The Evolutionary Modeling and Short-range Climatic Prediction for Meteorological Element Time Series

1. Introduction Let us suppose that the meteorological element series is the set of solution by integrating a perfect cli- matic numerical model with certain initial conditions, boundary conditions etc., thus it is also the concen- trated expression of nonlinear interaction between all climatic factors (including itself) in the model. Be- cause of limited understanding the mechanism of cli- matic system changes, the unsolved problems are not less than the solved ones in the climatic numerical …

The Evolutionary Modeling and Short-range Climatic Prediction for Meteorological Element Time Series

The time series of precipitation in flood season (May-September) at Wuhan Station, which is set as an example of the kind of time series with chaos characters, is split into two parts: One includes macro climatic timescale period waves that are affected by some relatively steady climatic factors such as astronomical factors (sunspot, etc.), some other known and/or unknown factors, and the other includes micro climatic timescale period waves superimposed on the macro one. The evolutionary modeling (EM), which develops from genetic programming (GP), is supposed to be adept at simulating the former part because it creates the nonlinear ordinary differential equation (NODE) based upon the data series. The natural fractals (NF) are used to simulate the latter part. The final prediction is the sum of results from both methods, thus the model can reflect multi-time scale effects of forcing factors in the climate system. The results of this example for 2002 and 2003 are satisfactory for climatic prediction operation. The NODE can suggest that the data vary with time, which is beneficial to think over short-range climatic analysis and prediction. Comparison in principle between evolutionary modeling and linear modeling indicates that the evolutionary one is a better way to simulate the complex time series with nonlinear characteristics.