With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multi...With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters.展开更多
Strategic Priority Research Program on Space Science has gained remarkable achievements. Space Environment Prediction Center(SEPC) affiliated with the National Space Science Center(NSSC) has been providing space weath...Strategic Priority Research Program on Space Science has gained remarkable achievements. Space Environment Prediction Center(SEPC) affiliated with the National Space Science Center(NSSC) has been providing space weather services and helps secure space missions. Presently, SEPC is capable to offer a variety of space weather services covering many phases of space science missions including planning, design, launch,and orbital operation. The service packages consist of space weather forecasts, warnings, and effect analysis that can be utilized to avoid potential space weather hazard or reduce the damage caused by space storms,space radiation exposure for example. Extensive solar storms that occurred over Chinese Ghost Festival(CGF)in September 2017 led to a large enhancement of the solar energetic particle flux at 1 AU, which affected the near Earth radiation environment and brought great threat to orbiting satellites. Based on the space weather service by SEPC, satellite ground support groups collaborating with the space Tracking, Telemetering and Command system(TT&C) team were able to take immediate measures to react to the CGF solar storm event.展开更多
In space weather forecasting, forecast verification is necessary so that the forecast quality can be assessed. This paper provides an example of how to choose and devise verification methods and techniques according t...In space weather forecasting, forecast verification is necessary so that the forecast quality can be assessed. This paper provides an example of how to choose and devise verification methods and techniques according to different space weather forecast products. Solar proton events(SPEs) are hazardous space weather events, and forecasting them is one of the major tasks of the Space Environment Prediction Center(SEPC) at the National Space Science Center of the Chinese Academy of Sciences. Through analyzing SPE occurrence characteristics, SPE forecast properties, and verification requirements at SEPC, verification methods for SPE probability forecasts are identified, and verification results obtained. Overall, SPE probability forecasts at SEPC exhibit good accuracy, reliability, and discrimination. Compared with climatology and persistence forecasts, the SPE forecasts are more accurate. However, the forecasts for SPE onset days are substantially underestimated and need to be considerably improved.展开更多
In-situ solar wind measurement at a solar longitude separated from the earth in interplanetary space is expected to provide a great progress in practical space weather forecast, which has been confirmed by some recent...In-situ solar wind measurement at a solar longitude separated from the earth in interplanetary space is expected to provide a great progress in practical space weather forecast, which has been confirmed by some recent studies. We introduce geoeffective solar wind conditions in correlation analysis between STEREO and ACE measurements. We sort solar wind data of ACE by using geomagnetic condition, and evaluate actual ability for predicting geoeffective solar wind arrival at ACE from STEREO-A and B solar wind measurement, by assuming simple corotating structures in interplanetary space. The results show that geomagnetic disturbances are more difficult to be predicted than quiet intervals, suggesting that the simple correlation method of solar wind measurement at separated solar longitude is not enough for accurately predicting geomagnetic disturbances, even though the correlation seems generally high. Although in-situ solar wind monitoring at a vantage point trailing behind the earth would definitely improve the prediction capability of solar wind structure arriving at the terrestrial plasma environment, we emphasize that the predictive ability of geoeffective disturbances would still remain low. We suggest that more sophisticated prediction schemes should be developed.展开更多
A nonlinear autoregressive approach with exogenous input is used as a novel method for statistical forecasting of the disturbance storm time index, a measure of space weather related to the ring current which surround...A nonlinear autoregressive approach with exogenous input is used as a novel method for statistical forecasting of the disturbance storm time index, a measure of space weather related to the ring current which surrounds the Earth, and fluctuations in disturbance storm time field strength as a result of incoming solar particles. This ring current produces a magnetic field which opposes the planetary geomagnetic field. Given the occurrence of solar activity hours or days before subsequent geomagnetic fluctuations and the potential effects that geomagnetic storms have on terrestrial systems, it would be useful to be able to predict geophysical parameters in advance using both historical disturbance storm time indices and external input of solar winds and the interplanetary magnetic field. By assessing various statistical techniques it is determined that artificial neural networks may be ideal for the prediction of disturbance storm time index values which may in turn be used to forecast geomagnetic storms. Furthermore, it is found that a Bayesian regularization neural network algorithm may be the most accurate model compared to both other forms of artificial neural network used and the linear models employing regression analyses.展开更多
基金Supported by National Natural Science Foundation of China(41574181)。
文摘With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters.
文摘Strategic Priority Research Program on Space Science has gained remarkable achievements. Space Environment Prediction Center(SEPC) affiliated with the National Space Science Center(NSSC) has been providing space weather services and helps secure space missions. Presently, SEPC is capable to offer a variety of space weather services covering many phases of space science missions including planning, design, launch,and orbital operation. The service packages consist of space weather forecasts, warnings, and effect analysis that can be utilized to avoid potential space weather hazard or reduce the damage caused by space storms,space radiation exposure for example. Extensive solar storms that occurred over Chinese Ghost Festival(CGF)in September 2017 led to a large enhancement of the solar energetic particle flux at 1 AU, which affected the near Earth radiation environment and brought great threat to orbiting satellites. Based on the space weather service by SEPC, satellite ground support groups collaborating with the space Tracking, Telemetering and Command system(TT&C) team were able to take immediate measures to react to the CGF solar storm event.
基金supported by the National Basic Program of China (Grant No. 2012CB825600)
文摘In space weather forecasting, forecast verification is necessary so that the forecast quality can be assessed. This paper provides an example of how to choose and devise verification methods and techniques according to different space weather forecast products. Solar proton events(SPEs) are hazardous space weather events, and forecasting them is one of the major tasks of the Space Environment Prediction Center(SEPC) at the National Space Science Center of the Chinese Academy of Sciences. Through analyzing SPE occurrence characteristics, SPE forecast properties, and verification requirements at SEPC, verification methods for SPE probability forecasts are identified, and verification results obtained. Overall, SPE probability forecasts at SEPC exhibit good accuracy, reliability, and discrimination. Compared with climatology and persistence forecasts, the SPE forecasts are more accurate. However, the forecasts for SPE onset days are substantially underestimated and need to be considerably improved.
文摘In-situ solar wind measurement at a solar longitude separated from the earth in interplanetary space is expected to provide a great progress in practical space weather forecast, which has been confirmed by some recent studies. We introduce geoeffective solar wind conditions in correlation analysis between STEREO and ACE measurements. We sort solar wind data of ACE by using geomagnetic condition, and evaluate actual ability for predicting geoeffective solar wind arrival at ACE from STEREO-A and B solar wind measurement, by assuming simple corotating structures in interplanetary space. The results show that geomagnetic disturbances are more difficult to be predicted than quiet intervals, suggesting that the simple correlation method of solar wind measurement at separated solar longitude is not enough for accurately predicting geomagnetic disturbances, even though the correlation seems generally high. Although in-situ solar wind monitoring at a vantage point trailing behind the earth would definitely improve the prediction capability of solar wind structure arriving at the terrestrial plasma environment, we emphasize that the predictive ability of geoeffective disturbances would still remain low. We suggest that more sophisticated prediction schemes should be developed.
文摘A nonlinear autoregressive approach with exogenous input is used as a novel method for statistical forecasting of the disturbance storm time index, a measure of space weather related to the ring current which surrounds the Earth, and fluctuations in disturbance storm time field strength as a result of incoming solar particles. This ring current produces a magnetic field which opposes the planetary geomagnetic field. Given the occurrence of solar activity hours or days before subsequent geomagnetic fluctuations and the potential effects that geomagnetic storms have on terrestrial systems, it would be useful to be able to predict geophysical parameters in advance using both historical disturbance storm time indices and external input of solar winds and the interplanetary magnetic field. By assessing various statistical techniques it is determined that artificial neural networks may be ideal for the prediction of disturbance storm time index values which may in turn be used to forecast geomagnetic storms. Furthermore, it is found that a Bayesian regularization neural network algorithm may be the most accurate model compared to both other forms of artificial neural network used and the linear models employing regression analyses.
