{"refrec":{"BRefID":328174,"RR":"<b>Zheng, G.; Li, X.; Zhang, R.-H.; Liu, B.</b> (2020). Purely satellite data–driven deep learning forecast of complicated tropical instability waves. <i>Science Advances 6(29)</i>: eaba1482. <a href=\"https://dx.doi.org/10.1126/sciadv.aba1482\" target=\"_blank\">https://dx.doi.org/10.1126/sciadv.aba1482</a>","BEntID":321651,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>Science Advances 6(29)</i>: eaba1482. <a href=\"https://dx.doi.org/10.1126/sciadv.aba1482\" target=\"_blank\">https://dx.doi.org/10.1126/sciadv.aba1482</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Zheng, G.; Li, X.; Zhang, R.-H.; Liu, B.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Zheng, G. <i>et al.</i>","Englishabstract":"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. 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