期刊
SCIENCE ADVANCES
卷 6, 期 29, 页码 -出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.aba1482
关键词
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资金
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB42000000, XDA19090103]
- Key R&D Project of Shandong Province [2019JZZY010102]
- National Natural Science Foundation of China [41676167, 41776183]
- Key Deployment Project of Center for Ocean Mega-Science, CAS [COMS2019R02]
- CAS Program [Y9KY04101L]
- Project of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography [SOEDZZ2003]
Forecasting fields of oceanic phenomena has long been dependent on physical equation-based numerical models. The challenge is that many natural processes need to be considered for understanding complicated phenomena. In contrast, rules of the processes are already embedded in the time-series observation itself. Thus, inspired by largely available satellite remote sensing data and the advance of deep learning technology, we developed a purely satellite data-driven deep learning model for forecasting the sea surface temperature evolution associated with a typical phenomenon: a tropical instability wave. During the testing period of 9 years (2010-2019), our model accurately and efficiently forecasts the sea surface temperature field. This study demonstrates the strong potential of the satellite data-driven deep learning model as an alternative to traditional numerical models for forecasting oceanic phenomena.
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