http://www.cnr.it/ontology/cnr/individuo/prodotto/ID44722
Methods for the reconstruction of vertical profiles from surface data: multivariate analyses, residual GEM and variable temporal signals in the North Pacific Ocean (Articolo in rivista)
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- Label
- Methods for the reconstruction of vertical profiles from surface data: multivariate analyses, residual GEM and variable temporal signals in the North Pacific Ocean (Articolo in rivista) (literal)
- Anno
- 2005-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1175/JTECH1792.1 (literal)
- Alternative label
Buongiorno Nardelli B., Santoleri R. (2005)
Methods for the reconstruction of vertical profiles from surface data: multivariate analyses, residual GEM and variable temporal signals in the North Pacific Ocean
in Journal of atmospheric and oceanic technology
(literal)
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- Buongiorno Nardelli B., Santoleri R. (literal)
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- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Istituto di Scienze dell'Atmosfera e del Clima, CNR, Rome, Italy (literal)
- Titolo
- Methods for the reconstruction of vertical profiles from surface data: multivariate analyses, residual GEM and variable temporal signals in the North Pacific Ocean (literal)
- Abstract
- Different methods for the extrapolation of vertical profiles from sea surface measurements have been tested on 14 years of Conductivity-Temperature-Depth (CTD) data collected within the HOT (Hawaii Ocean Time-series) program at the station ALOHA in the north Pacific Ocean. A new technique, called multivariate EOF Reconstruction (mEOF-R), has been proposed. mEOF-R is similar to previously developed CPR (Coupled Pattern Reconstruction, Buongiorno Nardelli and Santoleri, 2004) and relies on the availability of surface measurements and historical profiles of salinity, temperature and steric heights. The method is based on the multivariate EOF analysis of the vertical profiles of the three parameters and on the assumption that only few modes are needed to explain most of the variance/covariance of the fields. CPR, sEOF-R (single EOF Reconstruction, Carnes et al. 1994) and mEOF-R performances have been compared with the results of residual GEM techniques (Mitchell et al., 2004) and with ad hoc climatologies, stressing the potential of each method in relation to the length of the time series used to train the models and to the accuracy expected from planned satellite missions for the measurement of surface salinity, sea level and temperature. mEOF-R generally produces the most reliable estimates (in the worst cases comparable to the climatologies), and seems to be slightly less susceptible to errors in the surface input. Multivariate EOF analysis of HOT data also gave by itself interesting results, being able to discriminate the three major signals driving the temporal variability in the area. (literal)
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