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Volume 5, issue 4
Ocean Sci., 5, 649–660, 2009
https://doi.org/10.5194/os-5-649-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Mediterranean Ocean Forecasting System: toward environmental...

Ocean Sci., 5, 649–660, 2009
https://doi.org/10.5194/os-5-649-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  07 Dec 2009

07 Dec 2009

Mediterranean Forecasting System: forecast and analysis assessment through skill scores

M. Tonani1, N. Pinardi2, C. Fratianni1, J. Pistoia1, S. Dobricic3, S. Pensieri4, M. de Alfonso5, and K. Nittis6 M. Tonani et al.
  • 1Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy
  • 2University of Bologna, Corso di Scienze Ambientali, Ravenna, Italy
  • 3Centro euro-Mediterraneo per i Cambiamenti Climatici, Bologna, Italy
  • 4Consiglio Nazionale delle Ricerche-ISSIA, Genova, Italy
  • 5Puertos del Estado, Madrid, Spain
  • 6Hellenic Centre for Marine Research, Athens, Greece

Abstract. This paper describes the first evaluation of the quality of the forecast and analyses produced at the basin scale by the Mediterranean ocean Forecasting System (MFS) (http://gnoo.bo.ingv.it/mfs). The system produces short-term ocean forecasts for the following ten days. Analyses are produced weekly using a daily assimilation cycle. The analyses are compared with independent data from buoys, where available, and with the assimilated data before the data are inserted. In this work we have considered 53 ten days forecasts produced from 16 August 2005 to 15 August 2006.

The forecast skill is evaluated by means of root mean square error (rmse) differences, bias and anomaly correlations at different depths for temperature and salinity, computing differences between forecast and analysis, analysis and persistence and forecast and persistence. The Skill Score (SS) is defined as the ratio of the rmse of the difference between analysis and forecast and the rmse of the difference between analysis and persistence. The SS shows that at 5 and 30 m the forecast is always better than the persistence, but at 300 m it can be worse than persistence for the first days of the forecast. This result may be related to flow adjustments introduced by the data assimilation scheme. The monthly variability of SS shows that when the system variability is high, the values of SS are higher, therefore the forecast has higher skill than persistence.

We give evidence that the error growth in the surface layers is controlled by the atmospheric forcing inaccuracies, while at depth the forecast error can be interpreted as due to the data insertion procedure. The data, both in situ and satellite, are not homogeneously distributed in the basin; therefore, the quality of the analyses may be different in different areas of the basin.

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