Denny M. Oliveira, James M. Weygand, John C. Coxon, Eftyhia Zesta
In this study, we investigate the effects caused by interplanetary (IP) shock impact angles on the subsequent ground dB/dt variations during substorms. IP shock impact angles have been revealed as a major factor controlling the subsequent geomagnetic activity, meaning that shocks with small inclinations with the Sun-Earth line are more likely to trigger higher geomagnetic activity resulting from nearly symmetric magnetospheric compressions. Such field variations are linked to the generation of geomagnetically induced currents (GICs), which couple to artificial conductors on the ground leading to deleterious consequences. We use a sub-set of a shock data base with 237 events observed in the solar wind at L1 upstream of the Earth, and large arrays of ground magnetometers at stations located in North America and Greenland. The spherical elementary current system methodology is applied to the geomagnetic field data, and field-aligned-like currents in the ionosphere are derived. Then, such currents are inverted back to the ground and dB/dt variations are computed. Geographic maps are built with these field variations as a function of shock impact angles. The main findings of this investigation are: (a) typical dB/dt variations (5–10 nT/s) are caused by shocks with moderate inclinations; (b) the more frontal the shock impact, the more intense and the more spatially defined the ionospheric current amplitudes; and (c) nearly frontal shocks trigger more intense dB/dt variations with larger equatorward latitudinal expansions. Therefore, the findings of this work provide new insights for GIC forecasting focusing on nearly frontal shock impacts on the magnetosphere.
{"title":"Substorm-Time Ground dB/dt Variations Controlled by Interplanetary Shock Impact Angles: A Statistical Study","authors":"Denny M. Oliveira, James M. Weygand, John C. Coxon, Eftyhia Zesta","doi":"10.1029/2023sw003767","DOIUrl":"https://doi.org/10.1029/2023sw003767","url":null,"abstract":"In this study, we investigate the effects caused by interplanetary (IP) shock impact angles on the subsequent ground <i>d</i><i>B</i>/<i>d</i><i>t</i> variations during substorms. IP shock impact angles have been revealed as a major factor controlling the subsequent geomagnetic activity, meaning that shocks with small inclinations with the Sun-Earth line are more likely to trigger higher geomagnetic activity resulting from nearly symmetric magnetospheric compressions. Such field variations are linked to the generation of geomagnetically induced currents (GICs), which couple to artificial conductors on the ground leading to deleterious consequences. We use a sub-set of a shock data base with 237 events observed in the solar wind at L1 upstream of the Earth, and large arrays of ground magnetometers at stations located in North America and Greenland. The spherical elementary current system methodology is applied to the geomagnetic field data, and field-aligned-like currents in the ionosphere are derived. Then, such currents are inverted back to the ground and <i>d</i><i>B</i>/<i>d</i><i>t</i> variations are computed. Geographic maps are built with these field variations as a function of shock impact angles. The main findings of this investigation are: (a) typical <i>d</i><i>B</i>/<i>d</i><i>t</i> variations (5–10 nT/s) are caused by shocks with moderate inclinations; (b) the more frontal the shock impact, the more intense and the more spatially defined the ionospheric current amplitudes; and (c) nearly frontal shocks trigger more intense <i>d</i><i>B</i>/<i>d</i><i>t</i> variations with larger equatorward latitudinal expansions. Therefore, the findings of this work provide new insights for GIC forecasting focusing on nearly frontal shock impacts on the magnetosphere.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"142 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140026192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Space weather indices are used to drive forecasts of thermosphere density, which directly affects objects in low‐Earth orbit (LEO) through atmospheric drag force. A set of proxies and indices (drivers), F10.7, S10.7, M10.7, and Y10.7 are used as inputs by the JB2008, (https://doi.org/10.2514/6.2008‐6438) thermosphere density model. The United States Air Force (USAF) operational High Accuracy Satellite Drag Model (HASDM), relies on JB2008, (https://doi.org/10.2514/6.2008‐6438), and forecasts of solar drivers from a linear algorithm. We introduce methods using long short‐term memory (LSTM) model ensembles to improve over the current prediction method as well as a previous univariate approach. We investigate the usage of principal component analysis (PCA) to enhance multivariate forecasting. A novel method, referred to as striped sampling, is created to produce statistically consistent machine learning data sets. We also investigate forecasting performance and uncertainty estimation by varying the training loss function and by investigating novel weighting methods. Results show that stacked neural network model ensembles make multivariate driver forecasts which outperform the operational linear method. When using MV‐MLE (multivariate multi‐lookback ensemble), we see an improvement of RMSE for F10.7, S10.7, M10.7, and Y10.7 of 17.7%, 12.3%, 13.8%, 13.7% respectively, over the operational method. We provide the first probabilistic forecasting method for S10.7, M10.7, and Y10.7. Ensemble approaches are leveraged to provide a distribution of predicted values, allowing an investigation into robustness and reliability (R&R) of uncertainty estimates. Uncertainty was also investigated through the use of calibration error score (CES), with the MV‐MLE providing an average CES of 5.63%, across all drivers.
{"title":"PROBABILISTIC SHORT TERM SOLAR DRIVER FORECASTING WITH NEURAL NETWORK ENSEMBLES","authors":"Joshua D. Daniell, P. Mehta","doi":"10.33915/etd.12262","DOIUrl":"https://doi.org/10.33915/etd.12262","url":null,"abstract":"Space weather indices are used to drive forecasts of thermosphere density, which directly affects objects in low‐Earth orbit (LEO) through atmospheric drag force. A set of proxies and indices (drivers), F10.7, S10.7, M10.7, and Y10.7 are used as inputs by the JB2008, (https://doi.org/10.2514/6.2008‐6438) thermosphere density model. The United States Air Force (USAF) operational High Accuracy Satellite Drag Model (HASDM), relies on JB2008, (https://doi.org/10.2514/6.2008‐6438), and forecasts of solar drivers from a linear algorithm. We introduce methods using long short‐term memory (LSTM) model ensembles to improve over the current prediction method as well as a previous univariate approach. We investigate the usage of principal component analysis (PCA) to enhance multivariate forecasting. A novel method, referred to as striped sampling, is created to produce statistically consistent machine learning data sets. We also investigate forecasting performance and uncertainty estimation by varying the training loss function and by investigating novel weighting methods. Results show that stacked neural network model ensembles make multivariate driver forecasts which outperform the operational linear method. When using MV‐MLE (multivariate multi‐lookback ensemble), we see an improvement of RMSE for F10.7, S10.7, M10.7, and Y10.7 of 17.7%, 12.3%, 13.8%, 13.7% respectively, over the operational method. We provide the first probabilistic forecasting method for S10.7, M10.7, and Y10.7. Ensemble approaches are leveraged to provide a distribution of predicted values, allowing an investigation into robustness and reliability (R&R) of uncertainty estimates. Uncertainty was also investigated through the use of calibration error score (CES), with the MV‐MLE providing an average CES of 5.63%, across all drivers.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"52 3","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140269671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study focuses on modeling the characteristics of nighttime topside Ionospheric Plasma Irregularities (PI) on a global scale. We utilize Random Forest (RF) and a one-dimensional Convolutional Neural Network (1D-CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. Our objective is to simulate monthly global PI characteristics using a multilayer 1D-CNN model trained on 12 space weather and ionospheric parameters. In addition, we investigate the most influential input parameters for predicting global nighttime PI characteristics. Our findings indicate that non-equinox months exhibit the highest equatorial PI magnitude over the American-African longitudinal sector, contrary to the expected higher Rayleigh-Taylor instability growth rate during equinox months. Winter months display the most intense and widespread vertically and horizontally distributed equatorial PI patterns. We also observe double peaks across geomagnetic latitudes and longitudinally varying wavelike irregularity structures, particularly in May, August, and predominantly in September. Furthermore, north-south hemispherical asymmetry in PI observed across different seasons. Through the RF parameter importance analysis method, we determine that temporal, geographical, and magnetic disturbance-related factors play a crucial role in predicting global PI variabilities. These findings emphasize the significance of these variables in controlling the strongest PI characteristics observed in the Atlantic sector, which has garnered considerable attention in PI research. The employed 1D-CNN model demonstrates exceptional predictive capabilities, exhibiting a strong correlation of 0.98 for global PI characteristics across all months and satellites.
本研究的重点是在全球范围内模拟夜间顶部电离层等离子体不规则现象（PI）的特征。我们利用随机森林（RF）和一维卷积神经网络（1D-CNN）模型，结合来自 Swarm A、B 和 C 卫星的数据、来自 OMNIWeb 数据中心的空间气象数据以及带状和经向风模型数据。我们的目标是使用根据 12 个空间天气和电离层参数训练的多层 1D-CNN 模型模拟每月全球 PI 特征。此外，我们还研究了对预测全球夜间 PI 特性最有影响的输入参数。我们的研究结果表明，非春分月份在美洲-非洲纵向扇面上表现出最高的赤道 PI 幅值，这与预期的春分月份较高的瑞雷-泰勒不稳定性增长率相反。冬季显示出最强烈和最广泛的垂直和水平分布赤道 PI 模式。我们还观察到地磁纬度上的双峰和纵向变化的波状不规则结构，尤其是在 5 月和 8 月，主要是在 9 月。此外，在不同季节还观测到南北半球不对称的 PI。通过射频参数重要性分析方法，我们确定与时间、地理和磁干扰有关的因素在预测全球 PI 变率中起着至关重要的作用。这些发现强调了这些变量在控制大西洋扇区观测到的最强 PI 特征方面的重要作用，这在 PI 研究中引起了广泛关注。所采用的 1D-CNN 模型显示出卓越的预测能力，在所有月份和卫星的全球 PI 特性方面显示出 0.98 的强相关性。
{"title":"Modeling Equatorial to Mid-Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning","authors":"Ephrem Beshir Seba, Giovanni Lapenta","doi":"10.1029/2023sw003754","DOIUrl":"https://doi.org/10.1029/2023sw003754","url":null,"abstract":"This study focuses on modeling the characteristics of nighttime topside Ionospheric Plasma Irregularities (PI) on a global scale. We utilize Random Forest (RF) and a one-dimensional Convolutional Neural Network (1D-CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. Our objective is to simulate monthly global PI characteristics using a multilayer 1D-CNN model trained on 12 space weather and ionospheric parameters. In addition, we investigate the most influential input parameters for predicting global nighttime PI characteristics. Our findings indicate that non-equinox months exhibit the highest equatorial PI magnitude over the American-African longitudinal sector, contrary to the expected higher Rayleigh-Taylor instability growth rate during equinox months. Winter months display the most intense and widespread vertically and horizontally distributed equatorial PI patterns. We also observe double peaks across geomagnetic latitudes and longitudinally varying wavelike irregularity structures, particularly in May, August, and predominantly in September. Furthermore, north-south hemispherical asymmetry in PI observed across different seasons. Through the RF parameter importance analysis method, we determine that temporal, geographical, and magnetic disturbance-related factors play a crucial role in predicting global PI variabilities. These findings emphasize the significance of these variables in controlling the strongest PI characteristics observed in the Atlantic sector, which has garnered considerable attention in PI research. The employed 1D-CNN model demonstrates exceptional predictive capabilities, exhibiting a strong correlation of 0.98 for global PI characteristics across all months and satellites.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"12 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140003769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Beggan, E. Clarke, E. Lawrence, E. Eaton, J. Williamson, K. Matsumoto, H. Hayakawa
Dedicated scientific measurements of the strength and direction of the Earth's magnetic field began at Greenwich and Kew observatories in London, United Kingdom, in the middle of the nineteenth century. Using advanced techniques for the time, collimated light was focussed onto mirrors mounted on free‐swinging magnetized needles which reflected onto photographic paper, allowing continuous analog magnetograms to be recorded. By good fortune, both observatories were in full operation during the so‐called Carrington storm in early September 1859 and its precursor storm in late August 1859. Based on digital images of the magnetograms and information from the observatory yearbooks and scientific papers, it is possible to scale the measurements to International System of Units (SI units) and extract quasi‐minute cadence spot values. However, due to the magnitude of the storms, the periods of the greatest magnetic field variation were lost as the traces moved off‐page. We present the most complete digitized magnetic records to date of the 10‐day period from 25 August to 5 September 1859 encompassing the Carrington storm and its lesser recognized precursor on 28 August. We demonstrate the good correlation between observatories and estimate the instantaneous rate of change of the magnetic field.
{"title":"Digitized Continuous Magnetic Recordings for the August/September 1859 Storms From London, UK","authors":"C. Beggan, E. Clarke, E. Lawrence, E. Eaton, J. Williamson, K. Matsumoto, H. Hayakawa","doi":"10.1029/2023sw003807","DOIUrl":"https://doi.org/10.1029/2023sw003807","url":null,"abstract":"Dedicated scientific measurements of the strength and direction of the Earth's magnetic field began at Greenwich and Kew observatories in London, United Kingdom, in the middle of the nineteenth century. Using advanced techniques for the time, collimated light was focussed onto mirrors mounted on free‐swinging magnetized needles which reflected onto photographic paper, allowing continuous analog magnetograms to be recorded. By good fortune, both observatories were in full operation during the so‐called Carrington storm in early September 1859 and its precursor storm in late August 1859. Based on digital images of the magnetograms and information from the observatory yearbooks and scientific papers, it is possible to scale the measurements to International System of Units (SI units) and extract quasi‐minute cadence spot values. However, due to the magnitude of the storms, the periods of the greatest magnetic field variation were lost as the traces moved off‐page. We present the most complete digitized magnetic records to date of the 10‐day period from 25 August to 5 September 1859 encompassing the Carrington storm and its lesser recognized precursor on 28 August. We demonstrate the good correlation between observatories and estimate the instantaneous rate of change of the magnetic field.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"70 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140414988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Oyama, H. Vanhamäki, L. Cai, A. Shinbori, K. Hosokawa, T. Sakanoi, K. Shiokawa, A. Aikio, I. I. Virtanen, Y. Ogawa, Y. Miyoshi, S. Kurita, N. Nishitani
Solar cycles 24–25 were quiet until a geomagnetic storm with a Sym-H index of −170 nT occurred in late March 2023. On March 23–24, a Fabry-Perot interferometer (FPI; 630 nm) in Tromsø, Norway, recorded the highest thermospheric wind speed of over 500 m/s since 2009. Comparisons with magnetometer readings in Scandinavia showed that a large amount of electromagnetic energy was transferred to the ionosphere-thermosphere system. Total electron content maps suggested an enlarged auroral oval and revealed that the FPI observed winds near the polar cap instead of inside the oval for a long period during the storm main phase. The FPI wind had a strong equatorward component during the storm, likely because of the powerful anti-sunward ionospheric plasma flow in the polar cap. The positive Y-component of the IMF for 6 days before the storm caused a successive westward component of the FPI-measured wind during the storm main phase. On March 24, the first day of the storm recovery phase, thermospheric wind disturbed and the ionospheric density decreased significantly at high latitudes. This density depression lasted for several days, and a large amount of electromagnetic energy during the storm modified the thermospheric dynamics and ionospheric plasma density.
{"title":"Thermospheric Wind Response to March 2023 Storm: Largest Wind Ever Observed With a Fabry-Perot Interferometer in Tromsø, Norway Since 2009","authors":"S. Oyama, H. Vanhamäki, L. Cai, A. Shinbori, K. Hosokawa, T. Sakanoi, K. Shiokawa, A. Aikio, I. I. Virtanen, Y. Ogawa, Y. Miyoshi, S. Kurita, N. Nishitani","doi":"10.1029/2023sw003728","DOIUrl":"https://doi.org/10.1029/2023sw003728","url":null,"abstract":"Solar cycles 24–25 were quiet until a geomagnetic storm with a Sym-H index of −170 nT occurred in late March 2023. On March 23–24, a Fabry-Perot interferometer (FPI; 630 nm) in Tromsø, Norway, recorded the highest thermospheric wind speed of over 500 m/s since 2009. Comparisons with magnetometer readings in Scandinavia showed that a large amount of electromagnetic energy was transferred to the ionosphere-thermosphere system. Total electron content maps suggested an enlarged auroral oval and revealed that the FPI observed winds near the polar cap instead of inside the oval for a long period during the storm main phase. The FPI wind had a strong equatorward component during the storm, likely because of the powerful anti-sunward ionospheric plasma flow in the polar cap. The positive Y-component of the IMF for 6 days before the storm caused a successive westward component of the FPI-measured wind during the storm main phase. On March 24, the first day of the storm recovery phase, thermospheric wind disturbed and the ionospheric density decreased significantly at high latitudes. This density depression lasted for several days, and a large amount of electromagnetic energy during the storm modified the thermospheric dynamics and ionospheric plasma density.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"45 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140003557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. S. Rao, Nandita Srivastava, Monti Chakraborty, Sandeep Kumar, D. Chakrabarty
On 3 July 2021, an X1.5 solar flare from the National Oceanic and Atmospheric Administration solar Active Region AR12838 (24°N, 88°W) occurred at 14:18 UT, peaked at 14:29 UT, and decayed at 14:34 UT. The study of this X1.5 solar flare is significant due to its unique geomagnetic crochet feature at high latitudes and its effective signature on Earth. The study examined X-rays, the extreme ultraviolet spectrum, ionospheric equivalent current (IEC), and geomagnetic field components. The study reveals a sudden increase in IEC during the X1.5 flare episode, forming a zonal current region and producing a geomagnetic crochet signature in geomagnetic field components at high latitudes (50°–80°N) along the 11°–26°E longitude sector during the flare peak time. All three geomagnetic field components show different sensitivity to the solar flare effect (sfe), and the amplitude and phase of the geomagnetic crochet across latitudes (for a given longitude) are consistent with the variations in the IEC. The present study is the first to appraise geomagnetic crochets of low magnitude (8–40 nT) and short duration (10–15 min) at high latitudes, particularly in the polar cusp region, during the X-class limb flare.
2021年7月3日，美国国家海洋和大气管理局太阳活动区域AR12838（北纬24°，西经88°）发生了一次X1.5太阳耀斑，发生在世界标准时14:18，在世界标准时14:29达到峰值，在世界标准时14:34衰减。对这一 X1.5 级太阳耀斑的研究意义重大，因为它在高纬度地区具有独特的地磁钩编特征，并对地球产生了有效影响。研究考察了 X 射线、极紫外光谱、电离层等效电流 (IEC) 和地磁场成分。研究显示，在 X1.5 耀斑发生期间，电离层等效电流突然增加，形成了一个带状电流区，并在耀斑高峰期沿 11°-26°E 经度扇形高纬度地区（50°-80°N）的地磁场分量中产生了地磁钩针特征。所有三个地磁场分量都对太阳耀斑效应（sfe）表现出不同的敏感性，而且地磁钩针的跨纬度（给定经度）振幅和相位与 IEC 的变化一致。本研究首次评估了 X 级边缘耀斑期间高纬度地区，特别是极尖区的低幅（8-40 nT）、短时（10-15 分钟）地磁钩。
{"title":"Observations of Geomagnetic Crochet at High-Latitudes Due To X1.5 Class Solar Flare on 3 July 2021","authors":"S. S. Rao, Nandita Srivastava, Monti Chakraborty, Sandeep Kumar, D. Chakrabarty","doi":"10.1029/2023sw003719","DOIUrl":"https://doi.org/10.1029/2023sw003719","url":null,"abstract":"On 3 July 2021, an X1.5 solar flare from the National Oceanic and Atmospheric Administration solar Active Region AR12838 (24°N, 88°W) occurred at 14:18 UT, peaked at 14:29 UT, and decayed at 14:34 UT. The study of this X1.5 solar flare is significant due to its unique geomagnetic crochet feature at high latitudes and its effective signature on Earth. The study examined X-rays, the extreme ultraviolet spectrum, ionospheric equivalent current (IEC), and geomagnetic field components. The study reveals a sudden increase in IEC during the X1.5 flare episode, forming a zonal current region and producing a geomagnetic crochet signature in geomagnetic field components at high latitudes (50°–80°N) along the 11°–26°E longitude sector during the flare peak time. All three geomagnetic field components show different sensitivity to the solar flare effect (sfe), and the amplitude and phase of the geomagnetic crochet across latitudes (for a given longitude) are consistent with the variations in the IEC. The present study is the first to appraise geomagnetic crochets of low magnitude (8–40 nT) and short duration (10–15 min) at high latitudes, particularly in the polar cusp region, during the X-class limb flare.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"2015 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139967961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Hübert, C. D. Beggan, G. S. Richardson, N. Gomez-Perez, A. Collins, A. W. P. Thomson
Extreme space weather can damage ground-based infrastructure such as power lines, railways and gas pipelines through geomagnetically induced currents (GICs). Modeling GICs requires knowledge about the source magnetic field and the electrical conductivity structure of the Earth to calculate ground electric fields during enhanced geomagnetic activity. The electric field, in combination with detailed information about the power grid topology, enable the modeling of GICs in high-voltage (HV) power lines. Directly monitoring GICs in substations is possible with a Hall probe, but scarcely realized in the UK. Therefore we deployed the differential magnetometer method (DMM) to measure GICs at 12 sites in the UK power grid. The DMM includes the installation of two fluxgate magnetometers, one directly under a power line affected by GICs, and one as a remote site. The difference in recordings of the magnetic field at each instrument yields an estimate of the GICs in the respective power line segment via the Biot-Savart law. We collected data across the UK in 2018–2022, monitoring HV line segments where previous research indicated high GIC risk. We recorded magnetometer data during several smaller storms that allow detailed analysis of our GIC model. For the ground electric field computations we used recent magnetotelluric (MT) measurements recorded close to the DMM sites. Our results show that there is strong agreement in both amplitude and signal shape between measured and modeled line and substation GICs when using our HV model and the realistic electric field estimates derived from MT data.
{"title":"Validating a UK Geomagnetically Induced Current Model Using Differential Magnetometer Measurements","authors":"J. Hübert, C. D. Beggan, G. S. Richardson, N. Gomez-Perez, A. Collins, A. W. P. Thomson","doi":"10.1029/2023sw003769","DOIUrl":"https://doi.org/10.1029/2023sw003769","url":null,"abstract":"Extreme space weather can damage ground-based infrastructure such as power lines, railways and gas pipelines through geomagnetically induced currents (GICs). Modeling GICs requires knowledge about the source magnetic field and the electrical conductivity structure of the Earth to calculate ground electric fields during enhanced geomagnetic activity. The electric field, in combination with detailed information about the power grid topology, enable the modeling of GICs in high-voltage (HV) power lines. Directly monitoring GICs in substations is possible with a Hall probe, but scarcely realized in the UK. Therefore we deployed the differential magnetometer method (DMM) to measure GICs at 12 sites in the UK power grid. The DMM includes the installation of two fluxgate magnetometers, one directly under a power line affected by GICs, and one as a remote site. The difference in recordings of the magnetic field at each instrument yields an estimate of the GICs in the respective power line segment via the Biot-Savart law. We collected data across the UK in 2018–2022, monitoring HV line segments where previous research indicated high GIC risk. We recorded magnetometer data during several smaller storms that allow detailed analysis of our GIC model. For the ground electric field computations we used recent magnetotelluric (MT) measurements recorded close to the DMM sites. Our results show that there is strong agreement in both amplitude and signal shape between measured and modeled line and substation GICs when using our HV model and the realistic electric field estimates derived from MT data.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"176 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning is successful in many fields due to its ability to learn strong feature representations without the need for hand-crafted features, resulting in models with high representational power. However, many of these models are based on supervised learning and therefore depend on the availability of large annotated data sets. These are often difficult to obtain because they require human input. A common challenge for researchers in space weather is the sparsity of annotations in many of the available data sets, which are either unlabeled or have ambiguous labels. To alleviate the data bottleneck of loosely annotated data sets, unsupervised deep learning has become an important strategy, with anomaly detection being one of the most prominent applications. Unsupervised models have been successfully applied in various domains, such as medical imaging or video surveillance, to distinguish normal from abnormal data. In this work, we investigate how a purely unsupervised approach can be used to detect and extract solar phenomena in extreme ultraviolet images from the NASA SDO spacecraft. We show how a model based on variational autoencoders can be used to detect out-of-distribution samples and to localize regions of interest for solar activity. By using an unsupervised approach, we hope to contribute to space weather monitoring tools and further improve the understanding of space weather drivers.
深度学习之所以能在许多领域取得成功，是因为它能够学习强大的特征表征，而无需手工创建特征，从而产生具有高表征能力的模型。然而，这些模型中有许多是基于监督学习的，因此依赖于大量注释数据集的可用性。这些数据集通常很难获得，因为它们需要人工输入。空间天气研究人员面临的一个共同挑战是许多可用数据集的注释稀少，这些数据集要么没有标签，要么标签含糊不清。为了缓解松散注释数据集的数据瓶颈，无监督深度学习已成为一种重要策略，异常检测就是其中最突出的应用之一。无监督模型已成功应用于医疗成像或视频监控等多个领域，用于区分正常与异常数据。在这项工作中，我们研究了如何利用纯粹的无监督方法来检测和提取 NASA SDO 航天器极紫外图像中的太阳现象。我们展示了如何利用基于变异自动编码器的模型来检测异常分布样本，并定位太阳活动的相关区域。通过使用无监督方法，我们希望为空间天气监测工具做出贡献，并进一步提高对空间天气驱动因素的理解。
{"title":"Unsupervised Anomaly Detection With Variational Autoencoders Applied to Full-Disk Solar Images","authors":"Marius Giger, André Csillaghy","doi":"10.1029/2023sw003516","DOIUrl":"https://doi.org/10.1029/2023sw003516","url":null,"abstract":"Deep learning is successful in many fields due to its ability to learn strong feature representations without the need for hand-crafted features, resulting in models with high representational power. However, many of these models are based on supervised learning and therefore depend on the availability of large annotated data sets. These are often difficult to obtain because they require human input. A common challenge for researchers in space weather is the sparsity of annotations in many of the available data sets, which are either unlabeled or have ambiguous labels. To alleviate the data bottleneck of loosely annotated data sets, unsupervised deep learning has become an important strategy, with anomaly detection being one of the most prominent applications. Unsupervised models have been successfully applied in various domains, such as medical imaging or video surveillance, to distinguish normal from abnormal data. In this work, we investigate how a purely unsupervised approach can be used to detect and extract solar phenomena in extreme ultraviolet images from the NASA SDO spacecraft. We show how a model based on variational autoencoders can be used to detect out-of-distribution samples and to localize regions of interest for solar activity. By using an unsupervised approach, we hope to contribute to space weather monitoring tools and further improve the understanding of space weather drivers.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"21 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Minjing Li, Yue Deng, Brian J. Harding, Scott England
Large vertical shears in the E-region neutral zonal winds can lead to ion convergences and contribute to plasma irregularities, but climatological studies of vertical shears of horizontal winds in a global scale are lacking due to the limitations of data coverage. The Ionospheric Connection Explorer (ICON) Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) has provided neutral wind observations with an unprecedented spatial coverage. In this study, the climatology of dayside E-region neutral wind shears has been examined using 2-years’ data (2020–2021). Specifically, the study focuses on large wind shears with a magnitude larger than 20 m/s/km, since large wind shears are more likely to cause significant perturbation in the ionosphere-thermosphere (I-T) system. The results show that the probability of occurrence of large shears is strongly dependent on the altitude, with the vertical profile varying with shear direction, latitude, season, and local time. In general, below 110 km altitude, large negative shears of the eastward wind are most likely to happen during summer at 8–10 LT in 25°N–40°N latitudes, showing a high probability across nearly all longitudes. Meanwhile, large positive shears tend to occur in 10°S–10°N latitudes, with peak probabilities exhibiting roughly consistent longitudinal structures across 8–10 LT in all seasons. The discrepancies between positive and negative large shear distributions underlie different global tidal influences. The large-shear occurrence probabilities above 110 km are generally small, except in latitudes above 25°N during the winter for positive shears.
{"title":"Climatology of Dayside E-Region Zonal Neutral Wind Shears From ICON-MIGHTI Observations","authors":"Minjing Li, Yue Deng, Brian J. Harding, Scott England","doi":"10.1029/2023sw003670","DOIUrl":"https://doi.org/10.1029/2023sw003670","url":null,"abstract":"Large vertical shears in the E-region neutral zonal winds can lead to ion convergences and contribute to plasma irregularities, but climatological studies of vertical shears of horizontal winds in a global scale are lacking due to the limitations of data coverage. The Ionospheric Connection Explorer (ICON) Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) has provided neutral wind observations with an unprecedented spatial coverage. In this study, the climatology of dayside E-region neutral wind shears has been examined using 2-years’ data (2020–2021). Specifically, the study focuses on large wind shears with a magnitude larger than 20 m/s/km, since large wind shears are more likely to cause significant perturbation in the ionosphere-thermosphere (I-T) system. The results show that the probability of occurrence of large shears is strongly dependent on the altitude, with the vertical profile varying with shear direction, latitude, season, and local time. In general, below 110 km altitude, large negative shears of the eastward wind are most likely to happen during summer at 8–10 LT in 25<sup>°</sup>N–40<sup>°</sup>N latitudes, showing a high probability across nearly all longitudes. Meanwhile, large positive shears tend to occur in 10°S–10°N latitudes, with peak probabilities exhibiting roughly consistent longitudinal structures across 8–10 LT in all seasons. The discrepancies between positive and negative large shear distributions underlie different global tidal influences. The large-shear occurrence probabilities above 110 km are generally small, except in latitudes above 25<sup>°</sup>N during the winter for positive shears.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"23 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Laker, T. S. Horbury, H. O’Brien, E. J. Fauchon-Jones, V. Angelini, N. Fargette, T. Amerstorfer, M. Bauer, C. Möstl, E. E. Davies, J. A. Davies, R. Harrison, D. Barnes, M. Dumbović
Coronal mass ejections (CMEs) can create significant disruption to human activities and systems on Earth, much of which can be mitigated with prior warning of the upstream solar wind conditions. However, it is currently extremely challenging to accurately predict the arrival time and internal structure of a CME from coronagraph images alone. In this study, we take advantage of a rare opportunity to use Solar Orbiter, at 0.5 au upstream of Earth, as an upstream solar wind monitor. In combination with low-latency images from STEREO-A, we successfully predicted the arrival time of two CME events before they reached Earth. Measurements at Solar Orbiter were used to constrain an ensemble of simulation runs from the ELEvoHI model, reducing the uncertainty in arrival time from 10.4 to 2.5 hr in the first case study. There was also an excellent agreement in the Bz profile between Solar Orbiter and Wind spacecraft for the second case study, despite being separated by 0.5 au and 10° longitude. The opportunity to use Solar Orbiter as an upstream solar wind monitor will repeat once a year, which should further help assess the efficacy upstream in-situ measurements in real time space weather forecasting.
{"title":"Using Solar Orbiter as an Upstream Solar Wind Monitor for Real Time Space Weather Predictions","authors":"R. Laker, T. S. Horbury, H. O’Brien, E. J. Fauchon-Jones, V. Angelini, N. Fargette, T. Amerstorfer, M. Bauer, C. Möstl, E. E. Davies, J. A. Davies, R. Harrison, D. Barnes, M. Dumbović","doi":"10.1029/2023sw003628","DOIUrl":"https://doi.org/10.1029/2023sw003628","url":null,"abstract":"Coronal mass ejections (CMEs) can create significant disruption to human activities and systems on Earth, much of which can be mitigated with prior warning of the upstream solar wind conditions. However, it is currently extremely challenging to accurately predict the arrival time and internal structure of a CME from coronagraph images alone. In this study, we take advantage of a rare opportunity to use Solar Orbiter, at 0.5 au upstream of Earth, as an upstream solar wind monitor. In combination with low-latency images from STEREO-A, we successfully predicted the arrival time of two CME events before they reached Earth. Measurements at Solar Orbiter were used to constrain an ensemble of simulation runs from the ELEvoHI model, reducing the uncertainty in arrival time from 10.4 to 2.5 hr in the first case study. There was also an excellent agreement in the <i>B</i><sub><i>z</i></sub> profile between Solar Orbiter and Wind spacecraft for the second case study, despite being separated by 0.5 au and 10° longitude. The opportunity to use Solar Orbiter as an upstream solar wind monitor will repeat once a year, which should further help assess the efficacy upstream in-situ measurements in real time space weather forecasting.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"62 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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