共查询到20条相似文献，搜索用时 718 毫秒
1.
Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection 总被引：2，自引：0，他引：2
Omar W. Ahmed Rami Qahwaji Tufan Colak Paul A. Higgins Peter T. Gallagher D. Shaun Bloomfield 《Solar physics》2013,283(1):157-175
Novel machine-learning and feature-selection algorithms have been developed to study: i) the flare-prediction-capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); ii) SMART’s MF properties that are most significantly related to flare occurrence. Spatiotemporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine-learning and feature-selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare-prediction-capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast-verification measures and compared with the prediction measures of one of the standard technologies for flare-prediction that is also based on machine-learning: Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine-learning has the potential to achieve more accurate flare-prediction than ASAP. Feature-selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of six MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties. 相似文献
2.
Solar energetic particles (SEPs) are released into the heliosphere by solar flares and coronal mass ejections (CMEs). They are mostly protons, with smaller amounts of heavy ions from helium to iron, and lesser amounts of species heavier than iron. The spectra of heavy ions have been previously studied mostly by using the fluence of the particles in an event-integrated spectrum in a small number of spectral snapshots. In this article, we analyze the temporal evolution of the heavy-ion spectra using two large SEP events (27 January 2012 and 7 January 2014) from the Solar TErrestrial Relations Observatory (STEREO) era using Advanced Composition Explorer (ACE) Solar Isotope Spectrometer (SIS) and Ultra Low Energy Isotope Spectrometer (ULEIS), Energetic Particles: Acceleration, Composition and Transport (EPACT) onboard Wind, and the STEREO-A (Ahead) and -B (Behind) Low-Energy Telescope (LET) and Suprathermal Ion Telescope (SIT) instruments, taking a large number of snapshots covering the temporal evolution of the event. We find large differences in the spectra of the ions after the main flux enhancement in terms of the grouping of similar species, but also in terms of the location of the instruments. Although it is somewhat less noticeable than in the case of the temporal evolution of protons (Doran and Dalla, Solar Phys. 291, 2071, 2016), we observe a wave-like pattern travelling through the heavy ion spectra from the highest energies to the lowest, creating an “arch” structure that later straightens into a power law after 18 to 24 hours.
相似文献3.
Sequential chromospheric brightenings (SCBs) are often observed in the immediate vicinity of erupting flares and are associated with coronal mass ejections. Since their initial discovery in 2005, there have been several subsequent investigations of SCBs. These studies have used differing detection and analysis techniques, making it difficult to compare results between studies. This work employs the automated detection algorithm of Kirk et al. (Solar Phys. 283, 97, 2013) to extract the physical characteristics of SCBs in 11 flares of varying size and intensity. We demonstrate that the magnetic substructure within the SCB appears to have a significantly smaller area than the corresponding \(\mbox{H}\upalpha\) emission. We conclude that SCBs originate in the lower corona around \(0.1~R_{\odot}\) above the photosphere, propagate away from the flare center at speeds of \(35\,\mbox{--}\,85~\mbox{km}\,\mbox{s}^{-1}\), and have peak photosphere magnetic intensities of \(148\pm2.9~\mbox{G}\). In light of these measurements, we infer SCBs to be distinctive chromospheric signatures of erupting coronal mass ejections. 相似文献
4.
5.
The aim of this paper is to investigate the association of geomagnetic storms with the component of the interplanetary magnetic field (IMF) perpendicular to the ecliptic (\(Bz\)), the solar wind speed (\(V\)), the product of solar wind speed and \(Bz\) (VBz), the Kp index, and the sunspot number (SSN) for two consecutive even solar cycles, Solar Cycles 22 (1986?–?1995) and 24 (2009?–?2017). A comparative study has been done using the superposed epoch method (Chree analysis). The results of the present analysis show that \(Bz\) is a geoeffective parameter. The correlation coefficient between Dst and \(Bz\) is found to be 0.8 for both Solar Cycles 22 and 24, which indicates that these two parameters are highly correlated. Statistical relationships between Dst and Kp are established and it is shown that for the two consecutive even solar cycles, Solar Cycles 22 and 24, the patterns are strikingly similar. The correlation coefficient between Dst and Kp is found to be the same for the two solar cycles (?0.8), which clearly indicates that these parameters are well anti-correlated. For the same studied period we found that the SSN does not show any relationship with Dst and Kp, while there exists an inverse relation between Dst and the solar wind speed, with some time lag. We have also found that VBz is a more relevant parameter for the production of geomagnetic storms, as compared to \(V\) and \(Bz\) separately. In addition, we have found that in Solar Cycles 22 and 24 this combined parameter is more relevant during the descending phase as compared to the ascending phase. 相似文献
6.
A. Kilcik V. Yurchyshyn B. Donmez V. N. Obridko A. Ozguc J. P. Rozelot 《Solar physics》2018,293(4):63
We analyzed temporal and periodic variations of sunspot counts (SSCs) in flaring (C-, M-, or X-class flares), and non-flaring active regions (ARs) for nearly three solar cycles (1986 through 2016). Our main findings are as follows: i) temporal variations of monthly means of the daily total SSCs in flaring and non-flaring ARs behave differently during a solar cycle and the behavior varies from one cycle to another; during Solar Cycle 23 temporal SSC profiles of non-flaring ARs are wider than those of flaring ARs, while they are almost the same during Solar Cycle 22 and the current Cycle 24. The SSC profiles show a multi-peak structure and the second peak of flaring ARs dominates the current Cycle 24, while the difference between peaks is less pronounced during Solar Cycles 22 and 23. The first and second SSC peaks of non-flaring ARs have comparable magnitude in the current solar cycle, while the first peak is nearly absent in the case of the flaring ARs of the same cycle. ii) Periodic variations observed in the SSCs profiles of flaring and non-flaring ARs derived from the multi-taper method (MTM) spectrum and wavelet scalograms are quite different as well, and they vary from one solar cycle to another. The largest detected period in flaring ARs is \(113\pm 1.6~\mbox{days}\) while we detected much longer periodicities (\(327\pm 13\), \(312 \pm 11\), and \(256\pm 8~\mbox{days}\)) in the non-flaring AR profiles. No meaningful periodicities were detected in the MTM spectrum of flaring ARs exceeding \(55\pm 0.7~\mbox{days}\) during Solar Cycles 22 and 24, while a \(113\pm 1.3~\mbox{days}\) period was detected in flaring ARs of Solar Cycle 23. For the non-flaring ARs the largest detected period was only \(31\pm 0.2~\mbox{days}\) for Cycle 22 and \(72\pm 1.3~\mbox{days}\) for the current Cycle 24, while the largest measured period was \(327\pm 13~\mbox{days}\) during Solar Cycle 23. 相似文献
7.
C. Verbeeck P. A. Higgins T. Colak F. T. Watson V. Delouille B. Mampaey R. Qahwaji 《Solar physics》2013,283(1):67-95
Since the Solar Dynamics Observatory (SDO) began recording ≈?1 TB of data per day, there has been an increased need to automatically extract features and events for further analysis. Here we compare the overall detection performance, correlations between extracted properties, and usability for feature tracking of four solar feature-detection algorithms: the Solar Monitor Active Region Tracker (SMART) detects active regions in line-of-sight magnetograms; the Automated Solar Activity Prediction code (ASAP) detects sunspots and pores in white-light continuum images; the Sunspot Tracking And Recognition Algorithm (STARA) detects sunspots in white-light continuum images; the Spatial Possibilistic Clustering Algorithm (SPoCA) automatically segments solar EUV images into active regions (AR), coronal holes (CH), and quiet Sun (QS). One month of data from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) and SOHO/Extreme Ultraviolet Imaging Telescope (EIT) instruments during 12 May?–?23 June 2003 is analysed. The overall detection performance of each algorithm is benchmarked against National Oceanic and Atmospheric Administration (NOAA) and Solar Influences Data Analysis Center (SIDC) catalogues using various feature properties such as total sunspot area, which shows good agreement, and the number of features detected, which shows poor agreement. Principal Component Analysis indicates a clear distinction between photospheric properties, which are highly correlated to the first component and account for 52.86% of variability in the data set, and coronal properties, which are moderately correlated to both the first and second principal components. Finally, case studies of NOAA 10377 and 10365 are conducted to determine algorithm stability for tracking the evolution of individual features. We find that magnetic flux and total sunspot area are the best indicators of active-region emergence. Additionally, for NOAA 10365, it is shown that the onset of flaring occurs during both periods of magnetic-flux emergence and complexity development. 相似文献
8.
Seismic maps of the Sun’s far hemisphere, computed from Doppler data from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) are now being used routinely to detect strong magnetic regions on the far side of the Sun ( http://jsoc.stanford.edu/data/farside/ ). To test the reliability of this technique, the helioseismically inferred active region detections are compared with far-side observations of solar activity from the Solar TErrestrial RElations Observatory (STEREO), using brightness in extreme-ultraviolet light (EUV) as a proxy for magnetic fields. Two approaches are used to analyze nine months of STEREO and HMI data. In the first approach, we determine whether new large east-limb active regions are detected seismically on the far side before they appear Earth side and study how the detectability of these regions relates to their EUV intensity. We find that while there is a range of EUV intensities for which far-side regions may or may not be detected seismically, there appears to be an intensity level above which they are almost always detected and an intensity level below which they are never detected. In the second approach, we analyze concurrent extreme-ultraviolet and helioseismic far-side observations. We find that 100% (22) of the far-side seismic regions correspond to an extreme-ultraviolet plage; 95% of these either became a NOAA-designated magnetic region when reaching the east limb or were one before crossing to the far side. A low but significant correlation is found between the seismic signature strength and the EUV intensity of a far-side region. 相似文献
9.
Kostas Florios Ioannis Kontogiannis Sung-Hong Park Jordan A. Guerra Federico Benvenuto D. Shaun Bloomfield Manolis K. Georgoulis 《Solar physics》2018,293(2):28
We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012?–?2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude \({>}\,\mbox{M1}\) and \({>}\,\mbox{C1}\) within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy \(\mathrm{ACC}=0.93(0.00)\), true skill statistic \(\mathrm{TSS}=0.74(0.02)\), and Heidke skill score \(\mathrm{HSS}=0.49(0.01)\) for \({>}\,\mbox{M1}\) flare prediction with probability threshold 15% and \(\mathrm{ACC}=0.84(0.00)\), \(\mathrm{TSS}=0.60(0.01)\), and \(\mathrm{HSS}=0.59(0.01)\) for \({>}\,\mbox{C1}\) flare prediction with probability threshold 35%. 相似文献
10.
In this work a total of 266 interplanetary coronal mass ejections observed by the Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph (SOHO/LASCO) and then studied by in situ observations from Advanced Composition Explorer (ACE) spacecraft, are presented in a new catalog for the time interval 1996?–?2009 covering Solar Cycle 23. Specifically, we determine the characteristics of the CME which is responsible for the upcoming ICME and the associated solar flare, the initial/background solar wind plasma and magnetic field conditions before the arrival of the CME, the conditions in the sheath of the ICME, the main part of the ICME, the geomagnetic conditions of the ICME’s impact at Earth and finally we remark on the visual examination for each event. Interesting results revealed from this study include the high correlation coefficient values of the magnetic field \(B_{z}\) component against the Ap index (\(r = 0.84\)), as well as against the Dst index (\(r = 0.80\)) and of the effective acceleration against the CME linear speed (\(r = 0.98\)). We also identify a north–south asymmetry for X-class solar flares and an east–west asymmetry for CMEs associated with strong solar flares (magnitude ≥ M1.0) which finally triggered intense geomagnetic storms (with \(\mathrm{Ap} \geq179\)). The majority of the geomagnetic storms are determined to be due to the ICME main part and not to the extreme conditions which dominate inside the sheath. For the intense geomagnetic storms the maximum value of the Ap index is observed almost 4 hours before the minimum Dst index. The amount of information makes this new catalog the most comprehensive ICME catalog for Solar Cycle 23. 相似文献
11.
Miikka Paassilta Athanasios Papaioannou Nina Dresing Rami Vainio Eino Valtonen Bernd Heber 《Solar physics》2018,293(4):70
Based on energetic particle observations made at \({\approx}\,1\) AU, we present a catalogue of 46 wide-longitude (\({>}\,45^{\circ}\)) solar energetic particle (SEP) events detected at multiple locations during 2009?–?2016. The particle kinetic energies of interest were chosen as \({>}\,55\) MeV for protons and 0.18?–?0.31 MeV for electrons. We make use of proton data from the Solar and Heliospheric Observatory/Energetic and Relativistic Nuclei and Electron Experiment (SOHO/ERNE) and the Solar Terrestrial Relations Observatory/High Energy Telescopes (STEREO/HET), together with electron data from the Advanced Composition Explorer/Electron, Proton, and Alpha Monitor (ACE/EPAM) and the STEREO/Solar Electron and Proton Telescopes (SEPT). We consider soft X-ray data from the Geostationary Operational Environmental Satellites (GOES) and coronal mass ejection (CME) observations made with the SOHO/Large Angle and Spectrometric Coronagraph (LASCO) and STEREO/Coronagraphs 1 and 2 (COR1, COR2) to establish the probable associations between SEP events and the related solar phenomena. Event onset times and peak intensities are determined; velocity dispersion analysis (VDA) and time-shifting analysis (TSA) are performed for protons; TSA is performed for electrons. In our event sample, there is a tendency for the highest peak intensities to occur when the observer is magnetically connected to solar regions west of the flare. Our estimates for the mean event width, derived as the standard deviation of a Gaussian curve modelling the SEP intensities (protons \({\approx}\,44^{\circ}\), electrons \({\approx}\,50^{\circ}\)), largely agree with previous results for lower-energy SEPs. SEP release times with respect to event flares, as well as the event rise times, show no simple dependence on the observer’s connection angle, suggesting that the source region extent and dominant particle acceleration and transport mechanisms are important in defining these characteristics of an event. There is no marked difference between the speed distributions of the CMEs related to wide events and the CMEs related to all near-Earth SEP events of similar energy range from the same time period. 相似文献
12.
A “Solar Dynamo” (SODA) Index prediction of the amplitude of Solar Cycle 25 is described. The SODA Index combines values of the solar polar magnetic field and the solar spectral irradiance at 10.7 cm to create a precursor of future solar activity. The result is an envelope of solar activity that minimizes the 11-year period of the sunspot cycle. We show that the variation in time of the SODA Index is similar to several wavelet transforms of the solar spectral irradiance at 10.7 cm. Polar field predictions for Solar Cycles 21?–?24 are used to show the success of the polar field precursor in previous sunspot cycles. Using the present value of the SODA index, we estimate that the next cycle’s smoothed peak activity will be about \(140 \pm30\) solar flux units for the 10.7 cm radio flux and a Version 2 sunspot number of \(135 \pm25\). This suggests that Solar Cycle 25 will be comparable to Solar Cycle 24. The estimated peak is expected to occur near \(2025.2 \pm1.5\) year. Because the current approach uses data prior to solar minimum, these estimates may improve as the upcoming solar minimum draws closer. 相似文献
13.
We studied the occurrence and characteristics of geomagnetic storms associated with disk-centre full-halo coronal mass ejections (DC-FH-CMEs). Such coronal mass ejections (CMEs) can be considered as the most plausible cause of geomagnetic storms. We selected front-side full-halo coronal mass ejections detected by the Large Angle and Spectrometric Coronagraph onboard the Solar and Heliospheric Observatory (SOHO/LASCO) from the beginning of 1996 till the end of 2015 with source locations between solar longitudes E10 and W10 and latitudes N20 and S20. The number of selected CMEs was 66 of which 33 (50%) were deduced to be the cause of 30 geomagnetic storms with \(\mathrm{Dst} \leq- 50~\mbox{nT}\). Of the 30 geomagnetic storms, 26 were associated with single disk-centre full-halo CMEs, while four storms were associated, in addition to at least one disk-centre full-halo CME, also with other halo or wide CMEs from the same active region. Thirteen of the 66 CMEs (20%) were associated with 13 storms with \(-100~\mbox{nT} < \mbox{Dst} \leq- 50~\mbox{nT}\), and 20 (30%) were associated with 17 storms with \(\mbox{Dst}\leq- 100~\mbox{nT}\). We investigated the distributions and average values of parameters describing the DC-FH-CMEs and their interplanetary counterparts encountering Earth. These parameters included the CME sky-plane speed and direction parameter, associated solar soft X-ray flux, interplanetary magnetic field strength, \(B_{t}\), southward component of the interplanetary magnetic field, \(B_{s}\), solar wind speed, \(V_{sw}\), and the \(y\)-component of the solar wind electric field, \(E_{y}\). We found only a weak correlation between the Dst of the geomagnetic storms associated with DC-FH-CMEs and the CME sky-plane speed and the CME direction parameter, while the correlation was strong between the Dst and all the solar wind parameters (\(B_{t}\), \(B_{s}\), \(V_{sw}\), \(E_{y}\)) measured at 1 AU. We investigated the dependences of the properties of DC-FH-CMEs and the associated geomagnetic storms on different phases of solar cycles and the differences between Solar Cycles 23 and 24. In the rise phase of Solar Cycle 23 (SC23), five out of eight DC-FH-CMEs were geoeffective (\(\mbox{Dst} \leq- 50~\mbox{nT}\)). In the corresponding phase of SC24, only four DC-FH-CMEs were observed, three of which were nongeoeffective (\(\mbox{Dst} > - 50~\mbox{nT}\)). The largest number of DC-FH-CMEs occurred at the maximum phases of the cycles (21 and 17, respectively). Most of the storms with \(\mbox{Dst}\leq- 100~\mbox{nT}\) occurred at or close to the maximum phases of the cycles. When comparing the storms during epochs of corresponding lengths in Solar Cycles 23 and 24, we found that during the first 85 months of Cycle 23 the geoeffectiveness rate of the disk-centre full-halo CMEs was 58% with an average minimum value of the Dst index of \(- 146~\mbox{nT}\). During the corresponding epoch of Cycle 24, only 35% of the disk-centre full-halo CMEs were geoeffective with an average value of Dst of \(- 97~\mbox{nT}\). 相似文献
14.
15.
We present here an interesting two-step filament eruption during 14?–?15 March 2015. The filament was located in NOAA AR 12297 and associated with a halo Coronal Mass Ejection (CME). We use observations from the Atmospheric Imaging Assembly (AIA) and Heliospheric Magnetic Imager (HMI) instruments onboard the Solar Dynamics Observatory (SDO), and from the Solar and Heliospheric Observatory (SOHO) Large Angle and Spectrometric Coronagraph (LASCO). We also use \(\mbox{H}\upalpha\) data from the Global Oscillation Network Group (GONG) telescope and the Kanzelhoehe Solar Observatory. The filament shows a first step eruption on 14 March 2015 and it stops its rise at a projected altitude \({\approx}\,125~\mbox{Mm}\) on the solar disk. It remains at this height for \({\approx}\,12~\mbox{hrs}\). Finally it erupts on 15 March 2015 and produces a halo CME. We also find jet activity in the active region during both days, which could help the filament de-stabilization and eruption. The decay index is calculated to understand this two-step eruption. The eruption could be due to the presence of successive instability–stability–instability zones as the filament is rising. 相似文献
16.
In this article, an automated solar flare detection method applied to both full-disk and local high-resolution H\(\upalpha\) images is proposed. An adaptive gray threshold and an area threshold are used to segment the flare region. Features of each detected flare event are extracted, e.g. the start, peak, and end time, the importance class, and the brightness class. Experimental results have verified that the proposed method can obtain more stable and accurate segmentation results than previous works on full-disk images from Big Bear Solar Observatory (BBSO) and Kanzelhöhe Observatory for Solar and Environmental Research (KSO), and satisfying segmentation results on high-resolution images from the Goode Solar Telescope (GST). Moreover, the extracted flare features correlate well with the data given by KSO. The method may be able to implement a more complicated statistical analysis of H\(\upalpha\) solar flares. 相似文献
17.
We report on the kinematics of two interacting CMEs observed on 13 and 14 June 2012. The two CMEs originated from the same active region NOAA 11504. After their launches which were separated by several hours, they were observed to interact at a distance of \(100~R_{\odot}\) from the Sun. The interaction led to a moderate geomagnetic storm at the Earth with minimum \(\mathrm{D}_{\mathrm{st}}\) index of approximately ?86 nT. The kinematics of the two CMEs is estimated using data from the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) instrument onboard the Solar Terrestrial Relations Observatory (STEREO). Assuming a head-on collision scenario, we find that the collision is inelastic in nature. Further, the signatures of their interaction are examined using the in situ observations obtained by Wind and the Advance Composition Explorer (ACE) spacecraft. It is also found that this interaction event led to the strongest sudden storm commencement (SSC) (\({\approx\,}150\) nT) of the present Solar Cycle 24. The SSC was of long duration, approximately 20 hours. The role of interacting CMEs in enhancing the geoeffectiveness is examined. 相似文献
18.
R. A. Hock P. C. Chamberlin T. N. Woods D. Crotser F. G. Eparvier D. L. Woodraska E. C. Woods 《Solar physics》2012,275(1-2):145-178
The NASA Solar Dynamics Observatory (SDO), scheduled for launch in early 2010, incorporates a suite of instruments including the Extreme Ultraviolet Variability Experiment (EVE). EVE has multiple instruments including the Multiple Extreme ultraviolet Grating Spectrographs (MEGS) A, B, and P instruments, the Solar Aspect Monitor (SAM), and the Extreme ultraviolet SpectroPhotometer (ESP). The radiometric calibration of EVE, necessary to convert the instrument counts to physical units, was performed at the National Institute of Standards and Technology (NIST) Synchrotron Ultraviolet Radiation Facility (SURF III) located in Gaithersburg, Maryland. This paper presents the results and derived accuracy of this radiometric calibration for the MEGS A, B, P, and SAM instruments, while the calibration of the ESP instrument is addressed by Didkovsky et?al. (Solar Phys., 2010, doi: 10.1007/s11207-009-9485-8 ). In addition, solar measurements that were taken on 14 April 2008, during the NASA 36.240 sounding-rocket flight, are shown for the prototype EVE instruments. 相似文献
19.
20.
Recently we (Kahler and Ling, Solar Phys.292, 59, 2017: KL) have shown that time–intensity profiles [\(I(t)\)] of 14 large solar energetic particle (SEP) events can be fitted with a simple two-parameter fit, the modified Weibull function, which is characterized by shape and scaling parameters [\(\alpha\) and \(\beta\)]. We now look for a simple correlation between an event peak energy intensity [\(I_{\mathrm{p}}\)] and the time integral of \(I(t)\) over the event duration: the fluence [\(F\)]. We first ask how the ratio of \(F/I_{\mathrm{p}}\) varies for the fits of the 14 KL events and then examine that ratio for three separate published statistical studies of SEP events in which both \(F\) and \(I_{\mathrm{p}}\) were measured for comparisons of those parameters with various solar-flare and coronal mass ejection (CME) parameters. The three studies included SEP energies from a 4?–?13 MeV band to \(E > 100~\mbox{MeV}\). Within each group of SEP events, we find a very robust correlation (\(\mathrm{CC} > 0.90\)) in log–log plots of \(F\)versus\(I_{\mathrm{p}}\) over four decades of \(I_{\mathrm{p}}\). The ratio increases from western to eastern longitudes. From the value of \(I_{\mathrm{p}}\) for a given event, \(F\) can be estimated to within a standard deviation of a factor of \({\leq}\,2\). Log–log plots of two studies are consistent with slopes of unity, but the third study shows plot slopes of \({<}\,1\) and decreasing with increasing energy for their four energy ranges from \(E > 10~\mbox{MeV}\) to \({>}\,100~\mbox{MeV}\). This difference is not explained. 相似文献