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China Realizes Real-time Transmission of Deep-Sea Data in 3000-meter Depth
Jan 10, 2018
The research vessel "Kexue" has embarked its expedition in the Western Pacific since 26 November, 2017.
Chinese scientists recently announced that they had upgraded 8 sets of subsurface moorings to achieve "live transmission" of deep-sea data over the upper 1000-meter depth, and had realized real-time transmission of temperature, salinity, and three-dimensional velocity in 3000-meter depth, which was a new record.
Last year, they set out to overcome difficulties in the long-term transmission of real-time data from subsurface moorings, and succeeded in achieving real-time transmission of deep-sea data from one mooring for more than 280 days in a row, according to WANG Fan, senior scientist of Aoshan Talents Program of Qingdao National Laboratory for Marine Science and Technology (QDLM) and director of the Institute of Oceanology of the Chinese Academy of Sciences (IOCAS).
"Based on the previous work, real-time transmission of deep water data is being expanded towards the majority of the subsurface moorings in the CAS scientific observing network, and the depth range of real-time deep-sea data has been extended from 1000 to 3000 meters depth in this year." WANG said.
He added that the data could enhance capacity for exploring the deep ocean mysteries and for accelerating the establishment of marine climate forecast and disaster prevention and reduction system for China.
The CAS Scientific Observing Network in the Western Pacific is a key component of the "Two Oceans One Sea" observation system of QNLM. The construction of the real-time Scientific Observing Network in the Western Pacific has been supported by the Strategic Priority Research Program and Equipment Development Project of CAS, the Aoshan Science and Technology Innovation Plan and Wenhai Plan of QNLM.
"Kexue" will return to Qingdao in February 2018 after completing the scientific tasks for the maintenance and upgrade of observation network.
Figure: Time series of real-time deep water (approximately 3147-meter depth) temperature, salinity, and current speed in the Western Pacific. (Image by IOCAS)
Jan 10, 2018
The research vessel "Kexue" has embarked its expedition in the Western Pacific since 26 November, 2017.
Chinese scientists recently announced that they had upgraded 8 sets of subsurface moorings to achieve "live transmission" of deep-sea data over the upper 1000-meter depth, and had realized real-time transmission of temperature, salinity, and three-dimensional velocity in 3000-meter depth, which was a new record.
Last year, they set out to overcome difficulties in the long-term transmission of real-time data from subsurface moorings, and succeeded in achieving real-time transmission of deep-sea data from one mooring for more than 280 days in a row, according to WANG Fan, senior scientist of Aoshan Talents Program of Qingdao National Laboratory for Marine Science and Technology (QDLM) and director of the Institute of Oceanology of the Chinese Academy of Sciences (IOCAS).
"Based on the previous work, real-time transmission of deep water data is being expanded towards the majority of the subsurface moorings in the CAS scientific observing network, and the depth range of real-time deep-sea data has been extended from 1000 to 3000 meters depth in this year." WANG said.
He added that the data could enhance capacity for exploring the deep ocean mysteries and for accelerating the establishment of marine climate forecast and disaster prevention and reduction system for China.
The CAS Scientific Observing Network in the Western Pacific is a key component of the "Two Oceans One Sea" observation system of QNLM. The construction of the real-time Scientific Observing Network in the Western Pacific has been supported by the Strategic Priority Research Program and Equipment Development Project of CAS, the Aoshan Science and Technology Innovation Plan and Wenhai Plan of QNLM.
"Kexue" will return to Qingdao in February 2018 after completing the scientific tasks for the maintenance and upgrade of observation network.