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Volume 16, issue 8
Nat. Hazards Earth Syst. Sci., 16, 1807–1819, 2016
https://doi.org/10.5194/nhess-16-1807-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Situational sea awareness technologies for maritime safety...

Nat. Hazards Earth Syst. Sci., 16, 1807–1819, 2016
https://doi.org/10.5194/nhess-16-1807-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Review article 09 Aug 2016

Review article | 09 Aug 2016

Development of super-ensemble techniques for ocean analyses: the Mediterranean Sea case

Jenny Pistoia et al.

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Boria, R. A., Olson, L. E., Goodman, S. M., and Anderson, R. P.: Spatial filtering to reduce sampling bias can improve the performance of ecological niche models, Ecol. Model., 275, 73–77, https://doi.org/10.1016/j.ecolmodel.2013.12.012, 2014.
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Casey, K., Brandon, T., Cornillon, P., and Evans, R.: The Past, Present and Future of the AVHRR Pathfinder SST Program, Oceanography from Space: Revisited, 2010.
Dobricic, S. and Pinardi, N.: An oceanographic three-dimensional variational data assimilation scheme, Ocean Model., 22, 89–105, https://doi.org/10.1016/j.ocemod.2008.01.004, 2008.
Evans, R., Harrison, M., Graham, R., and Mylne, K.: Joint Medium-Range Ensembles from The Met. Office and ECMWF Systems, Mon. Weather Rev., 128, 3104–3127, https://doi.org/10.1175/1520-0493(2000)128<3104:JMREFT>2.0.CO;2, 2000.
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In this work we developed a new multi-model super-ensemble method to estimate sea surface temperature, an important product of ocean analysis systems. We find that ensemble size, quality, type of members and the training period length are all important elements of the MMSE methodology and require careful calibration. We show that with a rather limited but overconfident data set (with a low bias of the starting ensemble members) the RMSE analysis can be improved.
In this work we developed a new multi-model super-ensemble method to estimate sea surface...
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