Kun Wu, Liying Qian, Wenbin Wang, Xuguang Cai, Joseph M. Mclnerney
We investigate in detail the occurrence and evolution of ionospheric equatorial plasma bubbles (EPBs) during a moderate storm on 17 September 2021, using Global-scale Observations of the Limb and Disk (GOLD) observations and Whole Atmosphere Community Climate Model-eXtended (WACCM-X) simulations. GOLD observations show that there were no EPBs on 16 September before the storm but EPBs occurred after the storm commencement on 17 September. The EPBs extended to ∼30° magnetic latitude. A diagnostic analysis of WACCM-X simulations reveals that the rapid enhancement of prompt penetration electric fields (PPEFs) after the sudden storm commencement is the main reason that triggered the occurrence of the EPBs. Further quantitative analysis shows that vertical plasma drifts, which are enhanced by the PPEF, played a dominant role in strengthening the Rayleigh-Taylor instability, leading to the occurrence of the EPBs and the large latitudinal extension of the EPBs to ∼30° magnetic latitude during the night of 17 September.
{"title":"Investigation of the Physical Mechanisms of the Formation and Evolution of Equatorial Plasma Bubbles During a Moderate Storm on 17 September 2021","authors":"Kun Wu, Liying Qian, Wenbin Wang, Xuguang Cai, Joseph M. Mclnerney","doi":"10.1029/2023sw003673","DOIUrl":"https://doi.org/10.1029/2023sw003673","url":null,"abstract":"We investigate in detail the occurrence and evolution of ionospheric equatorial plasma bubbles (EPBs) during a moderate storm on 17 September 2021, using Global-scale Observations of the Limb and Disk (GOLD) observations and Whole Atmosphere Community Climate Model-eXtended (WACCM-X) simulations. GOLD observations show that there were no EPBs on 16 September before the storm but EPBs occurred after the storm commencement on 17 September. The EPBs extended to ∼30° magnetic latitude. A diagnostic analysis of WACCM-X simulations reveals that the rapid enhancement of prompt penetration electric fields (PPEFs) after the sudden storm commencement is the main reason that triggered the occurrence of the EPBs. Further quantitative analysis shows that vertical plasma drifts, which are enhanced by the PPEF, played a dominant role in strengthening the Rayleigh-Taylor instability, leading to the occurrence of the EPBs and the large latitudinal extension of the EPBs to ∼30° magnetic latitude during the night of 17 September.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"27 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138716062","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}
A. T. Chartier, J. Steele, G. Sugar, D. R. Themens, S. K. Vines, J. D. Huba
New, open access tools have been developed to validate ionospheric models in terms of technologically relevant metrics. These are ionospheric errors on GPS 3D position, HF ham radio communications, and peak F-region density. To demonstrate these tools, we have used output from Sami is Another Model of the Ionosphere (SAMI3) driven by high-latitude electric potentials derived from Active Magnetosphere and Planetary Electrodynamics Response Experiment, covering the first available month of operation using Iridium-NEXT data (March 2019). Output of this model is now available for visualization and download via https://sami3.jhuapl.edu. The GPS test indicates SAMI3 reduces ionospheric errors on 3D position solutions from 1.9 m with no model to 1.6 m on average (maximum error: 14.2 m without correction, 13.9 m with correction). SAMI3 predicts 55.5% of reported amateur radio links between 2–30 MHz and 500–2,000 km. Autoscaled and then machine learning “cleaned” Digisonde NmF2 data indicate a 1.0 × 1011 el. m3 median positive bias in SAMI3 (equivalent to a 27% overestimation). The positive NmF2 bias is largest during the daytime, which may explain the relatively good performance in predicting HF links then. The underlying data sources and software used here are publicly available, so that interested groups may apply these tests to other models and time intervals.
已开发出新的开放式工具，用于根据技术相关指标验证电离层模型。这些指标是电离层对全球定位系统三维定位、高频火腿无线电通信和峰值 F 区密度的误差。为了演示这些工具，我们使用了萨米是电离层的另一个模型（SAMI3）的输出，该模型由主动磁层和行星电动力学响应实验得出的高纬度电势驱动，覆盖了使用铱星-NEXT数据运行的第一个可用月份（2019年3月）。该模型的输出现在可通过 https://sami3.jhuapl.edu 进行可视化和下载。GPS 测试表明，SAMI3 将电离层对 3D 定位解决方案的误差从无模型时的 1.9 米减少到平均 1.6 米（最大误差：无修正时 14.2 米，有修正时 13.9 米）。SAMI3 预测了 55.5% 报告的 2-30 MHz 和 500-2,000 km 之间的业余无线电链路。经过自动缩放和机器学习 "净化 "的 Digisonde NmF2 数据表明，SAMI3 的正偏差中值为 1.0 × 1011 el. m3（相当于高估 27%）。NmF2 的正偏差在白天最大，这可能是白天预测高频链路性能相对较好的原因。这里使用的基础数据源和软件都是公开的，因此有兴趣的团体可以将这些测试应用于其他模型和时间间隔。
{"title":"Validating Ionospheric Models Against Technologically Relevant Metrics","authors":"A. T. Chartier, J. Steele, G. Sugar, D. R. Themens, S. K. Vines, J. D. Huba","doi":"10.1029/2023sw003590","DOIUrl":"https://doi.org/10.1029/2023sw003590","url":null,"abstract":"New, open access tools have been developed to validate ionospheric models in terms of technologically relevant metrics. These are ionospheric errors on GPS 3D position, HF ham radio communications, and peak F-region density. To demonstrate these tools, we have used output from Sami is Another Model of the Ionosphere (SAMI3) driven by high-latitude electric potentials derived from Active Magnetosphere and Planetary Electrodynamics Response Experiment, covering the first available month of operation using Iridium-NEXT data (March 2019). Output of this model is now available for visualization and download via https://sami3.jhuapl.edu. The GPS test indicates SAMI3 reduces ionospheric errors on 3D position solutions from 1.9 m with no model to 1.6 m on average (maximum error: 14.2 m without correction, 13.9 m with correction). SAMI3 predicts 55.5% of reported amateur radio links between 2–30 MHz and 500–2,000 km. Autoscaled and then machine learning “cleaned” Digisonde NmF2 data indicate a 1.0 × 10<sup>11</sup> el. m<sup>3</sup> median positive bias in SAMI3 (equivalent to a 27% overestimation). The positive NmF2 bias is largest during the daytime, which may explain the relatively good performance in predicting HF links then. The underlying data sources and software used here are publicly available, so that interested groups may apply these tests to other models and time intervals.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"37 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138715972","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}
Atilim Guneş Baydin, Bala Poduval, Nathan A. Schwadron
The high energy particles originating from the Sun, known as solar energetic particles (SEPs), contribute significantly to the space radiation environment, posing serious threats to astronauts and scientific instruments on board spacecraft. The mechanism that accelerates the SEPs to the observed energy ranges, their transport in the inner heliosphere, and the influence of suprathermal seed particle spectrum are open questions in heliophysics. Accurate predictions of the occurrences of SEP events well in advance are necessary to mitigate their adverse effects but prediction based on first principle models still remains a challenge. In this scenario, adopting a machine learning approach to SEP modeling and prediction is desirable. However, the lack of a balanced database of SEP events restrains this approach. We addressed this limitation by generating large data sets of synthetic SEP events sampled from the physics-based model, Energetic Particle Radiation Environment Module (EPREM). Using this data, we developed neural networks-based surrogate models to study the seed population parameter space. Our models, EPREM-S, run thousands to millions of times faster (depending on computer hardware), making simulation-based inference workflows practicable in SEP studies while providing predictive uncertainty estimates using a deep ensemble approach.
{"title":"A Surrogate Model for Studying Solar Energetic Particle Transport and the Seed Population","authors":"Atilim Guneş Baydin, Bala Poduval, Nathan A. Schwadron","doi":"10.1029/2023sw003593","DOIUrl":"https://doi.org/10.1029/2023sw003593","url":null,"abstract":"The high energy particles originating from the Sun, known as solar energetic particles (SEPs), contribute significantly to the space radiation environment, posing serious threats to astronauts and scientific instruments on board spacecraft. The mechanism that accelerates the SEPs to the observed energy ranges, their transport in the inner heliosphere, and the influence of suprathermal seed particle spectrum are open questions in heliophysics. Accurate predictions of the occurrences of SEP events well in advance are necessary to mitigate their adverse effects but prediction based on first principle models still remains a challenge. In this scenario, adopting a machine learning approach to SEP modeling and prediction is desirable. However, the lack of a balanced database of SEP events restrains this approach. We addressed this limitation by generating large data sets of synthetic SEP events sampled from the physics-based model, Energetic Particle Radiation Environment Module (EPREM). Using this data, we developed neural networks-based surrogate models to study the seed population parameter space. Our models, EPREM-S, run thousands to millions of times faster (depending on computer hardware), making simulation-based inference workflows practicable in SEP studies while providing predictive uncertainty estimates using a deep ensemble approach.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"286 1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138629176","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}
Kirolosse M. Girgis, Tohru Hada, Akimasa Yoshikawa, Shuichi Matsukiyo, Viviane Pierrard, Susan W. Samwel
During a few solar energetic particle (SEP) events, solar protons were trapped within the geomagnetic field and reached the outer edge of the inner radiation belt. We reproduced this phenomenon by modeling the proton flux distribution at the Low-Earth Orbit (LEO) for different geomagnetic conditions during solar particle events. We developed a three-dimensional relativistic test particle simulation code to compute the 70–180 MeV solar proton Lorentz trajectories in low L-shell range from 1 to 3. The Tsyganenko model (T01) generated the background static magnetic field with the IGRF (v12) model. We have selected three Dst index values: −7, −150, and −210 nT, to define quiet time, strong, and severe geomagnetic storms and to generate the corresponding inner magnetic field configurations. Our results showed that the simulated solar proton flux was more enhanced in the high-latitude regions and more expanded toward the lower latitude range as long as the geomagnetic storm was intensified. Satellite observations and geomagnetic cutoff rigidities confirmed the numerical results. Furthermore, the LEO proton flux distribution was deformed, so the structure of the proton flux inside the South Atlantic Anomaly (SAA) became longitudinally extended as the Dst index decreased. Moreover, we have assessed the corresponding radiation environment of the LEO mission. We realized that, for a higher inclined LEO mission during an intense geomagnetic storm (Dst = −210 nT), the probability of the occurrence of the Single Event Upset (SEU) rates increased by 19% and the estimated accumulated absorbed radiation doses increased by 17% in comparison with quiet conditions.
{"title":"Geomagnetic Storm Effects on the LEO Proton Flux During Solar Energetic Particle Events","authors":"Kirolosse M. Girgis, Tohru Hada, Akimasa Yoshikawa, Shuichi Matsukiyo, Viviane Pierrard, Susan W. Samwel","doi":"10.1029/2023sw003664","DOIUrl":"https://doi.org/10.1029/2023sw003664","url":null,"abstract":"During a few solar energetic particle (SEP) events, solar protons were trapped within the geomagnetic field and reached the outer edge of the inner radiation belt. We reproduced this phenomenon by modeling the proton flux distribution at the Low-Earth Orbit (LEO) for different geomagnetic conditions during solar particle events. We developed a three-dimensional relativistic test particle simulation code to compute the 70–180 MeV solar proton Lorentz trajectories in low <i>L</i>-shell range from 1 to 3. The Tsyganenko model (T01) generated the background static magnetic field with the IGRF (v12) model. We have selected three <i>Dst</i> index values: −7, −150, and −210 nT, to define quiet time, strong, and severe geomagnetic storms and to generate the corresponding inner magnetic field configurations. Our results showed that the simulated solar proton flux was more enhanced in the high-latitude regions and more expanded toward the lower latitude range as long as the geomagnetic storm was intensified. Satellite observations and geomagnetic cutoff rigidities confirmed the numerical results. Furthermore, the LEO proton flux distribution was deformed, so the structure of the proton flux inside the South Atlantic Anomaly (SAA) became longitudinally extended as the <i>Dst</i> index decreased. Moreover, we have assessed the corresponding radiation environment of the LEO mission. We realized that, for a higher inclined LEO mission during an intense geomagnetic storm (<i>Dst</i> = −210 nT), the probability of the occurrence of the Single Event Upset (SEU) rates increased by 19% and the estimated accumulated absorbed radiation doses increased by 17% in comparison with quiet conditions.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"67 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138574465","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}
The majority of studies into space weather impacts on ground-based systems focus on power supply networks and oil and gas pipelines. The effects on railway signaling infrastructure remain a sparsely covered aspect even though these systems are known to have experienced adverse effects in the past as a result of geomagnetic activity. This study extends recent modeling of geomagnetic effects on DC signaling for AC-electrified railways in the UK that analyzed “right side” failures in which green signals are turned to red. The extended model reported here allows the study of “wrong side” failures where red signals are turned green: a failure mode that is potentially more dangerous. Railway lines using track circuit signaling, like those modeled in this study, are separated into a number of individual blocks. This study shows that a relay is most susceptible to “wrong side” failure when a train is at the end of a track circuit block. Assuming that each train is positioned at the end of the block it is occupying, the results show that the geoelectric field threshold at which “wrong side” failures can occur is lower than for “right side” failures. This misoperation field level occurs on a timescale of once every 10 or 20 years. We also show that the estimated electric field caused by a 1-in-100 years event could cause a significant number of “wrong side” failures at multiple points along the railway lines studied, although this depends on the number of trains on the line at that time.
{"title":"Modeling “Wrong Side” Failures Caused by Geomagnetically Induced Currents in Electrified Railway Signaling Systems in the UK","authors":"C. J. Patterson, J. A. Wild, D. H. Boteler","doi":"10.1029/2023sw003625","DOIUrl":"https://doi.org/10.1029/2023sw003625","url":null,"abstract":"The majority of studies into space weather impacts on ground-based systems focus on power supply networks and oil and gas pipelines. The effects on railway signaling infrastructure remain a sparsely covered aspect even though these systems are known to have experienced adverse effects in the past as a result of geomagnetic activity. This study extends recent modeling of geomagnetic effects on DC signaling for AC-electrified railways in the UK that analyzed “right side” failures in which green signals are turned to red. The extended model reported here allows the study of “wrong side” failures where red signals are turned green: a failure mode that is potentially more dangerous. Railway lines using track circuit signaling, like those modeled in this study, are separated into a number of individual blocks. This study shows that a relay is most susceptible to “wrong side” failure when a train is at the end of a track circuit block. Assuming that each train is positioned at the end of the block it is occupying, the results show that the geoelectric field threshold at which “wrong side” failures can occur is lower than for “right side” failures. This misoperation field level occurs on a timescale of once every 10 or 20 years. We also show that the estimated electric field caused by a 1-in-100 years event could cause a significant number of “wrong side” failures at multiple points along the railway lines studied, although this depends on the number of trains on the line at that time.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"21 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138569678","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}
A. B. Andreyev, S. N. Mukasheva, V. I. Kapytin, O. I. Sokolova
Extreme solar events, such as powerful solar flares are accompanied by the release of strong solar disturbances, such as coronal mass ejections (CMEs). The impact of CMEs on the Earth's magnetosphere causes geomagnetic storms, which trigger geomagnetic effects measurable in the ionosphere, upper atmosphere, and on and in the ground. During extreme cases, rapidly changing geomagnetic fields generate intense geomagnetically induced currents (GICs), which can cause dramatic effects on man-made technological systems, including transmission lines and pipelines. In countries with large territories such as Kazakhstan, long power lines contribute to high values of induced currents during periods of extreme geoeffective solar events. It is of interest to estimate the values of GICs in an extensive network of power lines on the territory of Kazakhstan. However, there are no estimations of induced currents in power lines in Kazakhstan, and most estimation techniques are made difficult because of absence of field measurements of Earth conductivity. This study aims to model geoelectric fields on the surface of the Earth for Kazakhstan and to estimate the values of the GICs in 500 kV power lines. This study also compares between two methods for calculating induced voltages in power lines: one based on linear paths and the other based on curvilinear paths between substations of transmission power lines.
{"title":"Estimating Geomagnetically Induced Currents in High-Voltage Power Lines for the Territory of Kazakhstan","authors":"A. B. Andreyev, S. N. Mukasheva, V. I. Kapytin, O. I. Sokolova","doi":"10.1029/2023sw003639","DOIUrl":"https://doi.org/10.1029/2023sw003639","url":null,"abstract":"Extreme solar events, such as powerful solar flares are accompanied by the release of strong solar disturbances, such as coronal mass ejections (CMEs). The impact of CMEs on the Earth's magnetosphere causes geomagnetic storms, which trigger geomagnetic effects measurable in the ionosphere, upper atmosphere, and on and in the ground. During extreme cases, rapidly changing geomagnetic fields generate intense geomagnetically induced currents (GICs), which can cause dramatic effects on man-made technological systems, including transmission lines and pipelines. In countries with large territories such as Kazakhstan, long power lines contribute to high values of induced currents during periods of extreme geoeffective solar events. It is of interest to estimate the values of GICs in an extensive network of power lines on the territory of Kazakhstan. However, there are no estimations of induced currents in power lines in Kazakhstan, and most estimation techniques are made difficult because of absence of field measurements of Earth conductivity. This study aims to model geoelectric fields on the surface of the Earth for Kazakhstan and to estimate the values of the GICs in 500 kV power lines. This study also compares between two methods for calculating induced voltages in power lines: one based on linear paths and the other based on curvilinear paths between substations of transmission power lines.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138562114","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}
Nicholas Ssessanga, Wojciech Jacek Miloch, Lasse Boy Novock Clausen, Daria Kotova
Data assimilation (DA) techniques have recently gained traction in the ionospheric community, particularly at regional operational centers where more precise data are becoming prevalent. At center stage is the argument over which technique or scheme merits realization. At 4DSpace, we have in-house developed and assessed the performance of two regional flavors of short-term forecast strong constraint four-dimensional (4D, space and time) variational (SC4DVar) DA schemes; the orthodox incremental (SC4DVar-Inc) and ensemble-based (SC4DEnVar) approach. SC4DVar-Inc is bottled-necked by expensive Tangent Linear Models (TLMs) and model Ad-joints (MAs), while SC4DEnVar design mitigates these limitations. Both schemes initialize from the same background (IRI-2016), and electron densities forward propagated (30-min) by a Gauss Markov filter- the densities take on a log-normal distribution to assert the mandatory ionosphere density positive definiteness. Preliminary assimilation is performed only with ubiquitous Global Navigation Satellite System observables from ground-based receivers, with a focus on moderately stable mid-latitudes, specifically the Japanese archipelago and neighboring areas. Using a simulation analysis, we find that under model space localization, 30 member Ensembles are sufficient for regional SC4DEnVar. Verification of reconstructions is with independent observations from ground-based ionosonde and satellite radio occultations: the performance of both schemes is fairly adequate during the quiet period when the background has a better estimation of the hmF2. SC4DVar-Inc is slightly better over areas densely populated with measurements, but SC4DEnVar estimates the overall 3D ionosphere picture better, particularly in remote areas and during severe conditions. These results warrant SC4DEnVar as a better candidate for precise short-time regional forecasts.
{"title":"Performance Analysis of a Strong Constraint 4DVar and 4DEnVar on Regional Ionosphere Imaging","authors":"Nicholas Ssessanga, Wojciech Jacek Miloch, Lasse Boy Novock Clausen, Daria Kotova","doi":"10.1029/2023sw003584","DOIUrl":"https://doi.org/10.1029/2023sw003584","url":null,"abstract":"Data assimilation (DA) techniques have recently gained traction in the ionospheric community, particularly at regional operational centers where more precise data are becoming prevalent. At center stage is the argument over which technique or scheme merits realization. At 4DSpace, we have in-house developed and assessed the performance of two regional flavors of short-term forecast strong constraint four-dimensional (4D, space and time) variational (SC4DVar) DA schemes; the orthodox incremental (SC4DVar-Inc) and ensemble-based (SC4DEnVar) approach. SC4DVar-Inc is bottled-necked by expensive Tangent Linear Models (TLMs) and model Ad-joints (MAs), while SC4DEnVar design mitigates these limitations. Both schemes initialize from the same background (IRI-2016), and electron densities forward propagated (30-min) by a Gauss Markov filter- the densities take on a log-normal distribution to assert the mandatory ionosphere density positive definiteness. Preliminary assimilation is performed only with ubiquitous Global Navigation Satellite System observables from ground-based receivers, with a focus on moderately stable mid-latitudes, specifically the Japanese archipelago and neighboring areas. Using a simulation analysis, we find that under model space localization, 30 member Ensembles are sufficient for regional SC4DEnVar. Verification of reconstructions is with independent observations from ground-based ionosonde and satellite radio occultations: the performance of both schemes is fairly adequate during the quiet period when the background has a better estimation of the hmF2. SC4DVar-Inc is slightly better over areas densely populated with measurements, but SC4DEnVar estimates the overall 3D ionosphere picture better, particularly in remote areas and during severe conditions. These results warrant SC4DEnVar as a better candidate for precise short-time regional forecasts.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"40 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138566285","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}
Chigomezyo M. Ngwira, Robert Arritt, Charles Perry, James M. Weygand, Rishi Sharma
Space weather, a natural hazard, can adversely impact human technological assets. High-voltage electric power transmission grids constitute one of the most critical technological systems vulnerable to space weather driven geomagnetically induced currents (GICs). One of the major challenges pertaining to the study of GICs over the continental United States has been the availability of GIC measurements, which are critical for validation of geoelectric field and power flow models, for example. In this study, we analyze GIC measurements collected at 17 Electrical Power Research Institute (EPRI) SUNBURST transformer locations across the United States for which a GIC value of 10 A or greater was recorded. This data set includes 52 individual geomagnetic storms with Kp index 6 and above during the period from 2010 to 2021. The analysis confirms that there is a good correlation between the number of geomagnetic storms per year and the number of recorded GIC events. Our results also show that about 76% of the top 17 GIC events are associated with the storm main phase, while only 24% are attributed to storm sudden commencements. In addition, it is shown, for the first time, that mid-latitude positive bays can cause large GICs over the continental United States. Finally, this study shows that the largest measured GIC event in the data set was associated with a localized intense dB/dt structure, which could be attributed to substorm activity.
{"title":"Occurrence of Large Geomagnetically Induced Currents Within the EPRI SUNBURST Monitoring Network","authors":"Chigomezyo M. Ngwira, Robert Arritt, Charles Perry, James M. Weygand, Rishi Sharma","doi":"10.1029/2023sw003532","DOIUrl":"https://doi.org/10.1029/2023sw003532","url":null,"abstract":"Space weather, a natural hazard, can adversely impact human technological assets. High-voltage electric power transmission grids constitute one of the most critical technological systems vulnerable to space weather driven geomagnetically induced currents (GICs). One of the major challenges pertaining to the study of GICs over the continental United States has been the availability of GIC measurements, which are critical for validation of geoelectric field and power flow models, for example. In this study, we analyze GIC measurements collected at 17 Electrical Power Research Institute (EPRI) SUNBURST transformer locations across the United States for which a GIC value of 10 A or greater was recorded. This data set includes 52 individual geomagnetic storms with Kp index 6 and above during the period from 2010 to 2021. The analysis confirms that there is a good correlation between the number of geomagnetic storms per year and the number of recorded GIC events. Our results also show that about 76% of the top 17 GIC events are associated with the storm main phase, while only 24% are attributed to storm sudden commencements. In addition, it is shown, for the first time, that mid-latitude positive bays can cause large GICs over the continental United States. Finally, this study shows that the largest measured GIC event in the data set was associated with a localized intense dB/dt structure, which could be attributed to substorm activity.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138562270","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}
Accurate modeling of the total electron content (TEC) benefits scientific research and practical application. In this study, the global ionospheric maps from the Center for Orbit Determination of Europe (CODE) covering the years 2000–2021 are utilized to develop an empirical model of TEC by superposing the tide-like components in the ionosphere. The tide-like components, including the migrating and non-migrating ones, are first derived from the daily CODE TEC data. Then, the sine and cosine components of a tide-like signature are separately decomposed into the basic modes as a function of the modified inclination latitude with the principle component analysis, and the temporal evolution is regressed to the solar radiation dependence and interannual variation. As such, the climatological behavior of tidal amplitudes and phases could be well parameterized, and the developed model is capable of reproducing the global TEC patterns. The modeled TEC agrees well with the CODE input data with zero systematic error and a low root mean square error of 3.849 TECu, demonstrating a good model performance. This developed model, associated with the parametric tide-like signatures, could serve as a background for future investigations of the ionospheric responses to the forcing from below or above.
{"title":"An Empirical Model of Ionospheric Total Electron Content Based on Tide-Like Signature Modeling","authors":"Haibing Ruan, Jiuhou Lei, Jianyong Lu, Fen Tang","doi":"10.1029/2023sw003564","DOIUrl":"https://doi.org/10.1029/2023sw003564","url":null,"abstract":"Accurate modeling of the total electron content (TEC) benefits scientific research and practical application. In this study, the global ionospheric maps from the Center for Orbit Determination of Europe (CODE) covering the years 2000–2021 are utilized to develop an empirical model of TEC by superposing the tide-like components in the ionosphere. The tide-like components, including the migrating and non-migrating ones, are first derived from the daily CODE TEC data. Then, the sine and cosine components of a tide-like signature are separately decomposed into the basic modes as a function of the modified inclination latitude with the principle component analysis, and the temporal evolution is regressed to the solar radiation dependence and interannual variation. As such, the climatological behavior of tidal amplitudes and phases could be well parameterized, and the developed model is capable of reproducing the global TEC patterns. The modeled TEC agrees well with the CODE input data with zero systematic error and a low root mean square error of 3.849 TECu, demonstrating a good model performance. This developed model, associated with the parametric tide-like signatures, could serve as a background for future investigations of the ionospheric responses to the forcing from below or above.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"29 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138561752","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 paper first applies a prediction model based on self-attention memory ConvLSTM (SAM-ConvLSTM) to predict the global ionospheric total electron content (TEC) maps with up to 1 day of lead time. We choose the global ionospheric TEC maps released by the Center for Orbit Determination in Europe (CODE) as the training data set covering the period from 1999 to 2022. Besides that, we put several space environment data as additional multivariate-features into the framework of the prediction model to enhance its forecasting ability. In order to confirm the efficiency of the proposed model, the other two prediction models based on convolutional long short-term memory (LSTM) are used for comparison. The three models are trained and evaluated on the same data set. Results show that the proposed SAM-ConvLSTM prediction model performs more accurately than the other two models, and more stably under space weather events. In order to assess the generalization capabilities of the proposed model amidst severe space weather occurrences, we selected the period of 22–25 April 2023, characterized by a potent geomagnetic storm, for experimental validation. Subsequently, we employed the 1-day predicted global TEC products from the Center for Operational Products and Services (COPG) and the SAM-ConvLSTM model to evaluate their respective forecasting prowess. The results show that the SAM-ConvLSTM prediction model achieves lower prediction error. In one word, the ionospheric TEC prediction model proposed in this paper can establish the ionosphere TEC of spatio-temporal data association for a long time, and realize high precision of prediction performance.
{"title":"Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM-ConvLSTM Model","authors":"Hanze Luo, Yingkui Gong, Si Chen, Cheng Yu, Guang Yang, Fengzheng Yu, Ziyue Hu, Xiangwei Tian","doi":"10.1029/2023sw003707","DOIUrl":"https://doi.org/10.1029/2023sw003707","url":null,"abstract":"This paper first applies a prediction model based on self-attention memory ConvLSTM (SAM-ConvLSTM) to predict the global ionospheric total electron content (TEC) maps with up to 1 day of lead time. We choose the global ionospheric TEC maps released by the Center for Orbit Determination in Europe (CODE) as the training data set covering the period from 1999 to 2022. Besides that, we put several space environment data as additional multivariate-features into the framework of the prediction model to enhance its forecasting ability. In order to confirm the efficiency of the proposed model, the other two prediction models based on convolutional long short-term memory (LSTM) are used for comparison. The three models are trained and evaluated on the same data set. Results show that the proposed SAM-ConvLSTM prediction model performs more accurately than the other two models, and more stably under space weather events. In order to assess the generalization capabilities of the proposed model amidst severe space weather occurrences, we selected the period of 22–25 April 2023, characterized by a potent geomagnetic storm, for experimental validation. Subsequently, we employed the 1-day predicted global TEC products from the Center for Operational Products and Services (COPG) and the SAM-ConvLSTM model to evaluate their respective forecasting prowess. The results show that the SAM-ConvLSTM prediction model achieves lower prediction error. In one word, the ionospheric TEC prediction model proposed in this paper can establish the ionosphere TEC of spatio-temporal data association for a long time, and realize high precision of prediction performance.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"72 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138566273","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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