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一种动态数据的新建模法及其预报应用
NEW APPROACH TO DYNAMIC DATA MODELING AND ITS APPLICATION TO PRECIPITATION FORECASTING
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摘要: 文章提出了一种新的动态数据建模法, 利用观测的数据序列, 先用双向差分原理反导出一个非线性常微分方程。 以此作为微分动力核, 然后运用自忆性原理建立预报模式, 我们称之为数据机理自记忆模式(Data-based Mechanism Self-memory Model), 简称为数忆模式, 缩写为 DAMSM。 多个实例计算表明, 数忆模式的预报准确率是比较令人满意的, 给出了长江三角洲夏季降水年际预报的实例。Abstract: By use of an observed data series a new dynamic data modeling has been proposed. Taking a nonlinear ordinary differential equation which is retrieved from the data series based on the bilateral difference principle as a dynamic kernel, with the self-memorization principle a forecast model can be established, which is called the DAta-based Mechanistic Self-memory Model (DAMSM). Some computing cases show that the forecasting accuracy of the DAMSM is quite satisfactory. An example of inter-annual precipitation prediction in summer in the Yangtze delta is given.
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