Observational Data Of The Noise Reduction And Dynamic Modeling Techniques

In this paper we introduce a new method of noise reduction based on reconstructed phase space and recurrence plots: searching average method (SAM). In the application of this method we should determine a localized scale, and the essence of localized scale determination is the appropriate evaluation of noise level of chaotic signal. In this contribution we evaluate the optimal localized scale through recurrence plots; by doing so, we enhance the effectiveness of noise reduction significantly. SAM is more time saving and accurate compare with other nonlinear denoise method. We apply SAM in the processing of Henon mapping time series shuffled by white gaussian noise and through comparison we verified the effectiveness of this method in noise reduction. As an illustration of the preceding considerations we study here time series data of daily temperatures from 1960 to 2000 of Huma distributed by CMA, and evaluate the effect of denoise through nonlinear prediction methods. Through comparison of normalized prediction error before and after noise reduction we find that with the same number of predictive steps, the errors of normalized prediction is significantly reduced which means through noise reduction by SAM we can enhance the predictability of temperature observational data. Using SAM we also making noise reduction on daily temperature series of 435 stations in China from 1960 to 2000 distributed by CMA, obtained the change of normalized predictive error distribution before and after noise reduction, which indicates the effectiveness of denoise in different regions. Results indicate that with the same number of predictive steps, normalized predictive errors of all stations are significantly reduced, e.g. an obvious increasing of predictability. With numbers of predictive steps less than three the predictability shows a tendency of increasing from south to north China. The extraction of information from nonlinear time series became crucial because of the hierarchy and nonstationarity of climate system. The recently raised Data-based Mechanism Model is to find those variables which strongly correlated with dependent variables and analyzed its possible correlations, then construct a model based on observational data through inversion. In this paper first we construct a possible nonlinear ordinary differential equation based on regression, and then through bi-directional difference we obtain the concrete form of equation, e.g. inversion model. Based on principle of regression we constructed a difference-integration equation, and obtain the memory index of discrete form of equation through least square method. Finally we develop a model for predictive test. In this paper we investigate the law of probability variation of precipitation using observational data of summer precipitation from 1951 to 1998 in the Yangtze delta, analyzed its caused and construct a model for prediction. Through comparison we find the ratio of correct predictions are greatly enhanced. And base on this, we study the character of temporal distributions of precipitation probability from the angle of small probability events using drought indices of 530 years, and try to induce law of spatiotemporal variation of small probability events.