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

MA Ji-rui; HAN Gui-jun; LI Dong

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MA Ji-rui, HAN Gui-jun, LI Dong. A study on the application of variational adjoint data assimilation for numerical prediction of sea surface temperature[J]. Haiyang Xuebao, 2002, 24(5): 1-7.

Citation:

MA Ji-rui, HAN Gui-jun, LI Dong. A study on the application of variational adjoint data assimilation for numerical prediction of sea surface temperature[J]. Haiyang Xuebao, 2002, 24(5): 1-7.

MA Ji-rui, HAN Gui-jun, LI Dong. A study on the application of variational adjoint data assimilation for numerical prediction of sea surface temperature[J]. Haiyang Xuebao, 2002, 24(5): 1-7.

Citation:

MA Ji-rui, HAN Gui-jun, LI Dong. A study on the application of variational adjoint data assimilation for numerical prediction of sea surface temperature[J]. Haiyang Xuebao, 2002, 24(5): 1-7.

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

1.
National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, China
2.
National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, China;Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Received Date: 2001-06-28
Rev Recd Date: 2001-09-10

Abstract

Abstract

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.
- variational adjoint data assimilation,
- SST model,
- numerical prediction,
- China seas

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References(11)

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