scholarly journals Measurements of density contrast and sound-speed contrast for target strength estimation of Neocalanus copepods (Neocalanus cristatus and Neocalanus plumchrus) in the North Pacific Ocean

2009 ◽  
Vol 75 (6) ◽  
pp. 1377-1387 ◽  
Author(s):  
Ryuichi Matsukura ◽  
Hiroki Yasuma ◽  
Hiroto Murase ◽  
Shiroh Yonezaki ◽  
Tetsuichiro Funamoto ◽  
...  
2008 ◽  
Vol 66 (2) ◽  
pp. 258-263 ◽  
Author(s):  
Michael A. Guttormsen ◽  
Christopher D. Wilson

Abstract Guttormsen, M. A. and Wilson, C. D. 2009. In situ measurements of capelin (Mallotus villosus) target strength in the North Pacific Ocean. – ICES Journal of Marine Science, 66: 258–263. In situ measurements of capelin (Mallotus villosus) target strength (TS) were collected during summer 2001–2003 near Kodiak Island in the Gulf of Alaska, using a calibrated EK500 echosounder with 38 and 120 kHz split-beam transducers. Targets were detected over dispersed, night-time aggregations using standard acoustic methods, then filtered using a quality-control algorithm to reject invalid targets. The 38 kHz-based, fitted model estimate was TS = 20 log10L− 70.3 (r2 = 0.30), where L is total length of fish. Compared with other studies, the TS-fitted model at 38 kHz was similar to that calculated from swimbladder morphology measurements from St Lawrence estuary capelin (TS = 20 log10L− 69.3), but resulted in greater estimates than models based on in situ measurements of capelin TS in the Barents Sea (TS = 19.1 log10L−74.0) and northern Atlantic Ocean (TS = 20 log10L − 73.1). The large intraspecific variability exhibited in the fitted TS – L models for this species suggests the use of TS measurements from the geographic region where the data were collected.


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

Sign in / Sign up

Export Citation Format

Share Document