and thickness, uses common statistical measures such as bias and variance. Sea ice velocity and ice edge require specialized evaluation approaches which account for their spatial characteristics. Several aspects of sea ice evaluation practice remain as challenges for future developments in sea ice and evaluation research.
- Type
- Chapter
- Information
- Sea Ice Analysis and ForecastingTowards an Increased Reliance on Automated Prediction Systems, pp. 144 - 173Publisher: Cambridge University PressPrint publication year: 2017
References
Baddeley, A. J. (1992). An error metric for binary images. Robust Computer Vision: Quality of Vision Algorithms, Förstner, W. and Ruwiedel, S., eds., Wichmann, 59–78.Google Scholar
Davis, C. A., Brown, B. G. and Bullock, R. G. (2006). Object-based verification of precipitation forecasts, Part I: Methodology and application to mesoscale rain areas. Monthly Weather Review, 134, 1772–1784.Google Scholar
Dubuisson, M.P. and Jain, A. K. (1994). A modified Hausdorff distance for object matching. Proceedings of the International Conference on Pattern Recognition, Jerusalem, Israel, 566–568.Google Scholar
Dukhovskoy, D. S., Ubnoske, J., Blanchard-Wrigglesworth, E., Hiester, H. R. and Proshutinsky, A. (2015). Skill metrics for evaluation and comparison of sea ice models. Journal of Geophysical Research: Oceans, 120, 5910–5931. doi: 10.1002/2015JC010989Google Scholar
Ebert, E. E. (2008). Fuzzy verification of high resolution gridded forecasts: A review and proposed framework. Meteorological Applications, 15, 51–64Google Scholar
Gilleland, E., Ahijevych, D. A., Brown, B. G. and Ebert, E. E. (2010). Verifying forecasts spatially. Bulletin of the American Meteorological Society, 91(10), 1365–1369. doi: 10.1175/2010BAMS2819.1Google Scholar
Goessling, H. F., Tietsche, S., Day, J. J., Hawkins, E. and Jung, T. (2016). Predictability of the Arctic sea ice edge. Geophysical Research Letters, 43, 1642–1650. doi: 10.1002/2015GL067232Google 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
Heinrichs, J. F., Cavalieri, D. J. and Markus, T. (2006). Assessment of the AMSR-E sea-ice concentration product at the ice edge using RADARSAT-1 and MODIS imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(11), 3070–3080. doi: 10.1109/TGRS.2006.880622Google Scholar
Jolliffe, I. T. and Stephenson, W. B. (2012). Forecast verification: A practitioner’s guide in Atmospheric Science, 2nd edn. Wiley. doi: 10.1002/9781119960003Google Scholar
Merryfield, W. J., Lee, W.S., Wang, W., Chen, M. and Kumar, A. (2013). Multi-system seasonal predictions of Arctic sea ice. Geophysical Research Letters, 40, 1551–1556. doi: 10.1002/grl.50317Google Scholar
Murphy, A. H. (1993). What is a good forecast? An essay on the nature of goodness in weather forecasting. Weather and Forecasting, 8, 281–293.Google Scholar
Murphy, A. H. and Winkler, R. L. (1987). A general framework for forecast verification. Monthly Weather Review, 115, 1330–1338Google Scholar
Sigmond, M., Fyfe, J. C., Flato, G. M., Kharin, V. V. and Merryfield, W. J. (2013). Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system. Geophysical Research Letters, 40, 529–534. doi: 10.1002/grl.50129Google Scholar
Smith, G. C., Roy, F., Reszka, M., Surcel Colan, D., He, Z., Deacu, D., Bélanger, J.M., Skachko, S., Liu, Y., Dupont, F., Lemieux, J.F., Beaudoin, C., Tranchant, B., Drévillon, M., Garric, G., Testut, C.E., Lellouche, J.M., Pellerin, P., Ritchie, H., Lu, Y., Davidson, F., Buehner, M., Lajoie, M. and Caya, A. (2015). Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System. Quarterly Journal of the Royal Meteorological Society, 142(695). doi: 10.1002/qj.2555Google Scholar
Wilks, D. S. (2011). Statistical Methods in the Atmospheric Sciences, 3rd edn, Volume 100, Academic Press, ISBN-13: 978-0123850225Google Scholar