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THE MONTHLY prediction EXPERIMENTS USING A COUPLED ANALOGY-dynamical MODEL

Acta meteorologica Sinica 1991年第1期5卷 8-15页

作者：黄建平王绍武 Department of Geophysics Peking UniversityBeijing

Considering from point of view of the dynamics,it is convenient to regard the field to be predicted as a small disturbance superposed on the historical analogous field,and thus the statistical technique can be used in combining with the *** this line,a coupled atmosphere-earth surface analogy-dynamical model is formulated and applied to making monthly prediction. This approach facilitated the utility of the useful information contained in both the historical data set and the initial field to improve the dynamic model based solo on the latter and show better skill in prediction.

关键词： long-range prediction dynamical prediction historical analogy analogy-dynamical approach

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Preliminary Studies on Predicting the Tropical Indian Ocean Sea Surface Temperature through Combined Statistical Methods and Dynamic ENSO prediction

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Atmospheric and Oceanic Science Letters 2013年第1期6卷 52-59页

作者： WANG Li-Wei ZHENG Fei ZHU Jiang International Center for Climate and Environment Science(ICCES) Institute of Atmospheric PhysicsChinese Academy of Sciences Graduate University of Chinese Academy of Sciences Beijing Institute of Applied Meteorology

The sea surface temperature （SST） in the In- dian Ocean affects the regional climate over the Asian continent mostly through a modulation of the monsoon system. It is still difficult to provide an a priori indication of the seasonal variability over the Indian Ocean. It is widely recognized that the warm and cold events of SST over the tropical Indian Ocean are strongly linked to those of the equatorial eastern Pacific. In this study, a statistical prediction model has been developed to predict the monthly SST over the tropical Indian Ocean. This model is a linear regression model based on the lag relationship between the SST over the tropical Indian Ocean and the Nino3.4 （5°S-5°N, 170°W-120°W） SST Index. The pre- dictor （i.e., Nino3.4 SST Index） has been operationally predicted by a large size ensemble E1 Nifio and the Southern Oscillation （ENSO） forecast system with cou- pled data assimilation （Leefs_CDA）, which achieves a high predictive skill of up to a 24-month lead time for the equatorial eastern Pacific SST. As a result, the prediction skill of the present statistical model over the tropical In- dian Ocean is better than that of persistence prediction for January 1982 through December 2009.

关键词： Indian Ocean SST ENSO prediction statisti- cal method dynamical prediction

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