Solar Physics最新文献

{"title":"Spatiotemporal Machine-Learning Detection of Coronal Mass Ejections in SOHO/LASCO C2 Images","authors":"Tomasz J. Łaguz,&nbsp;Grzegorz Michałek,&nbsp;Adam A. Zychowicz,&nbsp;Seiji Yashiro","doi":"10.1007/s11207-026-02704-3","DOIUrl":"10.1007/s11207-026-02704-3","url":null,"abstract":"<div><h3>Context.</h3><p>Currently coronal mass ejections (CME) detection and measurements heavily depend on time-consuming human observations. Existing automated catalogs are based on manually crafted mathematical algorithms and struggle to provide the performance of a human observer (Lamy et al. 2019). Recent advances in machine learning (ML) have opened up new avenues for the early detection and analysis of coronal mass ejections (CMEs), which pose significant risks to space weather and technological systems on Earth.</p><h3>Aim.</h3><p>This study presents a novel methodology utilizing deep learning algorithms to analyze images from SOHO/LASCO coronagraphs, with the objective of enhancing the identification of CMEs by classifying the coronagraph frames into two categories: CME and non-CME.</p><h3>Method.</h3><p>We created a dataset based on level 0.5 SOHO LASCO (Solar and Heliospheric Observatory Large Angle Spectrometer COronagraph) C2 observations correlated with CDAW (Coordinated Data Analysis Workshop) CME measurements. Then we developed two convolutional neural network (CNN) models for binary classification: the standard AlexNet and a modified AlexNet for action recognition (Spatiotemporal AlexNet). We tested various data augmentation techniques and evaluated various approaches to clip creation from static images.</p><h3>Result.</h3><p>Initial results are very promising and show improvements in detection accuracy with the spatiotemporal model and suggest areas for further research in CME detection using machine learning. This research provides methodological insights that could aid the development of more effective CME detection systems and, ultimately, improved space weather preparedness. Further work will focus on replacing the automatically generated CDAW-based dataset with a manually curated version, as improved labeling quality is likely to enhance detection accuracy.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"301 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11207-026-02704-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brightenings AnD Polarity Inversion Tracking (BADPIT) Method for Studying Solar Active Region Evolution Before Major Solar Flares","authors":"Augustin André-Hoffmann,&nbsp;Marianna B. Korsós,&nbsp;Alexander Nindos,&nbsp;Spiros Patsourakos,&nbsp;Manolis K. Georgoulis,&nbsp;Robertus Erdélyi","doi":"10.1007/s11207-026-02701-6","DOIUrl":"10.1007/s11207-026-02701-6","url":null,"abstract":"<div><p>This study investigates the relationship between extreme ultraviolet (EUV) transient brightenings (TBs) and the onset of GOES X-class solar flares in active regions (ARs). We introduce the Brightenings AnD Polarity Inversion Tracking (BADPIT) method that can detect TBs across multiple SDO/AIA channels. To identify TBs, we impose two independent thresholds: a 3-<span>(sigma )</span> intensity-based criterion and a power law divergence approach. We demonstrate the application of BADPIT through a flaring and a non-flaring AR for 24 hours as a pathfinder to a comprehensive statistical study for a complete performance verification: the studied ARs are the non-flaring AR 13186 and the flaring AR 11429, both sharing a similar Hale sunspot classification. Preliminary results are encouraging: significantly more TBs are detected in the flaring AR, with up to five times more 3-<span>(sigma )</span> thresholded TBs identified, while power law thresholded events were frequent only in the flaring AR and mostly absent in the non-flaring AR. In the two ARs that we studied both the power law threshold method and the 3-<span>(sigma )</span> method show a potential to act as diagnostic tools for distinguishing between imminent flaring and non-flaring conditions several hours before the onset of major solar flares. However, our work is a proof-of-concept study, given the limited number of ARs we studied; its reliability as a forecasting tool will be investigated in a follow-up study in which a large sample of ARs will be analysed.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"301 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11207-026-02701-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polar Faculae and the Polar Magnetic Field in Solar Cycles 24 and 25","authors":"Julia R. Clark,&nbsp;W. Dean Pesnell,&nbsp;Matthew C. Barzal","doi":"10.1007/s11207-026-02702-5","DOIUrl":"10.1007/s11207-026-02702-5","url":null,"abstract":"<div><p>Polar faculae are footprints of the polar magnetic field that are visible as bright spots along intergranular lanes. Unlike equatorial faculae and sunspots, which are found at low to moderate solar latitudes and peak in number at solar maximum, polar faculae are found at latitudes greater than <span>(70^{circ })</span> and peak in number around solar minimum. Polar faculae tend to have the same magnetic polarity as the general polar magnetic field, and their number has been shown to correlate with the strength of that magnetic field. This makes them good candidates for studying the evolution of polar conditions throughout the solar cycle from the ecliptic.</p><p>We present a new automated method for counting and studying polar faculae in Helioseismic and Magnetic Imager (HMI) Ic_720s data using a source detection function from the Python library Photutils. We applied this method to both polar regions, using data averaged over each hour throughout the day between 2010 and 2022, the period for which HMI data is available in HelioCloud.</p><p>Our results show a variation that is similar to that of the faculae count data from the Debrecen Heliophysical Observatory when they overlap, and extend that time series to December 2022. We also found that the magnetic field of the polar faculae averaged over the polar cap has the same polarity as the polar magnetic field from other measurements. However, we show that faculae with both polarities are present throughout the sunspot cycle. This indicates that some polar faculae may be generated by a local dynamo.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"301 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11207-026-02702-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions","authors":"Spiridon Kasapis,&nbsp;Eren Dogan,&nbsp;Irina N. Kitiashvili,&nbsp;Alexander G. Kosovichev,&nbsp;John T. Stefan,&nbsp;Jake D. Butler,&nbsp;Jonas Tirona,&nbsp;Sarang Patil,&nbsp;Mengjia Xu","doi":"10.1007/s11207-026-02692-4","DOIUrl":"10.1007/s11207-026-02692-4","url":null,"abstract":"<div><p>The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore, it is crucial to detect Active Regions (ARs) before they start forming on the solar surface. This will enable the development of early-warning capabilities for upcoming space weather disturbances. For this reason, we prepared the Solar Active Region Emergence Dataset (SolARED). The dataset is derived from full-disk maps of the Doppler velocity, magnetic field, and continuum intensity, obtained by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). SolARED includes time series of remapped, tracked, and binned data that characterize the evolution of acoustic power of solar oscillations, unsigned magnetic flux, and continuum intensity for 50 large ARs before, during, and after their emergence on the solar surface, as well as surrounding areas observed on the solar disc between 2010 and 2023. The resulting ML-ready SolARED dataset is designed to support enhancements of predictive capabilities, enabling the development of operational forecasts for the emergence of active regions. The SolARED dataset is available at https://sun.njit.edu/sarportal/, through an interactive visualization web application.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"301 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of the Transport of High-Energy Protons in the Solar Atmosphere and the Resulting Spectra and Light Curves of the Associated (gamma )-Ray Emission","authors":"Alexey Kuzmitskiy,&nbsp;Alexey Kochanov","doi":"10.1007/s11207-026-02697-z","DOIUrl":"10.1007/s11207-026-02697-z","url":null,"abstract":"<div><p>High-energy <span>(gamma )</span>-ray emission generated in the solar atmosphere during powerful flares provides a unique channel of information on the acceleration of protons and ions to energies ≳ 300 MeV. Reliably identifying the shape of their energy spectrum with that of the secondary <span>(gamma )</span>-ray emission requires a robust theoretical calculation of both the evolution of accelerated particles in the solar atmosphere and the resulting <span>(gamma )</span>-ray emission for different particle injection scenarios. In this work, a numerical solution to the non-stationary Ginzburg–Syrovatskii particle transport equation in the “leaky box” approximation is obtained. Scenarios of free proton precipitation into the photosphere and precipitation with their preliminary trapping in the coronal parts of low and high magnetic loops are considered. The calculations for the photospheric and deeper layers take into account energy losses due to ionization and inelastic collisions, nuclear reactions that regenerate protons, and the production of neutral pions — the primary source of high-energy <span>(gamma )</span>-ray emission. The results of the calculation comprise the time-dependent spectra of secondary particles and the corresponding light curves of the <span>(gamma )</span>-ray emission. The inclusion of particle trapping in a coronal magnetic loop leads to sustained high-energy <span>(gamma )</span>-ray emission on time scales ranging from a fraction of a minute to several tens of minutes. For large coronal loops, the trapping time could potentially reach the order of hours. The obtained spectra are compared both with some calculations from previous years and with recent calculations performed using the Monte Carlo method.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"301 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mauna Loa Solar Observatory K-Cor Coronagraph Observations of Coronal Mass Ejections Associated with Solar Energetic Particles","authors":"O. C. St. Cyr,&nbsp;I. G. Richardson,&nbsp;J. T. Burkepile,&nbsp;M. Galloy,&nbsp;T. Nieves-Chinchilla,&nbsp;B. J. Thompson","doi":"10.1007/s11207-026-02679-1","DOIUrl":"10.1007/s11207-026-02679-1","url":null,"abstract":"<div><p>We report on 27 coronal mass ejections (CMEs) observed in the inner corona (roughly ≤ 2 solar radii (R_s)) at the Mauna Loa Solar Observatory (MLSO) with the K-Coronagraph (K-Cor) from late 2013 until November 2022, when MLSO operations were suspended due to a volcanic eruption. All these CMEs were associated with solar energetic particle (SEP) events including ∼ 25 MeV protons observed by spacecraft at different locations in the inner heliosphere (≤ 1 astronomical unit (au). We analyzed the characteristics of these CMEs and compared them to a sample of CMEs observed by K-Cor during the same period with no known SEPs. We found that both samples of SEP and non-SEP CMEs expanded in width between the inner and middle corona (&gt; 2 R_s), where they were observed by spacebased coronagraphs. However, the average and the median widths for the drivers of SEP CMEs were a factor of two larger than for the non-SEP CME drivers in both the inner and middle corona. Similarly, we found that the average speed of the SEP CMEs in the inner corona was more than a factor of two faster than the average for non-SEP CMEs, and almost a factor of two faster in the middle corona. Most of the CMEs (&gt; 80%) were accelerating in the inner corona for both SEP and non-SEP samples, though the average acceleration in the inner corona for the SEP CMEs was almost a factor three larger than for the non-SEP sample. The mass estimates provided in the Coordinated Data Analysis Workshops (CDAW) catalogue for the two samples also shows that the average for SEP CMEs was almost a factor three larger than for the non-SEP sample. The event of 2021-11-01 is presented as an example of an SEP CME included in this study. One of the 27 SEPs registered S1 on the National Oceanic and Atmospheric Administration (NOAA) solar radiation storm scale (S-scale) in the near-Earth environment, and several others were detected by the NOAA Geostationary Operational Environmental Satellites (GOES) spacecraft at lower levels. Additionally, three of the SEP events detected by the Solar Terrestrial Relations Observatory (STEREO-A) when far from Earth would have exceeded the S1 storm level. When observing, MLSO K-Cor provides near-real-time monitoring and alerts of CMEs appearing in the inner corona, hence it can provide an earlier warning than most spacebased coronagraphs of a potentially SEP-associated CME to robotic and human explorers who are concerned about the abrupt appearance of SEPs.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"301 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11207-026-02679-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Solar Activity Using Neutrosophic Fuzzy Time Series: A Study of Yearly and Monthly Hemispherical SSNs","authors":"Geeta Mathpal,&nbsp;Arvind Bhatt,&nbsp;Rajesh Mathpal,&nbsp;Meenakshi Rana","doi":"10.1007/s11207-026-02700-7","DOIUrl":"10.1007/s11207-026-02700-7","url":null,"abstract":"<div><p>Accurate forecasting of Sunspot Numbers (SSN) is essential for understanding solar activity and for mitigating the effects of space weather on technological systems. In this study, a Neutrosophic Fuzzy Time Series (NFTS) forecasting framework is developed to predict hemispherical 13-month smoothed SSN for both monthly and yearly datasets. The proposed approach maps the dataset into a universe of discourse and partitions it using a computational-based partitioning (CBP) technique. Triangular fuzzy sets are constructed, and neutrosophic membership components-truth, indeterminacy, and false are evaluated and aggregated using a score function. Fuzzy Logical Relationships (FLRs) and Fuzzy Logical Groups (FLGs) are then established to capture temporal dynamics, followed by defuzzification using the centroid method. The performance of the model is evaluated using statistical indicators such as Root Mean Square Error (RMSE), Mean Absolute Percentage error (MAPE), correlation coefficient (R), coefficient of determination (<span>(mathrm{R}^{2})</span>), Theil’s inequality coefficient, Prediction Performance (PP), and Willmott index. The results demonstrate high predictive accuracy with low RMSE and MAPE values and strong agreement between observed and predicted values. The NFTS model reliably predicts solar cycles and space weather by accurately modeling nonlinear behavior and hemispherical asymmetry.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"301 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of the Two-Dimensional Velocity Field of the Large-Scale Coronal Wave from September 6, 2011 Using the SOLERwave Tool","authors":"Markus Baumgartner-Steinleitner,&nbsp;Astrid M. Veronig,&nbsp;Karin Dissauer,&nbsp;Jens Pomoell","doi":"10.1007/s11207-026-02690-6","DOIUrl":"10.1007/s11207-026-02690-6","url":null,"abstract":"<div><p>We investigate the two-dimensional velocity field of the fast and complex large-scale coronal wave observed on September 6, 2011. We use both a classical perturbation profile approach and the newly developed multi-sector method of the SOLERwave tool, using a Huygens-plotting-based approach. The multi-sector method utilizes perturbation profiles derived in multiple directions (sectors) to determine the location of the wavefront at a given time. The two-dimensional velocity vector at each point along the wavefront is derived by identifying the point closest to it along the wavefront observed one time step earlier and dividing the distance between the two points along the solar surface by the time difference between the observations. For the event under study the resulting two-dimensional velocity field shows a significant difference between the northward traveling and the northwestward traveling part of the wave front of over 40%, in the range of 750 to 1500 km/s. To determine the cause of this difference in speed, we investigate the coronal structures, the photospheric magnetic field distribution and the Alfvén speed derived from a steady-state coronal magnetohydro dynamic (MHD) solution along different propagation directions of the wave. We find the large difference in magnetosonic speed determined in the investigated sector as the most likely explanation for the velocity difference.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"301 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11207-026-02690-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oscillatory Phase and Acoustic Travel-Time Inconsistencies Measured Between SDO/HMI and GONG Dopplergrams","authors":"Junwei Zhao,&nbsp;Ruizhu Chen,&nbsp;S. P. Rajaguru,&nbsp;Shukur Kholikov","doi":"10.1007/s11207-026-02694-2","DOIUrl":"10.1007/s11207-026-02694-2","url":null,"abstract":"<div><p>We investigate the causes of discrepancies in meridional-circulation measurements derived from the helioseismic observations by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory (SDO/HMI) and the Global Oscillation Network Group (GONG). Using contemporaneous Dopplergrams from both instruments that are processed consistently, we measure relative oscillatory phase shifts at identical solar locations and analyze north-south acoustic travel-time shifts on both sides of the solar central meridian. Our analysis reveals a persistent area of phase-shift anomalies in the northwestern quadrant of the solar disk, whose magnitude increases over the analysis period from 2010 through 2024. After removing the axisymmetric component, the phase-shift maps display a decreasing trend from the northeastern to the southwestern quadrant, which can be misinterpreted as flows in helioseismic analyses. The travel-time measurements also show significant inconsistencies on the eastern and western sides of the central meridian for both instruments, although a close agreement between the both sides is expected. These findings indicate that both SDO/HMI and GONG carry systematic artifacts affecting meridional-circulation measurements, and that the time-varying phase anomalies and eastern-western asymmetry pose major challenges for their accurate characterization and correction.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"301 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Prediction of Solar Wind Speed Large-Scale Structures Using the Solar Full-Disk Multi-Layer Magnetograph - the New Chinese Facility Constructed for the International Meridian Circle Program","authors":"M. L. Demidov,&nbsp;X. F. Wang,&nbsp;Y. Z. Sun,&nbsp;Y. Y. Deng","doi":"10.1007/s11207-026-02684-4","DOIUrl":"10.1007/s11207-026-02684-4","url":null,"abstract":"<div><p>Space weather is one of the most rapidly developing branches of science, important not only for pure heliophysics, but also for many areas of geophysics, space- and air-flights industry, electrical systems, and even for medicine in the sense of meteorological and terrestrial magnetic field dependence of human beings health. There are direct connections between processes on the Sun and near Earth space environment, including magnetosphere and ionosphere. For this reason the International Meridian Circle Program (IMCP), suggested by China and that has recently started working in operational mode, incorporates a new solar instrument – the Solar Full-disk Multi-layer Magnetograph (SFMM) – as one of its key elements. One of the main SFMM purposes is the prediction of solar wind parameters on the basis of full-disk observations of solar magnetic fields. Because it is known that the results of space weather predictions strongly depend on initial low boundary conditions (synoptic magnetic maps from different observatories), an addition of the new source of such data is extremely important and welcome.</p><p>This paper is the first attempt to build a synoptic magnetic map on the basis of daily full-disk measurements at the SFMM, to analyze its robustness, to use it for ambient solar wind speed forecasting, and to compare the modeling results with in situ data from the <i>Advance Composition Explorer</i> (ACE). It is demonstrated that an agreement between ACE measurements and SFMM predictions is not perfect and this issue is discussed. It is demonstrated that agreement could be significantly improved by means of modifications of some PFSS–WSA model parameters. Additionally it is shown that the solar magnetic field strengths detected with SFMM are practically the same as measured by the <i>Global Oscillation Network Group (GONG)</i>, but significantly higher (almost 4 times) than measured at the <i>Wilcox Solar Observatory (WSO)</i>. This result has no direct link with space weather modeling using the PFSS approach, but is discussed in in the context of cross-calibration of magnetic field measurements made by different observatories and/or using different spectral lines.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"301 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}