四维变分同化技术在优化Z-C预测模式初始场中的应用试验

APPLICATION OF THE FOUR DIMENSIONAL VARIATIONAL DATA ASSIMILATION TECHNIQUE ON OPTIMIZING THE INITIAL CONDITIONS OF Z-C MODEL

文中使用四维变分同化技术将海温观测资料同化到 Zebiak- Cane模式中 ,通过优化模式的初始场提高了模式的预报技巧。通过用理想场进行检验 ,说明所建立的同化伴随模式是正确的。用文中建立的四维变分同化模式以 1 997年 1月为初始场所做的预报结果与实况相比 ,结果较好。这对今后

The four dimensional variational data assimilation (4DVAR) technique is applied in the Zebiak Cane ENSO forecasting model for optimizing its initial conditions by using the adjoint method. The performance of adjoint model is checked via an ideal experiment. It suggests that ENSO forecasting model based on the adjoint model of 4DVAR technique has an ability of assimilating the observed sea surface temperature data. Comparing to the observational Nino3 Index, the simulated and forecasted one produced using the present model generally agree with that of observational during the period of 1997-1999. This suggests that Zebiak Cane ENSO forecasting model has been improved by using the 4DVAR technique.