Title:
Application of Machine-Learning on the Multi-Parameter Observations in the Earth's Upper Atmosphere
Application of Machine-Learning on the Multi-Parameter Observations in the Earth's Upper Atmosphere
Time:
07/16/2022 06:00 PM PDT
07/16/2022 07:00 PM MDT
07/16/2022 06:00 PM PDT
07/16/2022 07:00 PM MDT
07/16/2022 08:00 PM CDT
07/16/2022 09:00 PM EDT
07/16/2022 09:00 PM EDT
07/17/2022 02:00 AM BST
07/17/2022 03:00 AM CEST
07/17/2022 09:00 AM Taiwan
07/17/2022 09:00 AM Taiwan
Field:
Physics
Physics
Sub-field:
Space Physics
Space Physics
為配合本研究發表時程，本次錄影將延遲上傳
Abstract:
The Ionospheric CONnection Explorer (ICON) spacecraft launched in the deep solar minimum of late 2019 and joined the troop of Earth’s upper atmosphere monitors including Global-scale Observations of the Limb and Disk (GOLD). Due to the spatial and temporal entanglement and limited coverage of data, integrating the space-borne observations from different platforms is a challenging task and usually requires support from whole atmospheric modeling. Integrating the observations from both ICON and GOLD provides a broader and more comprehensive view of the upper atmosphere, spatially and temporally. Taking the advantages of the rapid development of artificial intelligence, the applications of the machine learning algorithms and neural networks on space sciences is foreseeable.
The Ionospheric CONnection Explorer (ICON) spacecraft launched in the deep solar minimum of late 2019 and joined the troop of Earth’s upper atmosphere monitors including Global-scale Observations of the Limb and Disk (GOLD). Due to the spatial and temporal entanglement and limited coverage of data, integrating the space-borne observations from different platforms is a challenging task and usually requires support from whole atmospheric modeling. Integrating the observations from both ICON and GOLD provides a broader and more comprehensive view of the upper atmosphere, spatially and temporally. Taking the advantages of the rapid development of artificial intelligence, the applications of the machine learning algorithms and neural networks on space sciences is foreseeable.
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