Erroneous GNSS positioning, failures in spacecraft operations and power outages due to geomagnetically induced currents are severe threats originating from space weather. Knowing the potential impacts on modern society in advance is key for many end-user applications. This covers not only the timing of severe geomagnetic storms but also predictions of substorm onsets at polar latitudes. In this study, we aim at contributing to the timing problem of space weather impacts and propose a new method to predict the solar wind propagation delay between Lagrangian point L1 and the Earth based on machine learning, specifically decision tree models. The propagation delay is measured from the identification of interplanetary discontinuities detected by the advanced composition explorer (ACE) and their subsequent sudden commencements in the magnetosphere recorded by ground-based magnetometers. A database of the propagation delay has been constructed on this principle including 380 interplanetary shocks with data ranging from 1998 to 2018. The feature set of the machine learning approach consists of six features, namely the three components of each the solar wind speed and position of ACE around L1. The performance assessment of the machine learning model is examined based on of 10-fold cross-validation. The machine learning results are compared to physics-based models, i.e., the flat propagation delay and the more sophisticated method based on the normal vector of solar wind discontinuities (vector delay). After hyperparameter optimization, the trained gradient boosting (GB) model is the best machine learning model among the tested ones. The GB model achieves an RMSE of 4.5 min concerning the measured solar wind propagation delay and also outperforms the physical flat and vector delay models by 50% and 15% respectively. To increase the confidence in the predictions of the trained GB model, we perform a performance validation, provide drop-column feature importance and analyze the feature impact on the model output with Shapley values. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the solar wind speed and spacecraft position from only one datapoint have to be fed into the algorithm for a good prediction.
{"title":"Timing of the solar wind propagation delay between L1 and Earth based on machine learning","authors":"C. Baumann, A. McCloskey","doi":"10.1051/swsc/2021026","DOIUrl":"https://doi.org/10.1051/swsc/2021026","url":null,"abstract":"Erroneous GNSS positioning, failures in spacecraft operations and power outages due to geomagnetically induced currents are severe threats originating from space weather. Knowing the potential impacts on modern society in advance is key for many end-user applications. This covers not only the timing of severe geomagnetic storms but also predictions of substorm onsets at polar latitudes. In this study, we aim at contributing to the timing problem of space weather impacts and propose a new method to predict the solar wind propagation delay between Lagrangian point L1 and the Earth based on machine learning, specifically decision tree models. The propagation delay is measured from the identification of interplanetary discontinuities detected by the advanced composition explorer (ACE) and their subsequent sudden commencements in the magnetosphere recorded by ground-based magnetometers. A database of the propagation delay has been constructed on this principle including 380 interplanetary shocks with data ranging from 1998 to 2018. The feature set of the machine learning approach consists of six features, namely the three components of each the solar wind speed and position of ACE around L1. The performance assessment of the machine learning model is examined based on of 10-fold cross-validation. The machine learning results are compared to physics-based models, i.e., the flat propagation delay and the more sophisticated method based on the normal vector of solar wind discontinuities (vector delay). After hyperparameter optimization, the trained gradient boosting (GB) model is the best machine learning model among the tested ones. The GB model achieves an RMSE of 4.5 min concerning the measured solar wind propagation delay and also outperforms the physical flat and vector delay models by 50% and 15% respectively. To increase the confidence in the predictions of the trained GB model, we perform a performance validation, provide drop-column feature importance and analyze the feature impact on the model output with Shapley values. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the solar wind speed and spacecraft position from only one datapoint have to be fed into the algorithm for a good prediction.","PeriodicalId":17034,"journal":{"name":"Journal of Space Weather and Space Climate","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47510167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
R. Erdélyi, M. Korsós, Xin Huang, Yong Yang, D. Pizzey, S. Wrathmall, I. Hughes, M. Dyer, V. Dhillon, B. Belucz, R. Braǰsa, P. Chatterjee, Xuewu Cheng, Yuanyong Deng, Santiago Varga Dominguez, Raul Joya, P. Gömöry, N. Gyenge, A. Hanslmeier, A. Kucera, D. Kuridze, Faquan Li, Zhong Liu, Xu Long, M. Mathioudakis, S. Matthews, J. McAteer, A. Pevtsov, W. Pötzi, P. Romano, Jinhua Shen, Janos Temesvary, A. Tlatov, Charles Triana, D. Utz, A. Veronig, Yuming Wang, Yihua Yan, T. V. Zaqarashvili, F. Zuccarello
The Solar Activity Magnetic Monitor (SAMM) Network (SAMNet) is a future UK-led international network of ground-based solar telescope stations. SAMNet, at its full capacity, will continuously monitor the Sun’s intensity, magnetic and Doppler velocity fields at multiple heights in the solar atmosphere (from photosphere to upper chromosphere). Each SAMM sentinel will be equipped with a cluster of identical telescopes each with different magneto-optical filter (MOFs) to take observations in K~I, Na~D and Ca~I spectral bands. A subset of SAMM stations will have white-light coronagraphs and emission line coronal spectropolarimeters. The objectives of SAMNet are to provide observational data for the space weather research and forecast. The goal is to achieve an operationally sufficient lead time of e.g. flare warning of 2-8 hours, and provide much sought-after continuous synoptic maps (e.g., LoS magnetic and velocity fields, intensity) of the lower solar atmosphere with a spatial resolution limited only by seeing or diffraction limit, and with a cadence of 10 minutes. The individual SAMM sentinels will be connected into their master HQ hub where data received from all the slave stations will be automatically processed and flare warning issued up to 26 hrs in advance.
{"title":"The Solar Activity Monitor Network - SAMNet","authors":"R. Erdélyi, M. Korsós, Xin Huang, Yong Yang, D. Pizzey, S. Wrathmall, I. Hughes, M. Dyer, V. Dhillon, B. Belucz, R. Braǰsa, P. Chatterjee, Xuewu Cheng, Yuanyong Deng, Santiago Varga Dominguez, Raul Joya, P. Gömöry, N. Gyenge, A. Hanslmeier, A. Kucera, D. Kuridze, Faquan Li, Zhong Liu, Xu Long, M. Mathioudakis, S. Matthews, J. McAteer, A. Pevtsov, W. Pötzi, P. Romano, Jinhua Shen, Janos Temesvary, A. Tlatov, Charles Triana, D. Utz, A. Veronig, Yuming Wang, Yihua Yan, T. V. Zaqarashvili, F. Zuccarello","doi":"10.1051/SWSC/2021025","DOIUrl":"https://doi.org/10.1051/SWSC/2021025","url":null,"abstract":"The Solar Activity Magnetic Monitor (SAMM) Network (SAMNet) is a future UK-led international network of ground-based solar telescope stations. SAMNet, at its full capacity, will continuously monitor the Sun’s intensity, magnetic and Doppler velocity fields at multiple heights in the solar atmosphere (from photosphere to upper chromosphere). Each SAMM sentinel will be equipped with a cluster of identical telescopes each with different magneto-optical filter (MOFs) to take observations in K~I, Na~D and Ca~I spectral bands. A subset of SAMM stations will have white-light coronagraphs and emission line coronal spectropolarimeters. The objectives of SAMNet are to provide observational data for the space weather research and forecast. The goal is to achieve an operationally sufficient lead time of e.g. flare warning of 2-8 hours, and provide much sought-after continuous synoptic maps (e.g., LoS magnetic and velocity fields, intensity) of the lower solar atmosphere with a spatial resolution limited only by seeing or diffraction limit, and with a cadence of 10 minutes. The individual SAMM sentinels will be connected into their master HQ hub where data received from all the slave stations will be automatically processed and flare warning issued up to 26 hrs in advance.","PeriodicalId":17034,"journal":{"name":"Journal of Space Weather and Space Climate","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48046148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
A model forecasting ionospheric disturbances and its impact on GNSS positioning is proposed, called HAPEE (High lAtitude disturbances Positioning Error Estimator). It allows predicting ROTI index and corresponding Precise Point Positioning (PPP) error in Arctic region (i.e. latitudes > 50°). The model is forecasting for the next hour a probability of a disturbance index or PPP error to exceed a given threshold, from solar wind conditions measured at L1 Lagrange point. Or alternatively, it is forecasting a disturbance index level that is exceeded during the next hour for a given percentage of the time. The ROTI model has been derived from NMA network measurements, considering a database covering the years 2007 up to 2019. It is demonstrated that the statistical variability of the ROTI index is mainly following a lognormal distribution. The proposed model has been tested favorably on measurements performed using measurements from stations of the NMA network that were not used for the model derivation. It is also shown that the statistics of PPP error conditioned by ROTI is following a Laplace distribution. Then a new compound model has been proposed, based on a conditional probability combining ROTI distribution conditioned by solar wind conditions and error distributions conditioned by ROTI index level.
提出了一种预测电离层扰动及其对全球导航卫星系统定位影响的模型，称为HAPEE（High lAtitude distributions positioning Error Estimator）。它可以预测北极地区（即纬度>50°）的ROTI指数和相应的精确点定位（PPP）误差。该模型根据L1拉格朗日点测得的太阳风条件，预测未来一小时扰动指数或PPP误差超过给定阈值的概率。或者，它预测的是在给定百分比的时间内，在接下来的一个小时内超过的干扰指数水平。ROTI模型是根据NMA网络测量得出的，考虑到涵盖2007年至2019年的数据库。结果表明，ROTI指数的统计变异性主要遵循对数正态分布。所提出的模型已经在使用未用于模型推导的NMA网络的站点的测量进行的测量上得到了良好的测试。还表明，ROTI条件下的PPP误差的统计数据遵循拉普拉斯分布。然后，基于条件概率，将太阳风条件下的ROTI分布和ROTI指数水平条件下的误差分布相结合，提出了一种新的复合模型。
{"title":"GNSS positioning error forecasting in the Arctic: ROTI and Precise Point Positioning error forecasting from solar wind measurements","authors":"V. Fabbro, K. Jacobsen, Y. Andalsvik, S. Rougerie","doi":"10.1051/SWSC/2021024","DOIUrl":"https://doi.org/10.1051/SWSC/2021024","url":null,"abstract":"A model forecasting ionospheric disturbances and its impact on GNSS positioning is proposed, called HAPEE (High lAtitude disturbances Positioning Error Estimator). It allows predicting ROTI index and corresponding Precise Point Positioning (PPP) error in Arctic region (i.e. latitudes > 50°). The model is forecasting for the next hour a probability of a disturbance index or PPP error to exceed a given threshold, from solar wind conditions measured at L1 Lagrange point. Or alternatively, it is forecasting a disturbance index level that is exceeded during the next hour for a given percentage of the time. The ROTI model has been derived from NMA network measurements, considering a database covering the years 2007 up to 2019. It is demonstrated that the statistical variability of the ROTI index is mainly following a lognormal distribution. The proposed model has been tested favorably on measurements performed using measurements from stations of the NMA network that were not used for the model derivation. It is also shown that the statistics of PPP error conditioned by ROTI is following a Laplace distribution. Then a new compound model has been proposed, based on a conditional probability combining ROTI distribution conditioned by solar wind conditions and error distributions conditioned by ROTI index level.","PeriodicalId":17034,"journal":{"name":"Journal of Space Weather and Space Climate","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48870004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
T. Gombosi, Yuxi Chen, A. Glocer, Zhenguang Huang, X. Jia, M. Liemohn, W. Manchester, T. Pulkkinen, N. Sachdeva, Q. Al Shidi, I. Sokolov, J. Szente, V. Tenishev, G. Tóth, B. van der Holst, D. Welling, Lulu Zhao, S. Zou
Magnetohydrodynamics (MHD)-based global space weather models have mostly been developed and maintained at academic institutions. While the “free spirit” approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan, that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).
{"title":"What sustained multi-disciplinary research can achieve: The space weather modeling framework","authors":"T. Gombosi, Yuxi Chen, A. Glocer, Zhenguang Huang, X. Jia, M. Liemohn, W. Manchester, T. Pulkkinen, N. Sachdeva, Q. Al Shidi, I. Sokolov, J. Szente, V. Tenishev, G. Tóth, B. van der Holst, D. Welling, Lulu Zhao, S. Zou","doi":"10.1051/swsc/2021020","DOIUrl":"https://doi.org/10.1051/swsc/2021020","url":null,"abstract":"Magnetohydrodynamics (MHD)-based global space weather models have mostly been developed and maintained at academic institutions. While the “free spirit” approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan, that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).","PeriodicalId":17034,"journal":{"name":"Journal of Space Weather and Space Climate","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45037427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
M. Georgoulis, D. S. Bloomfield, M. Piana, A. Massone, M. Soldati, P. Gallagher, E. Pariat, N. Vilmer, É. Buchlin, F. Baudin, A. Csillaghy, H. Sathiapal, D. Jackson, P. Alingery, F. Benvenuto, C. Campi, K. Florios, Constantin Gontikakis, C. Guennou, J. A. Guerra, I. Kontogiannis, Vittorio Latorre, S. Murray, Sung-Hong Park, Samuel von Stachelski, Aleksandar Torbica, Dario Vischi, Mark Worsfold
The European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different machine learning techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple machine-learning algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels, at least for major flares. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic.
{"title":"The flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data & machine learning era","authors":"M. Georgoulis, D. S. Bloomfield, M. Piana, A. Massone, M. Soldati, P. Gallagher, E. Pariat, N. Vilmer, É. Buchlin, F. Baudin, A. Csillaghy, H. Sathiapal, D. Jackson, P. Alingery, F. Benvenuto, C. Campi, K. Florios, Constantin Gontikakis, C. Guennou, J. A. Guerra, I. Kontogiannis, Vittorio Latorre, S. Murray, Sung-Hong Park, Samuel von Stachelski, Aleksandar Torbica, Dario Vischi, Mark Worsfold","doi":"10.1051/SWSC/2021023","DOIUrl":"https://doi.org/10.1051/SWSC/2021023","url":null,"abstract":"The European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different machine learning techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple machine-learning algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels, at least for major flares. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic.","PeriodicalId":17034,"journal":{"name":"Journal of Space Weather and Space Climate","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45684294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
J. Yan, A. Lan, Xiang Deng, J. J. Zhang, Chi Wang, H. Qiu
{"title":"An agile high-frequency radar used for ionospheric research - Erratum","authors":"J. Yan, A. Lan, Xiang Deng, J. J. Zhang, Chi Wang, H. Qiu","doi":"10.1051/SWSC/2021017","DOIUrl":"https://doi.org/10.1051/SWSC/2021017","url":null,"abstract":"","PeriodicalId":17034,"journal":{"name":"Journal of Space Weather and Space Climate","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47409101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
M. Clilverd, C. Rodger, M. Freeman, J. Brundell, D. Manus, M. Dalzell, E. Clarke, A. Thomson, G. Richardson, Finlay Macleod, I. Frame
Measurements from six longitudinally separated magnetic observatories, all located close to the 53° mid-latitude contour, are analysed. We focus on the large geomagnetic disturbance that occurred during 7 and 8 September 2017. Combined with available geomagnetically induced current (GIC) data from two substations, each located near to a magnetic observatory, we investigate the magnetospheric drivers of the largest events. We analyse solar wind parameters combined with auroral electrojet indices to investigate the driving mechanisms. Six magnetic field disturbance events were observed at mid-latitudes with dH/dt > 60 nT/min. Co-located GIC measurements identified transformer currents >15 A during three of the events. The initial event was caused by a solar wind pressure pulse causing largest effects on the dayside, consistent with the rapid compression of the dayside geomagnetic field. Four of the events were caused by substorms. Variations in the Magnetic Local Time of the maximum effect of each substorm-driven event were apparent, with magnetic midnight, morning-side, and dusk-side events all occurring. The six events occurred over a period of almost 24 h, during which the solar wind remained elevated at >700 km s−1, indicating an extended time scale for potential GIC problems in electrical power networks following a sudden storm commencement. This work demonstrates the challenge of understanding the causes of ground-level magnetic field changes (and hence GIC magnitudes) for the global power industry. It also demonstrates the importance of magnetic local time and differing inner magnetospheric processes when considering the global hazard posed by GIC to power grids.
分析了六个纵向分离的磁观测站的测量结果，这些观测站都位于中纬度53°的等高线附近。我们关注2017年9月7日和8日发生的大地磁扰动。结合来自两个变电站的可用地磁感应电流（GIC）数据，我们研究了最大事件的磁层驱动因素。我们结合极光电喷流指数分析了太阳风参数，以研究驱动机制。在中纬度地区观测到6次dH/dt＞60nT/min的磁场扰动事件。位于同一地点的GIC测量发现，在其中三次事件中，变压器电流>15A。最初的事件是由太阳气压脉冲引起的，该脉冲对白天产生了最大的影响，与白天地磁场的快速压缩一致。其中四起事件是由亚暴引起的。每一次亚暴驱动事件的最大影响的磁本地时间变化是明显的，磁午夜、早晨和黄昏都发生了。这六起事件发生在近24小时的时间内，在此期间，太阳风保持在>700 km s−1的高度，这表明在风暴突然开始后，电网中潜在的GIC问题的时间范围延长。这项工作表明，理解全球电力行业地面磁场变化（以及GIC大小）的原因是一项挑战。它还证明了在考虑GIC对电网造成的全球危害时，磁局部时间和不同的内部磁层过程的重要性。
{"title":"Geomagnetically induced currents during the 07–08 September 2017 disturbed period: a global perspective","authors":"M. Clilverd, C. Rodger, M. Freeman, J. Brundell, D. Manus, M. Dalzell, E. Clarke, A. Thomson, G. Richardson, Finlay Macleod, I. Frame","doi":"10.1051/SWSC/2021014","DOIUrl":"https://doi.org/10.1051/SWSC/2021014","url":null,"abstract":"Measurements from six longitudinally separated magnetic observatories, all located close to the 53° mid-latitude contour, are analysed. We focus on the large geomagnetic disturbance that occurred during 7 and 8 September 2017. Combined with available geomagnetically induced current (GIC) data from two substations, each located near to a magnetic observatory, we investigate the magnetospheric drivers of the largest events. We analyse solar wind parameters combined with auroral electrojet indices to investigate the driving mechanisms. Six magnetic field disturbance events were observed at mid-latitudes with dH/dt > 60 nT/min. Co-located GIC measurements identified transformer currents >15 A during three of the events. The initial event was caused by a solar wind pressure pulse causing largest effects on the dayside, consistent with the rapid compression of the dayside geomagnetic field. Four of the events were caused by substorms. Variations in the Magnetic Local Time of the maximum effect of each substorm-driven event were apparent, with magnetic midnight, morning-side, and dusk-side events all occurring. The six events occurred over a period of almost 24 h, during which the solar wind remained elevated at >700 km s−1, indicating an extended time scale for potential GIC problems in electrical power networks following a sudden storm commencement. This work demonstrates the challenge of understanding the causes of ground-level magnetic field changes (and hence GIC magnitudes) for the global power industry. It also demonstrates the importance of magnetic local time and differing inner magnetospheric processes when considering the global hazard posed by GIC to power grids.","PeriodicalId":17034,"journal":{"name":"Journal of Space Weather and Space Climate","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43829562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
The interplanetary and magnetospheric phenomena time-coincident with intense geomagnetically induced current (GIC) > 10 A and > 30 A events during 21 years (1999 through 2019) at the Mäntsälä, Finland (57.9° magnetic latitude) gas pipeline have been studied. Although forward shocks and substorms are predominant causes of intense GICs, some newly discovered geoeffective interplanetary features are: solar wind plasma parcel (PP) impingements, possible interplanetary magnetic field (IMF) northward (Bn) and southward (Bs) turnings, and reverse shocks. The PPs are possibly the loop and filament portions of coronal mass ejections (CMEs). From a study of > 30 A GIC events, it is found that supersubstorm (SSS: SML < −2500 nT) and intense substorm (−2500 nT < SML < −2000 nT) auroral electrojet intensifications are the most frequent (76%) cause of all of these GIC events. These events occur most often (76%) in superstorm (SYM-H ≤ −250 nT) main phases, but they can occur in other storm phases and lesser intensity storms as well. After substorms, PPs were the most frequent causes of Mäntsälä GIC > 30 A events. Forward shocks were the third most frequent cause of the > 30 A events. Shock-related GICs were observed to occur at all local times. The two “Halloween” superstorms of 29–30 and 30–31 October 2003 produced by far the greatest number of GICs in the interval of study (9 > 30 A GICs and 168 > 10 A GICs). In the first Halloween superstorm, a shock-triggered SSS (SML < −3548 nT) caused 33, 57, 51 and 52 A GICs. The 57 A GIC was the most intense event of the superstorm and of this study. It is possible that this SSS is a new form of substorm. Equally intense magnetic storms were also studied but their related GICs were far less numerous and less intense.
{"title":"The Interplanetary and Magnetospheric causes of Geomagnetically Induced Currents (GICs) > 10 A in the Mäntsälä Finland Pipeline: 1999 through 2019","authors":"B. Tsurutani, R. Hajra","doi":"10.1051/SWSC/2021001","DOIUrl":"https://doi.org/10.1051/SWSC/2021001","url":null,"abstract":"The interplanetary and magnetospheric phenomena time-coincident with intense geomagnetically induced current (GIC) > 10 A and > 30 A events during 21 years (1999 through 2019) at the Mäntsälä, Finland (57.9° magnetic latitude) gas pipeline have been studied. Although forward shocks and substorms are predominant causes of intense GICs, some newly discovered geoeffective interplanetary features are: solar wind plasma parcel (PP) impingements, possible interplanetary magnetic field (IMF) northward (Bn) and southward (Bs) turnings, and reverse shocks. The PPs are possibly the loop and filament portions of coronal mass ejections (CMEs).\u0000From a study of > 30 A GIC events, it is found that supersubstorm (SSS: SML < −2500 nT) and intense substorm (−2500 nT < SML < −2000 nT) auroral electrojet intensifications are the most frequent (76%) cause of all of these GIC events. These events occur most often (76%) in superstorm (SYM-H ≤ −250 nT) main phases, but they can occur in other storm phases and lesser intensity storms as well. After substorms, PPs were the most frequent causes of Mäntsälä GIC > 30 A events. Forward shocks were the third most frequent cause of the > 30 A events. Shock-related GICs were observed to occur at all local times.\u0000The two “Halloween” superstorms of 29–30 and 30–31 October 2003 produced by far the greatest number of GICs in the interval of study (9 > 30 A GICs and 168 > 10 A GICs). In the first Halloween superstorm, a shock-triggered SSS (SML < −3548 nT) caused 33, 57, 51 and 52 A GICs. The 57 A GIC was the most intense event of the superstorm and of this study. It is possible that this SSS is a new form of substorm. Equally intense magnetic storms were also studied but their related GICs were far less numerous and less intense.","PeriodicalId":17034,"journal":{"name":"Journal of Space Weather and Space Climate","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47526915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
The focus is on the physical background and comprehension of the origin and the heliospheric propagation of interplanetary coronal mass ejections (ICMEs), which can cause most severe geomagnetic disturbances. The paper considers mainly the analytical modelling, providing useful insight into the nature of ICMEs, complementary to that provided by numerical MHD models. It is concentrated on physical processes related to the origin of CMEs at the Sun, their heliospheric propagation, up to the effects causing geomagnetic perturbations. Finally, several analytical and statistical forecasting tools for space weather applications are described.
{"title":"Analytical and empirical modelling of the origin and heliospheric propagation of coronal mass ejections, and space weather applications","authors":"B. Vršnak","doi":"10.1051/SWSC/2021012","DOIUrl":"https://doi.org/10.1051/SWSC/2021012","url":null,"abstract":"The focus is on the physical background and comprehension of the origin and the heliospheric propagation of interplanetary coronal mass ejections (ICMEs), which can cause most severe geomagnetic disturbances. The paper considers mainly the analytical modelling, providing useful insight into the nature of ICMEs, complementary to that provided by numerical MHD models. It is concentrated on physical processes related to the origin of CMEs at the Sun, their heliospheric propagation, up to the effects causing geomagnetic perturbations. Finally, several analytical and statistical forecasting tools for space weather applications are described.","PeriodicalId":17034,"journal":{"name":"Journal of Space Weather and Space Climate","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42501092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Newly discovered descriptions about the great aurora observed in March 1582 are presented in this work. These records were made by Portuguese observers from Lisbon. Both records described the aurora like a great fire in the northern part of the sky. It was observed during three consecutive nights, according to one of the sources. Thus, we present a discussion of these auroral records in order to complement other works that studied the aurora sighted in March 1582.
{"title":"Portuguese eyewitness accounts of the great space weather event of 1582","authors":"Víctor Manuel Sánchez Carrasco, J. Vaquero","doi":"10.1051/swsc/2020005","DOIUrl":"https://doi.org/10.1051/swsc/2020005","url":null,"abstract":"Newly discovered descriptions about the great aurora observed in March 1582 are presented in this work. These records were made by Portuguese observers from Lisbon. Both records described the aurora like a great fire in the northern part of the sky. It was observed during three consecutive nights, according to one of the sources. Thus, we present a discussion of these auroral records in order to complement other works that studied the aurora sighted in March 1582.","PeriodicalId":17034,"journal":{"name":"Journal of Space Weather and Space Climate","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1051/swsc/2020005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47093072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
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