Skill metrics for evaluation and comparison of sea ice models
Dukhovskoy, Dmitry S.
Hiester, Hannah R.
MetadataShow full item record
Five quantitative methodologies (metrics) that may be used to assess the skill of sea ice models against a control field are analyzed. The methodologies are Absolute Deviation, Root-Mean-Square Deviation, Mean Displacement, Hausdorff Distance, and Modified Hausdorff Distance. The methodologies are employed to quantify similarity between spatial distribution of the simulated and control scalar fields providing measures of model performance. To analyze their response to dissimilarities in two-dimensional fields (contours), the metrics undergo sensitivity tests (scale, rotation, translation, and noise). Furthermore, in order to assess their ability to quantify resemblance of three-dimensional fields, the metrics are subjected to sensitivity tests where tested fields have continuous random spatial patterns inside the contours. The Modified Hausdorff Distance approach demonstrates the best response to tested differences, with the other methods limited by weak responses to scale and translation. Both Hausdorff Distance and Modified Hausdorff Distance metrics are robust to noise, as opposed to the other methods. The metrics are then employed in realistic cases that validate sea ice concentration fields from numerical models and sea ice mean outlook against control data and observations. The Modified Hausdorff Distance method again exhibits high skill in quantifying similarity between both two-dimensional (ice contour) and three-dimensional (ice concentration) sea ice fields. The study demonstrates that the Modified Hausdorff Distance is a mathematically tractable and efficient method for model skill assessment and comparison providing effective and objective evaluation of both two-dimensional and three-dimensional sea ice characteristics across data sets.
© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Oceans 120 (2015): 5910–5931, doi:10.1002/2015JC010989.
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International
Showing items related by title, author, creator and subject.
Stow, Craig A.; Jolliff, Jason; McGillicuddy, Dennis J.; Doney, Scott C.; Allen, J. Icarus; Friedrichs, Marjorie A. M.; Rose, Kenneth A.; Wallhead, Philip (2008-03-04)Coupled biological/physical models of marine systems serve many purposes including the synthesis of information, hypothesis generation, and as a tool for numerical experimentation. However, marine system models are ...
Skill metrics for confronting global upper ocean ecosystem-biogeochemistry models against field and remote sensing data Doney, Scott C.; Lima, Ivan D.; Moore, J. Keith; Lindsay, Keith; Behrenfeld, Michael J.; Westberry, Toby K.; Mahowald, Natalie M.; Glover, David M.; Takahashi, Taro (2008-03-04)We present a generalized framework for assessing the skill of global upper ocean ecosystem-biogeochemical models against in-situ field data and satellite observations. We illustrate the approach utilizing a multi-decade ...
Effect of roughness formulation on the performance of a coupled wave, hydrodynamic, and sediment transport model Ganju, Neil K.; Sherwood, Christopher R. (Elsevier B.V., 2010-03-27)A variety of algorithms are available for parameterizing the hydrodynamic bottom roughness associated with grain size, saltation, bedforms, and wave–current interaction in coastal ocean models. These parameterizations give ...