| With the development of China’s aerospace industry, we placed more and more attention to space environment and space environment forecast. Geomagnetic storm is one of the main reasons to make the failure of space disasters and some technical system of ground. So, geomagnetic forecast in various research and business areas are hot spots. In order to predict the occurrence of geomagnetic storms, the paper main to study the method of geomagnetic storm forecast, providing reliable data and better service for space weather operations.Geomagnetic disturbance can cause near-earth space environment’s (including the ionosphere and magnetosphere) a series of changes, and Kp index is the important reference index in disturbance of space weather. In this study, Hp component data monitored by GOES-8satellite of geosynchronous orbit was used by us. By analyzing statistical relation between Kp index and Hp component width of fluctuation, the results show that the change of Hp component and Kp index have good correlation. Based on the change of Hp component, we calculate the same time Kp index, and then Kp index forecast model is established by means of regression analysis and RBF neural networks. Monitoring results show prediction method has certain validity and practicability especially that Kp index calculated by artificial neural networks is very consistent with its measured values. By using this method, we could not rely on the data form geomagnetic diction to forecast geomagnetic disturbance quickly, to provide reference in time for space weather guarantee. In addition, our country is about to launch FY-4satellite, which has magnetic detector. Mean-while, this research will provide the foundation for our own data application.The solar wind speed is an important parameter to determine the occurrence and magnitude of geomagnetic disturbance. So it plays an important role in the medium-term forecast of geomagnetic storms. And because satellite STEREO_B is located upper reaches of Earth, the solar wind speed it detected play an important role in the reference. In this research, the wavelet analysis method was used to discuss the27-day periodicities characteristic of the solar wind speed detected by ACE and STEREO_B from2008to2011. Using the method of unary linear regression, the linear equation of solar wind speed is calculated. From the analysis and calculation results detected by ACE and STEREO_B, it is shown that the solar wind speed in8-16d and16-32d frequency domain is significant. The solar wind speed detected by two satellites presents significant27-day periodicities in parts of the same frequency domain. And it presents better correlation in the year when solar activity is low. As an application example, solar wind data detected by STEREO_B is applied to predicting the solar wind speed of ACE from2012-10-1to2012-10-17. The results show that the trend of predicted values and measured values are consistent, although the CME has a certain influence on the predicted values. Through this research, the relationship of solar wind speed detected from ACE and STEREO_B was preliminarily studied, and the solar wind speed was detected by satellite STEREO_B a few days before it came to the earth. So this work laid the foundation to establish the medium-term forecast models for the non-recurrent geomagnetic storms.The other detection data, such as cosmic rays and so on, they play an important role in space weather forecasting. In the fourth chapter, cosmic rays and solar wind speed are analyzed and discussed. Low years in solar activity, the correlation is good, negative correlation is showed. And this work will provide the foundation for the development of new data (cosmic rays) forecast methods. |
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