Search for: All records

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short‐term forecasts of the SYM‐H index based on 1‐ and 5‐min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM‐H index value at time pointt + whours for a given time pointtwherewis 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1‐ and 5‐min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. When predicting the SYM‐H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

 

Magnetic field plays an important role in various solar eruption phenomena. The formation and evolution of the characteristic magnetic field topology in solar eruptions are critical problems that will ultimately help us understand the origin of these eruptions in the solar source regions. With the development of advanced techniques and instruments, observations with higher resolutions in different wavelengths and fields of view have provided more quantitative information for finer structures. It is therefore essential to improve the method with which we study the magnetic field topology in the solar source regions by taking advantage of high-resolution observations. In this study, we employ a nonlinear force-free field extrapolation method based on a nonuniform grid setting for an M-class flare eruption event (SOL2015-06-22T17:39) with embedded vector magnetograms from the Solar Dynamics Observatory (SDO) and the Goode Solar Telescope (GST). The extrapolation results for which the nonuniform embedded magnetogram for the bottom boundary was employed are obtained by maintaining the native resolutions of the corresponding GST and SDO magnetograms. We compare the field line connectivity with the simultaneous GST/Hαand SDO/Atmospheric Imaging Assembly observations for these fine-scale structures, which are associated with precursor brightenings. Then we perform a topological analysis of the field line connectivity corresponding to fine-scale magnetic field structures based on the extrapolation results. The analysis results indicate that when we combine the high-resolution GST magnetogram with a larger magnetogram from the SDO, the derived magnetic field topology is consistent with a scenario of magnetic reconnection among sheared field lines across the main polarity inversion line during solar flare precursors.

 

Here, we present the study of a compact emission source during an X1.3 flare on 2022 March 30. Within a ∼41 s period (17:34:48 UT to 17:35:29 UT), Interface Region Imaging Spectrograph observations show spectral lines of Mgii, Cii, and Siivwith extremely broadened, asymmetric red wings. This source of interest (SOI) is compact, ∼1.″6, and is located in the wake of a passing ribbon. Two methods were applied to measure the Doppler velocities associated with these red wings: spectral moments and multi-Gaussian fits. The spectral-moments method considers the averaged shift of the lines, which are 85, 125, and 115 km s⁻¹for the Mgii, Cii, and Siivlines respectively. The red-most Gaussian fit suggests a Doppler velocity up to ∼160 km s⁻¹in all of the three lines. Downward mass motions with such high speeds are very atypical, with most chromospheric downflows in flares on the order 10–100 km s⁻¹. Furthermore, extreme-UV (EUV) emission is strong within flaring loops connecting two flare ribbons located mainly to the east of the central flare region. The EUV loops that connect the SOI and its counterpart source in the opposite field are much less brightened, indicating that the density and/or temperature is comparatively low. These observations suggest a very fast downflowing plasma in the transition region and upper chromosphere, which decelerates rapidly since there is no equivalently strong shift of the O I chromospheric lines. This unusual observation presents a challenge that models of the solar atmosphere’s response to flares must be able to explain.

 

Light bridges (LBs) are narrow structures dividing sunspot umbra, and their role in active region evolution is yet to be explored. We investigated the magnetic structure of the two LBs: a narrow LB (with width ∼810 km) and a considerably wider LB (2475 km) in the active region NOAA 12371. We employed: (1) the high-spatial-resolution spectropolarimetric data obtained by the Near InfraRed Imaging Spectropolarimeter (NIRIS) of the 1.6 m Goode Solar Telescope (GST) for studying the magnetic structure at the photosphere, and (2) the nonlinear force-free field (NLFFF) models, extrapolated from both the photospheric magnetogram from GST/NIRIS and from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, for studying the three-dimensional (3D) magnetic structure on a larger scale. Our observations reveal the presence of a field-free (or, more precisely, weak-field) region and the different velocity structures inside the two LBs. Analysis of the 3D NLFFF model shows a low-lying magnetic canopy as well as the enhanced current system above the LBs. The substantial difference between the LBs and the umbrae is found in the overall magnetic topology in that the field lines emanating from the two LBs are more twisted than that from the neighboring umbrae.

 

Magnetic reconnection is regarded as the mechanism for the rapid release of magnetic energy stored in active regions during solar flares, and quantitative measurements of the magnetic reconnection rate are essential for understanding solar flares. In the context of the standard two-ribbon flare model, we derive the coronal magnetic reconnection rate of the M6.5 flare on 2015 June 22 in two terms, reconnection flux change rate and reconnection electric field, both of which can be obtained from observations of the flare morphology. Data used include a sequence of chromospheric Hαimages with unprecedented resolution during the flare from the Visual Imaging Spectrometer of the Goode Solar Telescope (GST) at the Big Bear Solar Observatory and a preflare line-of-sight photospheric magnetogram from the GST Near-InfraRed Imaging Spectropolarimeter along with hard X-ray data from the Ramaty High Energy Solar Spectroscopic Imager. The temporal correlation between the magnetic reconnection rate and nonthermal emission is found, and the variation of the reconnection electric field is mainly determined by the ribbon speed, not by the local magnetic field encountered by the ribbon front. Spatially, the hard X-ray source overlaps with the location of the strongest electric field obtained at the same time. The ribbon motion shows abundant fine structures, including a local acceleration at the location of a light bridge with a weaker magnetic field.

 

Abstract We performed two data-based magnetohydrodynamic (MHD) simulations for solar active region 12371, which produced an M6.5 flare. The first simulation is a full data-driven simulation where the initial condition is given by a nonlinear force-free field (NLFFF). This NLFFF was extrapolated from photospheric magnetograms approximately 1 hr prior to the flare, and then a time-varying photospheric magnetic field is imposed at the bottom surface. The second simulation is also a data-driven simulation, but it stops driving at the bottom before the time of flare onset and then switches to the data-constrained simulation, where the horizontal component of the magnetic field varies according to an induction equation, while the normal component is fixed with time. Both simulations lead to an eruption, with both simulations producing highly twisted field lines before the eruption, which were not found in the NLFFF alone. After the eruption, the first simulation based on the time-varying photospheric magnetic field continues to produce sheared field lines after the flare without reproducing phenomena such as postflare loops. The second simulation reproduces the phenomena associated with flares well. However, in this case, the evolution of the bottom magnetic field is inconsistent with the evolution of the observed magnetic field. In this Letter, we report potential advantages and disadvantages in data-constrained and data-driven MHD simulations that need to be taken into consideration in future studies. 

In this paper, we study the evolution of the X5.4 flare (SOL2012-03-07T00:02) in NOAA Active Region 11429, focusing on its initiation mechanisms and back-reaction effects. To help our study, three-dimensional (3D) coronal magnetic field models are extrapolated from the photospheric magnetograms of the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory under the assumptions of nonlinear force-free field (NLFFF) and non-force-free field (non-FFF). We investigate the 3D magnetic structure and MHD kink instability, torus instability, and double-arc instability (DAI), and find that this flare is most likely triggered by the tether-cutting reconnection and the subsequent DAI. For the back-reactions of the flare, both NLFFF and non-FFF models clearly show an increase in horizontal magnetic field (B_h) and a decrease in inclination angle (ϕ) of the magnetic field near the polarity inversion line, from the photosphere up to a certain height (5 Mm and 8 Mm for non-FFF and NLFFF, respectively). In addition, the non-FFF model shows an enhancement of the downward Lorentz force acting on the photosphere, and the location of the enhancement spatially coincides with the location of the flare onset. The observed back-reaction is likely a consequence of magnetic reconnection.

 

The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h. 

Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections. They are the most important sources of space weather effects, which can severely impact the near-Earth environment. Thus it is essential to forecast flares (especially the M- and X-class ones) to mitigate their destructive and hazardous consequences. Here, we introduce several statistical and machine-learning approaches to the prediction of an active region’s (AR) flare index (FI) that quantifies the flare productivity of an AR by taking into account the number of different class flares within a certain time interval. Specifically, our sample includes 563 ARs that appeared on the solar disk from 2010 May to 2017 December. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, characterize coronal magnetic energy stored in ARs by proxy and are used as the predictors. We investigate the relationship between these SHARP parameters and the FI of ARs with a machine-learning algorithm (spline regression) and the resampling method (Synthetic Minority Oversampling Technique for Regression with Gaussian Noise). Based on the established relationship, we are able to predict the value of FIs for a given AR within the next 1 day period. Compared with other four popular machine-learning algorithms, our methods improve the accuracy of FI prediction, especially for a large FI. In addition, we sort the importance of SHARP parameters by the Borda count method calculated from the ranks that are rendered by nine different machine-learning methods.