Search Results (115)

Large-scale magnetic flux ropes (MFRs) usually become visible during an eruption and are the core structures of coronal mass ejections, but the nature of MFRs is still a mystery. Here, we identify a large transequatorial MFR that spans across NOAA 13373 (in the Northern Hemisphere) and NOAA 13374 (in the Southern Hemisphere). Here, NOAA 13373 is a growing, newly emerging active region with a leading sunspot moving rapidly to the southwest, and it is surrounded by a highly dynamic moving magnetic feature (MMF), while NOAA 13374 is a decaying active region with a tiny leading negative sunspot and a large fading area. Recurrent reconnection, which occurs under the MFRs around the leading sunspot of NOAA 13373, results in local energy release, appearing as local EUV brightening, and it is related to the appearance of a transequatorial MFR. The appearance of this MFR involves several stages: EUV brightening, the slow rising and expansion of the MFR and its hosted filament, and, eventually, fading and shrinking. These observations demonstrate that a large-scale MFR can exist for a long-term period and that MMFs play a key role in building up free energy and triggering small-scale reconnections in the lower atmosphere. The energy released by these reconnection events is insufficient for triggering the eruption of an MFR but results in local disturbances. Full article

Under the combined influence of climate change and human activities, the non-stationarity of reservoir runoff has significantly intensified, posing challenges for traditional statistical models to accurately capture its multi-scale abrupt changes. This study focuses on Qianping (QP) Reservoir and systematically integrates climate-driven mechanisms with machine learning approaches to uncover the patterns of runoff evolution and develop high-precision prediction models. The findings offer a novel paradigm for adaptive reservoir operation under non-stationary conditions. In this paper, we employ methods including extreme-point symmetric mode decomposition (ESMD), Bayesian ensemble time series decomposition (BETS), and cross-wavelet transform (XWT) to investigate the variation trends and mutation features of the annual runoff in QP Reservoir. Additionally, four models—ARIMA, LSTM, LSTM-RF, and LSTM-CNN—are utilized for runoff prediction and analysis. The results indicate that: (1) the annual runoff of QP Reservoir exhibits a quasi-8.25-year mid-short-term cycle and a quasi-13.20-year long-term cycle on an annual scale; (2) by using Bayesian estimators based on abrupt change year detection and trend variation algorithms, an abrupt change point with a probability of 79.1% was identified in 1985, with a confidence interval spanning 1984 to 1986; (3) cross-wavelet analysis indicates that the periodic associations between the annual runoff of QP Reservoir and climate-driving factors exhibit spatiotemporal heterogeneity: the AMO, AO, and PNA show multi-scale synergistic interactions; the DMI and ENSO display only phase-specific weak coupling; while solar sunspot activity modulates runoff over long-term cycles; and (4) The NSE of the ARIMA, LSTM, LSTM-RF, and LSTM-CNN models all exceed 0.945, the RMSE is below 0.477 × 10⁹ m³, and the MAE is below 0.297 × 10⁹ m³, Among them, the LSTM-RF model demonstrated the highest accuracy and the most stable predicted fluctuations, indicating that future annual runoff will continue to fluctuate but with a decreasing amplitude. Full article

To ensure the long-term consistency of sunspot group data, it is essential to harmonize measurements from SOHO/MDI and SDO/HMI, two major solar observatories with overlapping coverage. In our analysis, we use two complementary sets of data: SOHO/MDI–Debrecen Sunspot Data (SDD) and SDO/HMI–Debrecen Sunspot Data (HMIDD). Our objective is to identify systematic differences between their recorded parameters and to assess whether their data can be combined into a coherent time series. While the overlap between the datasets spans only about one year, this period allows for a direct statistical comparison without the need for additional image processing. Though the two instruments do not measure identical area values, our results reveal a strong linear relationship between them, which is in line with earlier studies. On the other hand, a systematic discrepancy in their magnetic field strength measurements was observed. Contrary to previous findings, SDO/HMI magnetic field values tend to be higher than those from SOHO/MDI. These differences may arise from the use of different calibration procedures and measurement techniques, or from the physical characteristics of the sunspot groups themselves. These results highlight the challenges involved in unifying data from multiple solar instruments that have been captured over extended time periods. While broad consistencies are observable, the differences between sunspot groups and measurement parameters demonstrate the importance of using careful, instrument-aware calibration approaches when combining such datasets. Full article

Solar flares, caused by magnetic field reconnection in the sun’s atmosphere, are intense bursts of electromagnetic radiation that can disrupt the Earth’s space environment, affecting communication systems, GPSs, and satellites. Traditional physics-based methods for solar flare forecasting have utilized the statistical relationships between solar activity indicators, such as sunspots and magnetic field properties, employing techniques like Poisson distributions and discriminant analysis to estimate probabilities and identify critical parameters. While these methods provide valuable insights, limitations in predictive accuracy have driven the integration of deep learning approaches. With the accumulation of solar observation data and the development of data-driven algorithms, deep learning methods have been widely used to build solar flare prediction models. Most research has focused on designing or selecting the right deep network for the task. However, the influence of the magnetic field height on deep-learning-based prediction models has not been studied. This paper investigates how different magnetic field heights affect solar flare prediction performance. Active regions were observed using HMI magnetograms from 2010 to 2019. The magnetic field heights were stratified to create a database of active regions, and deep neural networks like AlexNet, ResNet-18, and SqueezeNet were used to evaluate prediction performance. The results show that predictions at around 7200 km above the photosphere outperform other heights, aligning with physical method analysis. At this altitude, the average AUC of the predictions from the three models reaches 0.788. Full article

In this paper, we apply several latest results from statistical physics on the probability and energy of knotting to study the knotted field lines in solar corona. Since the solar magnetic field in small scale can be seen as nearly random, by assuming that the magnetic field lines behave similarly to random loops, we find the probability P of certain knot type K for the field line knotting as a function to the distance L between the foot-points of sunspots, which is $P_K\left(L\right)=C_K{L}^{2{\alpha }_K}exp\left({\displaystyle \frac{-L^2}{\beta }}\right)$. From the equation, we find that the variety of knot type increases with the distance. Since knotting is the topological resemblance to magnetic helicity, which is an invariant for ideal MHD, our result enriches the understanding of the probability of magnetic helicity as well as field line structure in active regions. Based on the relation between knotting and magnetic energy, we provide support to the high variety of field line knot types. Full article

Bipolar emerging flux regions (EFRs) form active regions (ARs) that generally evolve into a pre-existing magnetic environment in the solar atmosphere. Reconfiguration of the small- and large-scale magnetic connectivities is invoked to explain a plethora of energy-release phenomena observed at the sites of EFRs. These include brightening events, surges, and jets, whose triggers and relationships are still unclear. In this context, we study the formation of a proto-spot in AR NOAA 11462 by analyzing spectropolarimetric and spectroscopic measurements taken by the Interferometric Bidimensional Spectrometer along the Fe I 630.2 nm and Ca II 854.2 nm lines on 17 April 2012. We complement these high-resolution data with simultaneous SDO satellite observations. The proto-spot forms from magnetic flux and emerges into the photosphere, coalescing following plasma flows in its surroundings. The chromospheric and higher atmosphere observations show that flux emergence occurs in a pre-existing magnetic environment, with small- and large-scale coronal arcades that seemingly shape the proto-spot formation in the upper atmospheric layers. In addition, in the chromosphere, we observe an arch filament system and repeated intense brightening events and surges, likely due to magnetic interactions of the new flux with the pre-existing overlying coronal field. These phenomena have been observed since the early stages of the new flux emergence. Full article

On 25 February 2014 at around 00:39 UT, a major solar flare (code: SOL2014-02-25T00:39) erupted at sunspot region AR11990. Using the updated science quality data of GOES-15, it has been classified as an X7.13 solar flare. This gave rise to the electron density changes that affected the strengths of ionospheric electric currents. In this work, the difference in total electron content (TEC), between the TEC during a flare day and a quiet, fitted TEC, $\Delta \mathrm{T}\mathrm{E}\mathrm{C}$, and rate of change of TEC, $dTEC/dt$, are determined to observe electron density changes due to the solar flare over a low-latitude region. These stations are at Quezon City (PIMO) and Taguig City (PTAG). Also, responses in the geomagnetic field component, $\Delta H$, are calculated along with the variations in the equatorial electrojet (EEJ) strength. These are observed at equatorial, Davao (DAV) and Cagayan de Oro (CDO), and off-equatorial, Muntinlupa (MUT) and Legazpi (LGZ), stations. The resulting $\Delta \mathrm{T}\mathrm{E}\mathrm{C}$ values were 1.17–1.97 TECU while $dTEC/dt$ maxima were 0.29–0.48 TECU/min. The $dTEC/dt$ maxima were found to concur with the time the solar EUV reached peak intensity at 00:45 UT, 4 min before the flare (i.e., X-ray) peaked. Furthermore, the $\Delta H$ variations exhibited larger enhancements at the equatorial stations. These are mostly attributed to the EEJ contributing to the geomagnetic field variations. The amplification experienced by the EEJ due to the increased ionospheric conductivity is then reflected in the geomagnetic responses. For the CDO-LGZ stations, the EEJ strength reached ~37 nT, while for the DAV-MUT, this was ~60 nT. Full article

This study introduces an innovative empirical methodology by integrating spatial panel models with satellite imagery data from 1970 to 2019. This innovative approach illuminates the effects of greenhouse gas emissions, deforestation, and various global variables on regional temperature shifts and the environmental repercussions of land-use alterations, establishing a substantial empirical basis for climate change. The results revealed that global variables such as sunspot activity, the length of day (LOD), and the Global Mean Sea Level (GMSL) have negligible impacts on global temperature variations. This model uncovers the nuanced effect of deforestation on global temperatures, highlighting a decrease in temperature following deforestation above 40°N latitude, contrary to the warming effect observed in lower latitudes. Exceptionally, deforestation within the 10° N to 10° S tropical bands results in a temperature decrease, challenging the established theories. The results suggest that converting forests to grass/shrublands and croplands plays a significant role in these temperature dynamics. Full article

This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting long-memory time series. This characteristic is confirmed using popular datasets such as sunspot data and Australian beer production data. This approach provides a robust framework for accurate and reliable forecasting in long-memory time series. Additionally, we compare the GARMA-LSTM model with other implemented models, such as GARMA, TBATS, ARIMA, and ANN, highlighting its ability to address both long-memory and non-linear dynamics. Finally, we discuss the representativeness of the datasets selected and the adaptability of the proposed hybrid model to various time series scenarios. Full article

This study aims to investigate the impact of ionospheric models on Global Navigation Satellite System (GNSS) positioning and proposes an ionospheric prediction method based on a Transformer deep learning model. We construct a Transformer-based deep learning model that utilizes global ionospheric maps as input to achieve spatiotemporal prediction of Total Electron Content (TEC). To gain a deeper understanding of the model’s prediction mechanism, we employ integrated gradients for explainability analysis. The results reveal the key ionospheric features that the model focuses on during prediction, providing guidance for further model optimization. This study demonstrates the efficacy of a Transformer-based model in predicting Vertical Total Electron Content (VTEC), achieving comparable accuracy to traditional methods while offering enhanced adaptability to spatial and temporal variations in ionospheric behavior. Furthermore, the application of advanced explainability techniques, particularly the Integrated Decision Gradient (IDG) method, provides unprecedented insights into the model’s decision-making process, revealing complex feature interactions and spatial dependencies in VTEC prediction, thus bridging the gap between deep learning capabilities and explainable scientific modeling in geophysical applications. The model achieved positioning accuracies of −1.775 m, −2.5720 m, and 2.6240 m in the East, North, and Up directions respectively, with standard deviations of 0.3399 m, 0.2971 m, and 1.3876 m. For VTEC prediction, the model successfully captured the diurnal variations of the Equatorial Ionization Anomaly (EIA), with differences between predicted and CORG VTEC values typically ranging from −6 to 6 TECU across the study region. The gradient score analysis revealed that solar activity indicators (F10.7 and sunspot number) showed the strongest correlations (0.7–0.8) with VTEC variations, while geomagnetic indices exhibited more localized impacts. The IDG method effectively identified feature importance variations across different spatial locations, demonstrating the model’s ability to adapt to regional ionospheric characteristics. Full article

Recent advancements have led to a growing prevalence of studies examining ionospheric and atmospheric anomalies as potential precursors to earthquakes. In this context, the study involved analyzing variations in ionospheric total electron content (TEC), investigating anomalies, assessing space weather conditions, and examining changes in atmospheric parameters to evaluate potential precursors and post-seismic effects related to the Mw 7.7 and Mw 7.6 earthquakes that struck Kahramanmaraş consecutively in 2023. To compute the total electron content (TEC) values, data from 29 GNSS receivers covering a period of approximately 49 days were processed. In addition, since identical code signals were not available among all receiver stations, the study conducted an analysis of TEC estimations applying different GPS codes. To analyze space weather conditions, which are considered the main source of changes in the ionosphere, variations in sunspot number, solar activity index, magnetic activity indices (Kp and Dst), and geomagnetic field components were examined across the relevant period. To assess the potential presence of a distinct relationship between seismic activity at the Earth’s surface and ionospheric conditions, atmospheric parameters including temperature, relative humidity, and pressure were meticulously monitored and evaluated. As a result of the study, it was determined that TEC anomalies that could be evaluated as earthquake precursors independent of space weather conditions were observed starting from the 3rd day before the earthquake, and high positive TEC anomalies occurred immediately after the earthquakes. In atmospheric parameters, the change in behavior, particularly in temperature value, 10 days before the earthquake, is noteworthy. Full article

This study demonstrates a rich complexity of the time–frequency ionospheric signal spectrum, dependent on the measurement type and platform. Different phenomena contributing to satellite-derived and ground-derived geophysical data that only selected signal bands can be potentially sensitive to seismicity over time, and they are applicable in lithosphere–atmosphere–ionosphere coupling (LAIC) studies. In this study, satellite-derived and ground-derived ionospheric observations are filtered by a Fourier-based band-pass filter, and an experimental selection of potentially sensitive frequency bands has been carried out. This work focuses on band-pass filtered ionospheric observations and seismic activity in the region of the Aegean Sea over a two-year time period (2020–2021), with particular focus on the entire system of tectonic plate junctions, which are suspected to be a potential source of ionospheric disturbances distributed over hundreds of kilometers. The temporal evolution of seismicity power in the Aegean region is represented by the record of earthquakes characterized by M ≥ 4.5, used for the estimation of cumulative seismic energy. The ionospheric response to LAIC is explored in three data types: short inspections of in situ electron density (Ne) over a tectonic plate boundary by Swarm satellites, stationary determination of three Ne density profile parameters by the Athens Digisonde station AT138 (maximum frequency of the F2 layer: foF2; maximum frequency of the sporadic E layer: foEs; and frequency spread: ff), and stationary measure of vertical total electron content (VTEC) interpolated from a UPC-IonSAT Quarter-of-an-hour time resolution Rapid Global ionospheric map (UQRG) near Athens. The spectrograms are made with the use of short-term Fourier transform (STFT). These frequency bands in the spectrograms, which show a notable coincidence with seismicity, are filtered out and compared to cumulative seismic energy in the Aegean Sea, to the geomagnetic Dst index, to sunspot number (SN), and to the solar radio flux (F10.7). In the case of Swarm, STFT allows for precise removal of long-wavelength Ne signals related to specific latitudes. The application of STFT to time series of ionospheric parameters from the Digisonde station and GIM VTEC is crucial in the removal of seasonal signals and strong diurnal and semi-diurnal signal components. The time series formed from experimentally selected wavebands of different ionospheric observations reveal a moderate but notable correlation with the seismic activity, higher than with any solar radiation parameter in 8 out of 12 cases. The correlation coefficient must be treated relatively and with caution here, as we have not determined the shift between seismic and ionospheric events, as this process requires more data. However, it can be observed from the spectrograms that some weak signals from selected frequencies are candidates to be related to seismic processes. Full article

The dendrochronological parameters of 97 pedunculate oak (Quercus robur L.) trees including 20 plus trees (142-year-old on average) and four half-sib families for four of them were analyzed considering also specifically years of the most severe droughts that were identified using average monthly air temperature and precipitation data. The tree-ring width (TRW) was mostly affected by air temperature that had the largest cross-dating indices (CDI), up to 78% maximum. However, the 32-year Brückner–Egeson–Lockyer cycle (a climatic cycle of approximately 30–40 years that correlates with sunspot activity) was more reflected in the TRW dynamics in plus trees than precipitation and air temperature. A high-frequency of abnormal TRW was clearly observed during drought periods and in the following 2–3 years. Tree radial-growth reduction due to drought stress varied significantly between families. The resistance to drought based on TRW was higher in the maternal plus oak trees than in progeny. Drought resulted in reduced growth during the subsequent year(s); hence, the minimum growth occurred after the actual climate event. Autumn–winter precipitation and weather conditions were of the greatest importance at the onset of active vegetation in April and May. The influence of air temperature on oak growth was the largest in March (r = 0.39, p < 0.05). The strongest positive correlation between precipitation and growth (with r up to 0.38) was observed in May 2023. Plus trees had a high adaptive potential due to the stability of radial growth during drought with high resistance (Rt = 1.29) and resilience (Rs = 1.09) indexes. The offspring of families 1 (Rt = 0.89, Rs = 0.89) and 2 (Rt = 1.04, Rs = 0.87) had similar resistance and resilience, but the recovery indices (Rc) for offspring in families 1, 2 and 3 exceeded the recovery values for plus trees. For offspring in families 3 and 4, the index values were lower. The revealed responses of wood growth of plus trees to climatic parameters estimated as resistance (Rt), resilience (Rs) and recovery (Rc) indexes and similar responses in their progeny can be used in breeding pedunculate oak for wood growth productivity and drought resistance. Full article

High-frequency drought hazards have presented persistent challenges for environmental management and sustainable development in the South Hebei Plain, China. In this paper, the assessment of meteorological droughts in the South Hebei Plain was conducted using a multifaceted approach to ensure a comprehensive analysis. Our results demonstrated that distinct timescale cycles, ranging from centennial–semicentennial to interdecadal variations, can be identified over the past few centuries. These cycles aligned with patterns observed in the middle Yangtze basin and corresponded to regional climatic conditions. The drought cycles in the South Hebei Plain showed significant correlations with variations in the monsoon climate, sunspot activity, global changes, and human disturbances. Changes in the frequency, duration, and intensity of droughts have notably impacted hydrological variations. Extreme droughts, in particular, have heightened concerns about their effects on river systems, potentially increasing the risk of channel migration. This study enhanced our understanding of meteorological hazard patterns in the South Hebei Plain and provided valuable insights into different stages of drought management. It thus can offer lessons for improving drought preparedness and resilience and for formulating adaptive measures to mitigate future droughts and promote regional sustainability. Full article

Runoff prediction is of great importance to water utilization and water-project regulation. Although sun activity has been considered an important factor in runoff, little modeling has been constructed. This study put forward a forecast heuristic combining back propagation neural network (BPNN) and particle swarm optimization (PSO) for annual runoff based on sunspot number and applied it to the Yellow River of China for the period 1956–2016 and assessed the contribution of the sunspot number by placing sole BPNN modeling on the time series as a contrast. First, the heuristic is made up of BPNN calibration and PSO optimization: (1) we use historical data to calibrate BPNN models and obtain a prediction of the sunspot number for training and testing stages; (2) we use the PSO to minimize the difference between the predicted runoff of both BPNN and a linear equation for forecasting stage. Second, the application offers interesting findings: (1) while BPNN calibration obtains first-class forecasting with the ratio >85% with <20% absolute error in training and testing stages, the PSO can achieve similar performance in the forecasting stage; (2) the heuristic can achieve better prediction in years with a lower sunspot number; (3) besides the influence of the sun activity, atmospheric circulation, water usage, and water-project regulation do play important roles on the measured or natural runoff to some extent. This study could provide useful insights into further forecasting of measured and natural runoff under this forecast heuristic in the world. Full article