【摘 要】
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The monthly precipitation observational data of the Yangtze River delta are transformed into the temporal evolution of precipitation probability (PP), and its h
【机 构】
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Department of Physics, Yangzhou University, Yangzhou 225009, China“,”Institute of Atmospheric Physic
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The monthly precipitation observational data of the Yangtze River delta are transformed into the temporal evolution of precipitation probability (PP), and its hierarchically distributive characters have been revealed in this paper.Research results show that precipitation of the Yangtze River delta displays the interannual and interdecadal characters and the periods are all significant at a confidence level of more than 0.05. The interdecadM is an important time scale,because it is on the one hand a disturbance of long period changes, and on the other hand it is also the background for interannual change. The interdecadal and 3-7y oscillations have different motion laws in the data-based mechanism self-memory model (DAMSM). Meanwhile, this paper also provides a new train of thought for dynamic modelling.Because this method only involves a certain length of data series, it can be used in many fields, such as meteorology,hydrology, seismology, and economy etc, and thus has a bright perspective in practical applications.
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