Mineralogy of aeolian dust reaching the North Pacific Ocean: 2. Relationship of mineral assemblages to atmospheric transport patterns

1994 ◽  
Vol 99 (D10) ◽  
pp. 21025 ◽  
Author(s):  
John Merrill ◽  
Eve Arnold ◽  
Margaret Leinen ◽  
Clark Weaver
Keyword(s):  
Pacific Ocean ◽  
North Pacific ◽  
Atmospheric Transport ◽  
North Pacific Ocean ◽  
Aeolian Dust ◽  
The North ◽  
Mineral Assemblages ◽  
Transport Patterns ◽  
Relationship Of ◽  
The North Pacific

Mineralogy of aeolian dust reaching the North Pacific Ocean: 1. Sampling and analysis

1994 ◽  
Vol 99 (D10) ◽  
pp. 21017 ◽  
Author(s):  
Margaret Leinen ◽  
Joseph M. Prospero ◽  
Eve Arnold ◽  
Marsha Blank
Keyword(s):  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Aeolian Dust ◽  
The North ◽  
The North Pacific

Review of recent developments in aeolian dust signals of sediments from the North Pacific Ocean based on magnetic minerals

2019 ◽  
Vol 157 (5) ◽  
pp. 790-805
Author(s):  
Qiang Zhang ◽  
Qingsong Liu ◽  
Youbin Sun
Keyword(s):  
Pacific Ocean ◽  
North Pacific ◽  
High Efficiency ◽  
Magnetic Measurements ◽  
North Pacific Ocean ◽  
Aeolian Dust ◽  
Source Areas ◽  
The North ◽  
Recent Developments ◽  
The North Pacific

AbstractThe North Pacific Ocean (NPO) has received abundant aeolian dust transported by westerlies from the Asian inland. The aeolian components preserved in NPO sediments record information on palaeoclimatic and palaeoenvironmental changes in Asian source areas at different timescales. Previous studies have systematically investigated the source–sink effect of aeolian dust using the sedimentology, geochemistry, isotope and magnetic methods. In this study, we focus more on recent developments of aeolian signals in NPO sediments obtained by magnetic approaches. Generally, aeolian components contain a mixture of magnetite, maghemite, hematite and goethite of different origins. Magnetic properties (mineral category, concentration and particle size) of these minerals are modulated primarily by climatic/environmental conditions in source areas and sorting effects during the transportation process. Compared with the other methods, magnetic measurements have the advantages of non-sample destruction, high sensitivity and high efficiency. Finally, future studies are also discussed to address the importance of magnetism for tracing the dynamic transportation processes of the aeolian dust.

scholarly journals Neural Network Approach to Retrieving Ocean Subsurface Temperatures from Surface Parameters Observed by Satellites

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 388
Author(s):  
Hao Cheng ◽  
Liang Sun ◽  
Jiagen Li
Keyword(s):  
Neural Network ◽  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Sea Surface ◽  
Satellite Measurements ◽  
Surface Parameters ◽  
The North ◽  
The North Pacific ◽  
Bpnn Model

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.

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