Short-medium-term forecast of SST over western North Pacific based on ConvLSTM
Despite of the small change in short-term variation of Sea Surface Temperature(SST),the change plays an important role in determining the occurrence and development of ocean vortices,ocean fronts and tropical cyclones.Therefore,short-term SST forecast is of great significance and requires high accuracy.In this study,to make a continuous forecast of 7-day SST over a certain area in western North Pacific,a deep learning model based on the ConvLSTM was adopted by using the two features,namely,SST and temperature advection.The forecast results of this two-feature ConvLSTM were compared with not only those of one-feature(i.e.,SST)ConvLSTM but also those of HYbrid Coordinate Ocean Model(HYCOM).Results show that,within the 7-day forecast time,the addition of the temperature advection feature can largely improve the forecast skill of ConvLSTM,which even beyond HYCOM.Moreover,this study extended the forecasting time to 30 days,and analyzed the forecast skill of the ConvLSTM model in different seasons.It was found that the ConvLSTM model exhibits relatively high(low)forecast skill in spring and autumn(summer and winter).
deep LearningConvLSTM modelSST forecastwestern North Pacific
国家科技期刊平台
NSTL
万方数据
