http://www.cnr.it/ontology/cnr/individuo/prodotto/ID52659
Mediterranean Forecasting System: forecast and analysis (Articolo in rivista)
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- Label
- Mediterranean Forecasting System: forecast and analysis (Articolo in rivista) (literal)
- Anno
- 2009-01-01T00:00:00+01:00 (literal)
- Alternative label
M. Tonani, N. Pinardi, C. Fratianni, J. Pistoia, S. Dobricic, S. Pensieri, M. de Alfonso, K. Nittis (2009)
Mediterranean Forecasting System: forecast and analysis
in Ocean science (Print)
(literal)
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- M. Tonani, N. Pinardi, C. Fratianni, J. Pistoia, S. Dobricic, S. Pensieri, M. de Alfonso, K. Nittis (literal)
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- M. Tonani1, N. Pinardi2, C. Fratianni1, J. Pistoia1, S. Dobricic3, S. Pensieri4, M. de Alfonso5, and K. Nittis6
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 (literal)
- Titolo
- Mediterranean Forecasting System: forecast and analysis (literal)
- 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
30m the forecast is always better than the persistence, but at
300m 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. (literal)
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