Articles | Volume 16, issue 8
https://doi.org/10.5194/nhess-16-1807-2016
https://doi.org/10.5194/nhess-16-1807-2016
Review article
 | 
09 Aug 2016
Review article |  | 09 Aug 2016

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

Jenny Pistoia, Nadia Pinardi, Paolo Oddo, Matthew Collins, Gerasimos Korres, and Yann Drillet

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Cited articles

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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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.
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