| 海-气耦合距平滤波模式的月、季数值预报 |
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| 引用本文： | 巢纪平,王晓晞,陈英仪,王立治.海-气耦合距平滤波模式的月、季数值预报[J].Acta Meteorologica Sinica,1986,44(4):417-425. |
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| 作者姓名： | 巢纪平 王晓晞 陈英仪 王立治 |
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| 作者单位： | 国家海洋环境预报研究中心
(巢纪平，王晓晞，陈英仪)，国家海洋环境预报研究中心(王立治) |
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| 摘 要： | 本文用海—气耦合三层距平滤波模式(AFM)作了1976—1977以及1982—1983两个EINino事件年冬季八个月预报个例实验。其结果表明,该模式成功地预报了大尺度月地表温度距平场,预报与实况的相关系数基本上都超过了惯性预报。与距平大气环流模式(AGCM)相比,两者的结果相差不大,但AFM可节省近一百倍的计算时间。与此同时,我们还作了季节预报试验,即提前三个月作月预报,完成了1977年2月、3月和1983年2月、3月四个试验例子。结果表明,用该模式作大尺度环流的季节异常预报的潜力是存在的。最后,结合本文的结果,我们把提出距平滤波模式以来近十年的工作作了小结,对模式的预报能力作了评价。
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| 收稿时间： | 5/6/1985 12:00:00 AM |
| 修稿时间： | 1985/8/30 0:00:00 |
MONTHLY AND SEASONAL NUMERICALFORECASTS BY USING THE ANOMALYOCEAN-ATMOSPHERE COUPLED MODEL |
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| Chao Jiping,Wang Xiaoxi,Chen Yingyi and Wang Lizhi.MONTHLY AND SEASONAL NUMERICALFORECASTS BY USING THE ANOMALYOCEAN-ATMOSPHERE COUPLED MODEL[J].Acta Meteorologica Sinica,1986,44(4):417-425. |
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| Authors: | Chao Jiping Wang Xiaoxi Chen Yingyi and Wang Lizhi |
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| Affiliation: | National Research Center for Marine Environmental Forecasts, Beijing;National Research Center for Marine Environmental Forecasts, Beijing;National Research Center for Marine Environmental Forecasts, Beijing;National Research Center for Marine Environmental Forecasts, Beijing |
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| Abstract: | Monthly predictions for eight winter months during 1976-1977 and 1982-1983 El Nino events were performed by using a three-layer anomalous filtered model (AFM) in which transient Rossby waves were filterd.The results show that this model predicts successfully the large-scale patterns of the monthly mean surface temperature anomalies.The correlation coefficients between the observations and the predictions are higher than those of persistence predictions.By comparison with the anomalous general circulation model (AGCM) the AFM gives almost identical results,but the computer time required for running the AFM is almost 100 times less than that required for running the AGCM.It is also shown that the results of the three-layer model are better than those of the one-layer model.In the meanwhile,four seasonal forecasts were also carried out by using the same model.It seems to be shown that the AFM possesses potential ability in predicting seasonal large scale circulation anomalies. |
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