Subtropical High Forecast Model of Least Square Support Vector Machine

Based on the methods of empirical orthogonal decomposition (EOF), wavelet frequency decomposition and least square support vector machine, a summer 500 hPa potential height forecasting model is established to describe the form and change of the subtropical high situation. First, 500 hPa potential height fields sequences on NCEP/NCAR are separated into the time coefficients and corresponding eigenvectors which are orthogonal to each other with the method of empirical orthogonal decomposition. Then fifteen time coefficient series corresponding with major eigenvector (square contribution of 96.2%) are extracted and each time coefficient is decomposed to relatively simple signals with the method of wavelet analysis. Then, each signal prediction model is set up with the method of least square support vector machine. Finally, the forecasting simple signals are used to reconstruct the corresponding forecasting time series with the method of wavelet decomposition, then, the forecasting time series and corresponding major eigenvector are used to reconstruct 500 hPa potential fields with the methods of empirical orthogonal decomposition. The reconstructed potential fields are the fields which are forecasting results. Through experiments and analysis of contrast on the prediction model, the results show that the proposed algorithm model based on the above ideas can basically describe the distribution of 500 hPa potential situation and basically forecast the location and intensity of subtropical high within seven days. And the results also show that the 10-15 day forecasting results by the model can be used for reference for the medium and long-term activity of the subtropical high. The results also show that the model exhibits its properties of simplicity, stability, flexibility and good prospect of application.