Solar Wind And Geomagnetic Storm Prediction Based On Solar Extreme Ultraviolet Images

Solar wind is a stream of charged particles emanating from solar corona.Plasma parameters of solar wind not only affect the environmental state of interplanetary space,but also are inputs of other models,such as geomagnetic storm model.High speed solar wind originating from coronal hole?CH?will induce geomagnetic storms and other disturbances when it reaches the Earth,which will affect spacecrafts and ground technical system.During propagation in the interplanetary space,high speed solar wind will compress low speed streams,thus a co-rotating interaction region?CIR?forms.During the declining and minimum phases of solar cycles,geomagnetic storms caused by CIRs are dominant.Therefore,the prediction of solar wind,high-speed streams?HSSs?and geomagnetic storms caused by CIR is important in space weather.At present,solar wind speed empirical models and geomagnetic storm models all have shortcomings.The shortcomings of solar wind speed empirical models are as follows:?1?Forecasting index of the model only reflects the area of coronal hole,while besides area other characteristics also affect solar wind speed;?2?Empirical model only can predict peak speed and peak time of high-speed solar wind relatively accurately,but cannot predict other characteristics of speed change in detail.In addition,geomagnetic storm models with solar wind parameters as input predict geomagnetic storms in advance only by 1 hour.For solving above problems,based on solar extreme ultraviolet images and near-earth solar wind observation data,we build up solar wind empirical models,study the statistical relationship between coronal holes,high-speed streams and geomagnetic storms.The main research and conclusions of this dissertation are as follows:1.In order to enhance performance of solar wind empirical prediction models,we improve the forecasting indexbased on the brightness of solar extreme ultraviolet images(the improved indices are30 and90),and propose a new modeling method"event method"which is different from the"maximum/minimum method"used in the previous model.We construct 6 solar wind empirical models based on the combination of 3 forecasting index(coronal hole area,30 and90)and 2modeling methods?maximum/minimum method and event method?and predict solar wind speed in year 2011-2018.We evaluate 6 models from the perspectives of continuous time series and high-speed stream event.From the perspective of continuous time series,the mean square error of 6 empirical models is 102-112 km/s,and the average absolute error is 80-86 km/s.From the perspective of high-speed stream event,probability of detection,positive predictive value and threat score of 6 models are 73-81%,66%-73%and 0.55-0.61 respectively.Among six models,the model adopted event method and P_CH30 index has the best prediction results.The mean absolute error of high-speed stream peak time predicted by this model is 1.04 days,and the mean absolute error of high-speed stream peak speed is 81.5 km/s.This work provides a new idea for empirical models of solar wind.2.For detailed and accurate prediction of high-speed stream events,we study the statistical relationship between characteristics of coronal holes and high-speed streams.Based on solar extreme ultraviolet images and solar wind observation data from May2010 to December 2016,160 CH-HSS events are selected.By analyzing characteristics of CH-HSS events and relationship between coronal holes and high speed streams,we find that?1?transmission time of high-speed stream increases with the increasement of coronal hole peak area;?2?ascending/descending/duration time of high-speed stream events is directly proportional to the ascending/descending/duration time of coronal hole events;?3?peak speed of high-speed stream has a linear relationship with peak area of coronal hole,and correlation coefficient is 0.93.Based on the above results,we propose a prediction method for high-speed stream events,and take the CH-HSS event on December 9,2016 as an example for prediction test.The result shows that the arrival time,peak time,end time and peak speed predicted by this method are more ideal than those predicted by the empirical model of Reiss et al.?2016?.This work provides a new idea and reference for improving the prediction of high speed stream.3.In order to predict geomagnetic storms much earlier,we study the statistical relationship between coronal holes and geomagnetic storms.We select 152 coronal hole-geomagnetic storm events from May 2010 to December 2016,and statistically analyze two characteristic parameters(coronal hole area and P_ch index)of coronal hole and three geomagnetic index?ap,Dst,AE?.Conclusions are as follows:?1?the distribution of geomagnetic storm intensity and transmission time;?2?fitting results of coronal hole area and P_ch index with geomagnetic storm intensity and transmission time;fitting results of P_ch are superior to the fitting results of area.In addition,coronal hole-geomagnetic storm events are divided into two parts:training set and test set.The intensity and transmission time of geomagnetic storm in the test set are predicted by the statistical relationship between the coronal hole-geomagnetic storm of the training set.The results show that:?1?geomagnetic storm intensity predicted by P_ch index are better than predicted by coronal hole area,and the forecast results of AE index are better than other geomagnetic indexes;?2?geomagnetic storm transmission time predicted by coronal hole area and P_ch index both have advantages and disadvantages,and prediction results of ap index transmission time are better than other index transmission time.This work provides a basis for predicting geomagnetic storms 1-3 days in advance based on coronal hole imaging observations.