变分伴随数据同化在海表面温度预报中的应用研究

A study on the application of variational adjoint data assimilation for numerical prediction of sea surface temperature

将变分伴随数据同化技术应用于海表面温度 (SST)数值预报 .采用中国近海海表面温度短期数值预报模式 ,将船舶测报海表面温度同化到该模型中 ,对SST初始场进行优化 .文中给出了中国近海SST数值预报同化模型 5d试报结果与观测值的比较 ,整个区域的均绝差由同化前的 2 .71℃降至 0 .87℃ ,即变分伴随数据同化对改进SST数值预报的效果是比较明显的 。

The variational adjoint method is used for assimilating the observed data into the sea surface temperature(SST) numerical models. The frame of SST model adopted here is based on 'the short term forecasting model of offshore SST in China seas'. The experiment of SST hindcasting (5 d) is conducted when the initial field of SST is estimated by assimilating the SST ship report data into the model. The result with the absolute mean error decreased from 2.71 ℃ to 0.87 ℃ in the whole sea area shows that the variational adjoint calculations have successfully improved the accuracy of SST hindcasting. This experiment will guide a novel way towards the initialization for operational forecasting of SST.