Articles | Volume 16, issue 8
Nat. Hazards Earth Syst. Sci., 16, 1807–1819, 2016

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

Nat. Hazards Earth Syst. Sci., 16, 1807–1819, 2016

Review article 09 Aug 2016

Review article | 09 Aug 2016

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

Jenny Pistoia1, Nadia Pinardi2,3, Paolo Oddo1,a, Matthew Collins4, Gerasimos Korres5, and Yann Drillet6 Jenny Pistoia et al.
  • 1Istituto Nazionale Geofisica e Vulcanologia, Bologna, Italy
  • 2Department of Physics and Astronomy, University of Bologna, Bologna, Italy
  • 3Centro euro-Mediterraneo sui Cambiamenti Climatici, Via Augusto Imperatore, 16–73100 Lecce, Italy
  • 4College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
  • 5Hellenic Center for Marine Research, 46,7 Km Athens-Sounion Road, Anavissos, 19013, Greece
  • 6Mercator Océan, Parc Technologique du Canal, 8–10 rue Hermès, 31520 Ramonville St Agne, France
  • anow at: NATO Science and Technology Organization Centre for Maritime Research and Experimentation, La Spezia, Italy

Abstract. A super-ensemble methodology is proposed to improve the quality of short-term ocean analyses for sea surface temperature (SST) in the Mediterranean Sea. The methodology consists of a multiple linear regression technique applied to a multi-physics multi-model super-ensemble (MMSE) data set. This is a collection of different operational forecasting analyses together with ad hoc simulations, created by modifying selected numerical model parameterizations. A new linear regression algorithm based on empirical orthogonal function filtering techniques is shown to be efficient in preventing overfitting problems, although the best performance is achieved when a simple spatial filter is applied after the linear regression. Our results show that the MMSE methodology improves the ocean analysis SST estimates with respect to the best ensemble member (BEM) and that the performance is dependent on the selection of an unbiased operator and the length of training. The quality of the MMSE data set has the largest impact on the MMSE analysis root mean square error (RMSE) evaluated with respect to observed satellite SST. The MMSE analysis estimates are also affected by training period length, with the longest period leading to the smoothest estimates. Finally, lower RMSE analysis estimates result from the following: a 15-day training period, an overconfident MMSE data set (a subset with the higher-quality ensemble members) and the least-squares algorithm being filtered a posteriori.

Short summary
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.
Final-revised paper