scholarly journals Methods for the Reconstruction of Vertical Profiles from Surface Data: Multivariate Analyses, Residual GEM, and Variable Temporal Signals in the North Pacific Ocean

2005 ◽  
Vol 22 (11) ◽  
pp. 1762-1781 ◽  
Author(s):  
Bruno Buongiorno Nardelli ◽  
Rosalia Santoleri

Abstract Different methods for the extrapolation of vertical profiles from sea surface measurements have been tested on 14 yr of conductivity–temperature–depth (CTD) data collected within the Hawaii Ocean Time-series (HOT) program at A Long-Term Oligotrophic Habitat Assessment (ALOHA) station in the North Pacific Ocean. A new technique, called multivariate EOF reconstruction (mEOF-R), has been proposed. The mEOF-R technique is similar to the previously developed coupled pattern reconstruction (CPR) technique 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 a few modes are needed to explain most of the variance/covariance of the fields. The performances of CPR, single EOF reconstruction (sEOF-R), and mEOF-R have been compared with the results of residual GEM techniques 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. The mEOF-R method 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.

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 388
Author(s):  
Hao Cheng ◽  
Liang Sun ◽  
Jiagen Li

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.


2021 ◽  
Author(s):  
R. J. David Wells ◽  
Veronica A. Quesnell ◽  
Robert L. Humphreys ◽  
Heidi Dewar ◽  
Jay R. Rooker ◽  
...  

2010 ◽  
Vol 37 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Robert H. Byrne ◽  
Sabine Mecking ◽  
Richard A. Feely ◽  
Xuewu Liu

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