Abstract
Since fast head-on coronal mass ejections and their associated shocks represent potential hazards to the space environment of the Earth and even other planets, forecasting the arrival time ...of the corresponding interplanetary shock is a priority in space weather research and prediction. Based on the radio spectrum observations of the 16-element array of the Daocheng Solar Radio Telescope (DSRT), the flagship instrument of the Meridian Project of China, during its construction, this study determines the initial shock speed of a type II solar radio burst on 2022 April 17 from its drifting speed in the spectrum. Assuming that the shock travels at a steady speed during the piston-driven phase (determined from the X-ray flux of the associated flare) and then propagates through interplanetary space as a blast wave, we estimate the propagation and arrival time of the corresponding shock at the orbit of the Solar Terrestrial Relations Observatory-A (STEREO-A). The prediction shows that the shock will reach STEREO-A at 14:31:57 UT on 2022 April 19. The STEREO-A satellite detected an interplanetary shock at 13:52:12 UT on the same day. The discrepancy between the predicted and observed arrival time of the shock is only 0.66 hr. The purpose of this paper is to establish a general method for predicting the shock’s propagation and arrival time from this example, which will be utilized to predict more events in the future based on the observations of ground-based solar radio spectrometers or telescopes like DSRT.
Abstract
The formation mechanism of light bridges (LBs) is strongly related to the dynamic evolution of solar active regions (ARs). To study the relationship between LB formation and AR evolution ...phases, we employ 109 LB samples from 69 ARs in 2014 using observational data from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. LBs are well matched with the weak field lanes (WFLs), except those aligned on the polarity inversion line of
δ
sunspots. For penumbral intrusion (type-A) and umbral-dot emergence (type-C) LBs, the WFLs represent the splitting of magnetic flux systems. The sunspots tend to decay and split into several parts after type-A and type-C LBs are formed. For sunspot/umbra-merging (type-B) LBs, the declining WFLs are caused by collisions of flux systems. The sunspots merged and remained stable after type-B LBs formed. We conclude that type-B LBs are formed by collisions of flux systems, while type-A and type-C LBs are generated by splits. The time differences (
δ
T
) between LBs appearing and ARs peaking have an average value of 1.06, −1.60, and 1.82 days for type-A, B, and C LBs, with the standard deviations of 3.27, 2.17, and 1.89, respectively. A positive value of
δ
T
means that the LB appears after the AR peaks, whereas a negative
δ
T
means it appears before the peak. Type-A LBs tend to form in the decaying phase or around the peak time. Type-B LBs are more likely to be formed in the developing phase. Type-C LBs mostly take shape in the decaying phase of ARs.
Based on the latest database, we analyze the characteristics of the changing trends of sunspot number (SSN) and temperatures of the global mean surface (GMST) as well as six latitude bands during the ...Holocene, aiming to explore the long-term responses of the Holocene temperatures to solar activity. We adopt two methods, i.e., the 300-year moving average and the singular spectrum analysis to obtain the long-term trends of signals. We find that the average changes in the amplitude of temperatures in the Northern Hemisphere (NH) (3.65 °C) was greater than that in the Southern Hemisphere (SH) (1.28 °C) during the entire Holocene. There was one peak of temperatures (at 6500 BP) and two peaks of solar activity (at 4500 BP and 2000 BP) during our study interval. Spatially, the temperatures in the NH were more sensitive to solar forcing than those in the SH, especially in the latitude bands of 0°–30°N and 60°N-90°N. Moreover, the latitude band 0°-30°N had the strongest correlation with solar activity (C.C. = 0.38 for the 300-year moving average and C.C. = 0.55 for the singular spectrum analysis, p < 0.01), while the latitude band 60°S-90°S had the weakest correlation with solar activity (not significant in the statistical sense). Regarding the time response, solar forcing nearly did not affect the terrestrial temperatures in the early Holocene (8700 BP-6500 BP). While positive correlations started to be strong since 4500 BP and became stronger during 2000 BP-100 BP. All in all, the terrestrial temperatures were consistent with solar activity in the long-term trend during the Holocene, especially in the NH. Although many mysteries in historical climate reconstructions remain un-resolved, evaluating the impact of solar force on terrestrial temperature is crucial to reconstruct and predict terrestrial climate. Much deeper specific research on solar-terrestrial mechanisms deserves to be put on the agenda.
As an important index of solar activity, the 10.7-cm solar radio flux (F10.7) can indicate changes in the solar EUV radiation, which plays an important role in the relationship between the Sun and ...the Earth. Therefore, it is valuable to study and forecast F10.7. In this study, the long short-term memory (LSTM) method in machine learning is used to predict the daily value of F10.7. The F10.7 series from 1947 to 2019 are used. Among them, the data during 1947–1995 are adopted as the training dataset, and the data during 1996–2019 (solar cycles 23 and 24) are adopted as the test dataset. The fourfold cross validation method is used to group the training set for multiple validations. We find that the root mean square error (RMSE) of the prediction results is only 6.20~6.35 sfu, and the correlation coefficient (R) is as high as 0.9883~0.9889. The overall prediction accuracy of the LSTM method is equivalent to those of the widely used autoregressive (AR) and backpropagation neural network (BP) models. Especially for 2-day and 3-day forecasts, the LSTM model is slightly better. All this demonstrates the potentiality of the LSTM method in the real-time forecasting of F10.7 in future.
At mid and low heliographic latitudes, filament activity shifts equatorward starting from the beginning of the solar cycle. At high latitudes, it migrates poleward. Solar filaments exhibit the “rush ...to the poles” close to solar maximum, when the solar polar magnetic field reverses polarity. In order to better understand the behavior of the “rush to the poles,” we used cross-correlation analysis and wavelet transform methods for investigating the periodic characteristics and the phase relationship between two groups of the solar filaments at high latitudes observed during the period from 1919 March to 1989 December. The length of the solar cycle derived from the continuous wavelet transform is a function of latitude, but still shows a significant 11-yr cycle. The most significant periods of the solar filaments, respectively at higher latitudes than 50° and 60°, are 10.77 and 10.62 yr, using the wavelet transform method. From the cross-correlation analysis, the solar filaments at higher latitudes than 50° have a lead of six months with respect to those at higher latitudes than 60°. Different solar cycles exhibited different phase relationships between the two groups of solar filaments. The analysis of the cross-wavelet transform also indicates that the solar filaments at higher latitudes than 50° lead those at higher latitudes than 60° in the entire time interval. The relationship between the phase difference of the two groups of solar filaments and the intensity of solar activity is also discussed. What is more, the poleward shifting speeds are estimated.
The formation mechanism of light bridges (LBs) is strongly related to the dynamic evolution of solar active regions (ARs). To study the relationship between LB formation and AR evolution phases, we ...employ 109 LB samples from 69 ARs in 2014 using observational data from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory (HMI/SDO). LBs are well matched with the weak field lanes (WFLs), except that aligned on the polarity inversion line of {\delta} sunspots. For penumbral intrusion (type-A) and umbral-dot emergence (type-C) LBs, the WFLs represent the splitting of magnetic flux systems. The sunspots tend to decay and split into several parts after type-A and type-C LBs formed. For sunspot/umbra merging (type-B) LBs, the WFLs declining are caused by collisions of flux systems. The sunspots merge and keep stable after type-B LBs formed. We conclude that type-B LBs are formed by collisions of flux systems, while type-A and type-C LBs are generated by splits. The time differences ({\delta}T) between LBs appearing and ARs peaking have average value of 1.06, -1.60, 1.82 for type-A, B, C LBs, with the standard deviation of 3.27, 2.17, 1.89, respectively. A positive value of {\delta}T means that the LB appear after AR peaking, whereas a minus {\delta}T before the peak. Type-A LBs trend to form in the decaying phase or around the peak time. Type-B LBs are more likely to be formed in the developing phase. Type-C LBs mostly take shape in the decaying phase of ARs.
The relationship between quality of life at three months after lung cancer surgery and different surgical approaches is remains unclear. This study aimed to compare the quality of life of patients ...three months after uniportal and multiportal thoracoscopic lobectomy.
Data from patients who underwent lung surgery at the Department of Thoracic Surgery, Sichuan Cancer Hospital between April 2021 and October 2021 were collected. The European Organization for Research and Treatment of Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) and Quality of Life Questionnaire-Lung Cancer 29 (EORTC QLQ-LC29) were used to collect quality of life data of the patients. Potential confounding factors in the baseline data were included in a multivariate regression model for adjustment, and the quality of life of the two groups three months postoperatively was compared with traditional clinical outcomes.
A total of 130 lung cancer patients were included, with 57 males (43.8%) and 73 females (56.2%), and an average age of (57.1±
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