The Evolutionary Modeling and Short-range Climatic Prediction for Meteorological Element Time Series
YU Kang-qing · 2005
The evolutionary modeling(EM),which is developed from genetic programming(GP),is a relatively new technique that is an adaptive method for solving computational problems in complex systems which are of chaotic character and nonlinear variation with time in many fields.The use of EM for the observed time series of precipitation in flood season(May-September) at Wuhan station is studied in this paper.The time series of precipitation is split into two parts: one includes macro climatic timescale period waves that are affected by some relative steady climatic factors such as astronomical factors(like sunspot number etc.) as well as other factors that are known and unknown,the other includes micro climatic timescale period waves superimposed on the macro one.The evolutionary modeling(EM) is supposed to be adept at simulating the former part because it creates the nonlinear ordinary diferential equation(NODE) based upon the observed time series.The natural fractals(NF) are used to simulate the latter part.The final prediction is the sum of values from both methods,so the model can reflects multi-time scale satisfactory for climatic prediction operation.The NODE can suggest the data vary with time,which is benefit to think over climatic analysis and short-range climatic prediction.Comparison in principle between EM and a linear modeling AR(p) indicates that the EM is a much better method to simulate the complex time series being of nonlinear characteristics.