引用本文: | 王继光,张韧,郁文贤,等.基于卫星云图历史资料反演云团非线性预测模型[J].国防科技大学学报,2007,29(5):41-47.[点击复制] |
WANG Jiguang,ZHANG Ren,YU Wenxian,et al.Non-linear Forecast Model of Cloud Clusters Movement Based on Parameters Retrieval of Historical Satellite Cloud Pictures Time Series[J].Journal of National University of Defense Technology,2007,29(5):41-47[点击复制] |
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基于卫星云图历史资料反演云团非线性预测模型 |
王继光1, 张韧2, 郁文贤1, 洪梅2 |
(1.国防科技大学 电子科学与工程学院,湖南 长沙 410073;2.解放军理工大学 气象学院,江苏 南京 211101)
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摘要: |
针对线性预测方法难以有效描述云团的非线性、非平稳变化的困难,基于经验正交函数分解(EOF)和遗传算法参数优化结合的思想,提出了一条云团非线性预测模型反演的方法途径。首先将卫星云图序列作EOF的时、空分解;在此基础上,引入遗传算法对EOF的时间系数序列进行了动力模型重构和模型参数反演,建立了EOF时间系数的非线性微分方程组;再通过时、空函数合成,构造了云团演变的动力预报模型。试验结果表明,反演的云团预报模型能较为合理地描述特定季节区域内云团演变的基本趋势,预测结果与实际云图的主要特征基本相符,尤其是实现了云图3h以上的中、长时效的客观预测。 |
关键词: 卫星云图 云团预测 正交分解 遗传算法 参数优化 |
DOI: |
投稿日期:2007-03-13 |
基金项目:国家部委资助项目 |
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Non-linear Forecast Model of Cloud Clusters Movement Based on Parameters Retrieval of Historical Satellite Cloud Pictures Time Series |
WANG Jiguang1, ZHANG Ren2, YU Wenxian1, HONG Mei2 |
(1.College of Electronic Science and Engineering, National Univ. of Defense Technology, Changsha 410073, China;2.Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101,China)
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Abstract: |
Due to the fact that linear prediction method is difficult to describe the nonlinear, non-stationary changes of cloud clusters, a technique of retrieval nonlinear clouds clusters forecast model, based on the idea of combining the decomposition of empirical orthogonal function (EOF) and the genetic algorithm optimization parameters, was presented. Firstly, satellite image sequences were temporal-spatially decomposed by EOF. On this basis, genetic algorithms were introduced to make the dynamic model reconstruction and model parameters optimization retrieval of EOF time coefficients sequence, and a nonlinear differential equations of EOF time coefficients were established. Then, by the EOF temporal-spatial functions synthesis, a dynamic forecast model of cloud clusters evolution was structured. The experimental results showed that the retrieved clouds dynamic forecast model was more reasonable in describing the cloud evolution of the underlying trend in particular seasons and region, and the forecast results were better accorded with the basic characteristics of actual satellite cloud pictures. Especially, a middle-long period over three hours objective cloud clusters predictions was achieved. |
Keywords: satellite cloud pictures cloud clusters forecast empirical orthogonal function genetic algorithms parameter optimization |
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