Fuqing Huang, H. Ruan, J. Lei, J. Zhong, X. Yue, Guozhu Li, Yiding Chen, Jianhui He, Na Li, X. Luan, C. Xiong, Xiankang Dou
The F2‐peak plasma frequency (foF2) and the height of the F2 peak (hmF2) are two of the most important parameters for any ionospheric model, as well as radio propagation studies and applications. In this study, we have developed empirical models to capture the most significant variations of foF2 and hmF2. The derived empirical models (referred to as the USTC models within this study) are specified through global ionosonde and reanalysis data based on the International Reference Ionosphere (IRI) Consultative Committee on International Radio (CCIR) method and Constellation Observindg System for Meteorology, Ionosphere, and Climate (COSMIC) observations based on the empirical orthogonal function analysis, respectively. The USTC models are validated against the IRI CCIR model prediction. The comparison results revealed that the empirical foF2 model performs better in capturing the foF2 variations than the IRI CCIR model, which can overcome the underestimation of the IRI CCIR model at low latitudes. Although the IRI CCIR model overestimation at middle latitudes is addressed by the empirical hmF2 model, the visible differences between the model predictions and ionosonde observations still exist at low latitudes, which could be attributed to the significant difference between COSMIC and ionosonde hmF2 measures.
F2 峰等离子体频率（foF2）和 F2 峰高（hmF2）是任何电离层模型以及无线电传播研究和应用的两个最重要参数。在本研究中，我们开发了经验模型来捕捉 foF2 和 hmF2 的最显著变化。根据国际参考电离层（IRI）国际无线电咨询委员会（CCIR）方法和基于经验正交函数分析的气象、电离层和气候星座观测系统（COSMIC）观测数据，分别通过全球电离层和再分析数据指定了推导出的经验模型（在本研究中称为 USTC 模型）。USTC 模型与 IRI CCIR 模型预测进行了验证。对比结果表明，经验 foF2 模式在捕捉 foF2 变化方面的表现优于 IRI CCIR 模式，可以克服 IRI CCIR 模式在低纬度地区的低估问题。虽然经验 hmF2 模型解决了 IRI CCIR 模型在中纬度高估的问题，但在低纬度，模型预测和电离层观测之间仍然存在明显的差异，这可能是由于 COSMIC 和电离层 hmF2 测量之间的显著差异造成的。
{"title":"Empirical Models of foF2 and hmF2 Reconstituted by Global Ionosonde and Reanalysis Data and COSMIC Observations","authors":"Fuqing Huang, H. Ruan, J. Lei, J. Zhong, X. Yue, Guozhu Li, Yiding Chen, Jianhui He, Na Li, X. Luan, C. Xiong, Xiankang Dou","doi":"10.1029/2023sw003848","DOIUrl":"https://doi.org/10.1029/2023sw003848","url":null,"abstract":"The F2‐peak plasma frequency (foF2) and the height of the F2 peak (hmF2) are two of the most important parameters for any ionospheric model, as well as radio propagation studies and applications. In this study, we have developed empirical models to capture the most significant variations of foF2 and hmF2. The derived empirical models (referred to as the USTC models within this study) are specified through global ionosonde and reanalysis data based on the International Reference Ionosphere (IRI) Consultative Committee on International Radio (CCIR) method and Constellation Observindg System for Meteorology, Ionosphere, and Climate (COSMIC) observations based on the empirical orthogonal function analysis, respectively. The USTC models are validated against the IRI CCIR model prediction. The comparison results revealed that the empirical foF2 model performs better in capturing the foF2 variations than the IRI CCIR model, which can overcome the underestimation of the IRI CCIR model at low latitudes. Although the IRI CCIR model overestimation at middle latitudes is addressed by the empirical hmF2 model, the visible differences between the model predictions and ionosonde observations still exist at low latitudes, which could be attributed to the significant difference between COSMIC and ionosonde hmF2 measures.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"436 ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140776165","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}
Jun Chen, X. Ren, Guozhen Xu, Peng-Cheng Yang, Hang Liu, Xiaohong Zhang
This study applies the zero‐differenced integer ambiguity method, named PPP‐Fixed, to extract real‐time ionospheric data and eliminate the latencies of rapid/final Global Ionosphere Maps (GIMs). The PPP‐Fixed method is also used to derive ionospheric data for post‐processed GIM generation, named SGG Post‐GIM, combined with low earth orbit satellite data. The obtained hardware delays are applied to revise real‐time ionospheric data. Meanwhile, the estimated multi‐source ionospheric model is regarded as historical data to estimate an ionospheric prediction model for constraint using the semi‐parameter model. Then, the Kalman filter is employed to estimate the parameters to generate real‐time GIM. Finally, the accuracy of estimated real‐time GIM, named SGG RT‐GIM, and SGG Post‐GIM is assessed. During the experimental period, the mean differences of SGG Post‐GIM and SGG RT‐GIM relative to GIMs provided by the international Global Navigation Satellite System service, named IGSG, are −0.46 and −0.57 Total Electron Content Unit (TECU), respectively. The corresponding Root Mean Square (RMS) values are 1.64 and 3.08 TECU. Over the test period, the mean positioning errors of the single‐frequency precise point positioning corrected by IGSG, SGG Post‐GIM, SGG RT‐GIM, and Klobuchar model are 0.14, 0.19, 0.21, and 0.25 m in the horizontal direction, respectively, while the corresponding errors are 0.36, 0.33, 0.38, and 0.64 m in the up direction. Further, the mean biases of experimental days for the self‐consistency assessment are 0.06, −0.01, and −0.07 TECU for IGSG, SGG Post‐GIM, and SGG RT‐GIM, respectively. The corresponding RMS values are 1.19, 1.15, and 1.57 TECU.
{"title":"Method and Validation of Real‐Time Global Ionosphere Modeling Constraint by Multi‐Source GNSS/LEO Data","authors":"Jun Chen, X. Ren, Guozhen Xu, Peng-Cheng Yang, Hang Liu, Xiaohong Zhang","doi":"10.1029/2023sw003800","DOIUrl":"https://doi.org/10.1029/2023sw003800","url":null,"abstract":"This study applies the zero‐differenced integer ambiguity method, named PPP‐Fixed, to extract real‐time ionospheric data and eliminate the latencies of rapid/final Global Ionosphere Maps (GIMs). The PPP‐Fixed method is also used to derive ionospheric data for post‐processed GIM generation, named SGG Post‐GIM, combined with low earth orbit satellite data. The obtained hardware delays are applied to revise real‐time ionospheric data. Meanwhile, the estimated multi‐source ionospheric model is regarded as historical data to estimate an ionospheric prediction model for constraint using the semi‐parameter model. Then, the Kalman filter is employed to estimate the parameters to generate real‐time GIM. Finally, the accuracy of estimated real‐time GIM, named SGG RT‐GIM, and SGG Post‐GIM is assessed. During the experimental period, the mean differences of SGG Post‐GIM and SGG RT‐GIM relative to GIMs provided by the international Global Navigation Satellite System service, named IGSG, are −0.46 and −0.57 Total Electron Content Unit (TECU), respectively. The corresponding Root Mean Square (RMS) values are 1.64 and 3.08 TECU. Over the test period, the mean positioning errors of the single‐frequency precise point positioning corrected by IGSG, SGG Post‐GIM, SGG RT‐GIM, and Klobuchar model are 0.14, 0.19, 0.21, and 0.25 m in the horizontal direction, respectively, while the corresponding errors are 0.36, 0.33, 0.38, and 0.64 m in the up direction. Further, the mean biases of experimental days for the self‐consistency assessment are 0.06, −0.01, and −0.07 TECU for IGSG, SGG Post‐GIM, and SGG RT‐GIM, respectively. The corresponding RMS values are 1.19, 1.15, and 1.57 TECU.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"144 3","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140766636","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}
Daniel B. Phoenix, Christopher J. Mertens, Guillaume Gronoff, Kent Tobiska
Exposure to ionizing radiation from galactic cosmic rays (GCR) and solar energetic particles (SEP) at aircraft flight altitudes can have an adverse effect on human health. Although airline crews are classified as radiation workers by the International Commission on Radiological Protection (ICRP), in most countries, their level of exposure is unquantified and undocumented throughout the duration of their career. As such, there is a need to assess pilot ionizing radiation exposure. The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS), a real‐time, global, physics‐based model is used to assess such exposure. The Automated Radiation Measurements for Aerospace Safety (ARMAS) measurement data set consists of high latitude, high altitude, and long‐duration aircraft flights between 2013 and 2023. Here, we characterize radiation exposure at aviation flight altitudes using the NAIRAS model and compare with 45 flight trajectories from the recent ARMAS flight measurement inventory.
在飞机飞行高度接触银河宇宙射线（GCR）和太阳高能粒子（SEP）产生的电离辐射会对人体健康产生不利影响。尽管国际辐射防护委员会（ICRP）将航空机组人员归类为辐射工作人员，但在大多数国家，他们在整个职业生涯中的辐射水平是无法量化和记录的。因此，有必要对飞行员电离辐照进行评估。Nowcast of Aerospace Ionizing RAdiation System（NAIRAS）是一个基于物理学的实时全球模型，用于评估此类辐照。航空航天安全自动辐射测量（ARMAS）测量数据集包括 2013 年至 2023 年期间的高纬度、高海拔和长时间飞机飞行。在此，我们使用 NAIRAS 模型描述了航空飞行高度的辐照特征，并与最近 ARMAS 飞行测量清单中的 45 个飞行轨迹进行了比较。
{"title":"Characterization of Radiation Exposure at Aviation Flight Altitudes Using the Nowcast of Aerospace Ionizing Radiation System (NAIRAS)","authors":"Daniel B. Phoenix, Christopher J. Mertens, Guillaume Gronoff, Kent Tobiska","doi":"10.1029/2024sw003869","DOIUrl":"https://doi.org/10.1029/2024sw003869","url":null,"abstract":"Exposure to ionizing radiation from galactic cosmic rays (GCR) and solar energetic particles (SEP) at aircraft flight altitudes can have an adverse effect on human health. Although airline crews are classified as radiation workers by the International Commission on Radiological Protection (ICRP), in most countries, their level of exposure is unquantified and undocumented throughout the duration of their career. As such, there is a need to assess pilot ionizing radiation exposure. The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS), a real‐time, global, physics‐based model is used to assess such exposure. The Automated Radiation Measurements for Aerospace Safety (ARMAS) measurement data set consists of high latitude, high altitude, and long‐duration aircraft flights between 2013 and 2023. Here, we characterize radiation exposure at aviation flight altitudes using the NAIRAS model and compare with 45 flight trajectories from the recent ARMAS flight measurement inventory.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"130 3","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140778002","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}
Jong‐Sun Park, Quan Qi Shi, O. Troshichev, Khan‐Hyuk Kim, J. Shue, T. Pitkänen, Hui Zhang
In this study, we investigate statistical features of polar cap north (PCN) and south (PCS) indices in response to various interplanetary conditions (interplanetary magnetic field [IMF] orientation in three‐dimensions) and terrestrial conditions (seasonal and magnetic local time [MLT] locations of the index stations). The concurrent PCN‐PCS pairs for 1998–2002 and 2004–2018 are divided based on their sign type (positive‐positive, negative‐negative, negative‐positive, and positive‐negative PCN‐PCS pairs) and time coverage (the times when both index stations are in the dawn/dusk MLT sector during northern summer/winter). Analyzing the IMF orientation dependence on the occurrence probabilities of concurrent indices and on the differences between the indices in various sign types for each time coverage reveals that the statistical features in PCN‐PCS pairs obtained in the dawn MLT sector can be largely explained by the effects of the three‐component IMF (related to the polar cap convection patterns) combined with season (related to the hemispheric asymmetry in solar illumination‐induced ionospheric conductance). However, those obtained in the dusk MLT sector are controlled dominantly by seasonal effects rather than IMF orientation effects. Our findings indicate that PCN‐PCS pair data provide local views about the solar wind‐magnetosphere‐ionosphere (SW‐M‐I) coupling system with different control efficiencies of IMF orientation and season depending on the MLT location of the stations. Therefore, introducing polar cap indices recorded simultaneously at various locations in both hemispheres and analyzing them are strongly required to infer global views of the coupled SW‐M‐I system in the open field regions with higher confidence.
{"title":"Statistical Features of Polar Cap North and South Indices in Response to Interplanetary and Terrestrial Conditions: A Revisit","authors":"Jong‐Sun Park, Quan Qi Shi, O. Troshichev, Khan‐Hyuk Kim, J. Shue, T. Pitkänen, Hui Zhang","doi":"10.1029/2024sw003856","DOIUrl":"https://doi.org/10.1029/2024sw003856","url":null,"abstract":"In this study, we investigate statistical features of polar cap north (PCN) and south (PCS) indices in response to various interplanetary conditions (interplanetary magnetic field [IMF] orientation in three‐dimensions) and terrestrial conditions (seasonal and magnetic local time [MLT] locations of the index stations). The concurrent PCN‐PCS pairs for 1998–2002 and 2004–2018 are divided based on their sign type (positive‐positive, negative‐negative, negative‐positive, and positive‐negative PCN‐PCS pairs) and time coverage (the times when both index stations are in the dawn/dusk MLT sector during northern summer/winter). Analyzing the IMF orientation dependence on the occurrence probabilities of concurrent indices and on the differences between the indices in various sign types for each time coverage reveals that the statistical features in PCN‐PCS pairs obtained in the dawn MLT sector can be largely explained by the effects of the three‐component IMF (related to the polar cap convection patterns) combined with season (related to the hemispheric asymmetry in solar illumination‐induced ionospheric conductance). However, those obtained in the dusk MLT sector are controlled dominantly by seasonal effects rather than IMF orientation effects. Our findings indicate that PCN‐PCS pair data provide local views about the solar wind‐magnetosphere‐ionosphere (SW‐M‐I) coupling system with different control efficiencies of IMF orientation and season depending on the MLT location of the stations. Therefore, introducing polar cap indices recorded simultaneously at various locations in both hemispheres and analyzing them are strongly required to infer global views of the coupled SW‐M‐I system in the open field regions with higher confidence.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"38 ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140786201","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 global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space-based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space-based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm-C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm-B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE-GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively.
{"title":"Assimilating Space-Based Thermospheric Neutral Density (TND) Data Into the TIE-GCM Coupled Model During Periods With Low and High Solar Activity","authors":"Mona Kosary, Saeed Farzaneh, Maike Schumacher, Ehsan Forootan","doi":"10.1029/2023sw003811","DOIUrl":"https://doi.org/10.1029/2023sw003811","url":null,"abstract":"The global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space-based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space-based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm-C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm-B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE-GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"298 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140591954","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}
Some space weather models, such as the Space Weather Modeling Framework (SWMF) used in this study, use solar wind propagated from the first Lagrange point (L1) to the bow shock nose (BSN) to forecast geomagnetic storms. The SWMF is a highly coupled framework of space weather models that include multiple facets of the Geospace environment, such as the magnetosphere and ionosphere. The propagated solar wind measurements are used as a boundary condition for SWMF. The solar wind propagation method is a timeshift based on the calculated phase front normal (PFN) which leads to some uncertainties. For example, the propagated solar wind could have evolved during this timeshift. We use a data set of 123 geomagnetic storms between 2010 and 2019 run by the SWMF Geospace configuration to analyze the impact solar wind propagation and solar wind driving has on the geomagnetic indices. We look at the probability distributions of errors in SYM-H, cross polar cap potential (CPCP), and auroral electrojet indices AL and AU. Through studying the median errors (MdE), standard deviations and standardized regression coefficients, we find that the errors depend on the propagation parameters. Among the results, we show that the accuracy of the simulated SYM-H depends on the spacecraft distance from the Sun-Earth line. We also quantify the dependence of the standard deviation in SYM-H errors on the PFN and solar wind pressure. These statistics provide an insight into how the propagation method affects the final product of the simulation, which are the geomagnetic indices.
{"title":"Accuracy of Global Geospace Simulations: Influence of Solar Wind Monitor Location and Solar Wind Driving","authors":"Q. Al Shidi, T. I. Pulkkinen, D. Welling, G. Toth","doi":"10.1029/2023sw003747","DOIUrl":"https://doi.org/10.1029/2023sw003747","url":null,"abstract":"Some space weather models, such as the Space Weather Modeling Framework (SWMF) used in this study, use solar wind propagated from the first Lagrange point (L1) to the bow shock nose (BSN) to forecast geomagnetic storms. The SWMF is a highly coupled framework of space weather models that include multiple facets of the Geospace environment, such as the magnetosphere and ionosphere. The propagated solar wind measurements are used as a boundary condition for SWMF. The solar wind propagation method is a timeshift based on the calculated phase front normal (PFN) which leads to some uncertainties. For example, the propagated solar wind could have evolved during this timeshift. We use a data set of 123 geomagnetic storms between 2010 and 2019 run by the SWMF Geospace configuration to analyze the impact solar wind propagation and solar wind driving has on the geomagnetic indices. We look at the probability distributions of errors in SYM-H, cross polar cap potential (CPCP), and auroral electrojet indices AL and AU. Through studying the median errors (MdE), standard deviations and standardized regression coefficients, we find that the errors depend on the propagation parameters. Among the results, we show that the accuracy of the simulated SYM-H depends on the spacecraft distance from the Sun-Earth line. We also quantify the dependence of the standard deviation in SYM-H errors on the PFN and solar wind pressure. These statistics provide an insight into how the propagation method affects the final product of the simulation, which are the geomagnetic indices.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"51 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140324426","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}
Qihe Shao, Ying Liu, Yinhe Luo, Graham Heinson, Yixian Xu, Jinsong Du, Chao Chen
Evaluating the impact of geomagnetic disturbances on power grid infrastructure is critical to mitigate the risk posed by geomagnetically induced currents (GICs). In this paper, the geoelectric field and induced voltage distribution in North China were estimated from the SinoProbe magnetotelluric (MT) impedance data together with the geomagnetic observatory data of six INTERMAGNET stations recorded during the significant geomagnetic storm of 17th March 2015. The measured impedances from 119 SinoProbe MT sites were convolved with geomagnetic observatory data to account for the Earth's complex three-dimensional electrical resistivity structure. The resultant geoelectric field was then used to model the induced voltage distribution across the regional power transmission network in North China. Due to the large inter-site distances of the SinoProbe MT program, the derived geoelectric field is mostly homogeneous, except in the Ordos Basin that displays a polarization of the geoelectric field, and with higher magnitudes in the orogenic belts. The estimated geoelectric fields in Taihang-Lvliang, Yanshan, and Luxi orogenic belts of North China were large (>1 V/km) during the storm, due to high-resistivity lithosphere resulting in large voltage gradients in the Earth. However, in relation to locations of major power transmission lines, only the central part of North China experienced induced voltages exceeding 100 V.
{"title":"Geoelectric Field Estimations During Geomagnetic Storm in North China From SinoProbe Magnetotelluric Impedances","authors":"Qihe Shao, Ying Liu, Yinhe Luo, Graham Heinson, Yixian Xu, Jinsong Du, Chao Chen","doi":"10.1029/2023sw003758","DOIUrl":"https://doi.org/10.1029/2023sw003758","url":null,"abstract":"Evaluating the impact of geomagnetic disturbances on power grid infrastructure is critical to mitigate the risk posed by geomagnetically induced currents (GICs). In this paper, the geoelectric field and induced voltage distribution in North China were estimated from the SinoProbe magnetotelluric (MT) impedance data together with the geomagnetic observatory data of six INTERMAGNET stations recorded during the significant geomagnetic storm of 17th March 2015. The measured impedances from 119 SinoProbe MT sites were convolved with geomagnetic observatory data to account for the Earth's complex three-dimensional electrical resistivity structure. The resultant geoelectric field was then used to model the induced voltage distribution across the regional power transmission network in North China. Due to the large inter-site distances of the SinoProbe MT program, the derived geoelectric field is mostly homogeneous, except in the Ordos Basin that displays a polarization of the geoelectric field, and with higher magnitudes in the orogenic belts. The estimated geoelectric fields in Taihang-Lvliang, Yanshan, and Luxi orogenic belts of North China were large (>1 V/km) during the storm, due to high-resistivity lithosphere resulting in large voltage gradients in the Earth. However, in relation to locations of major power transmission lines, only the central part of North China experienced induced voltages exceeding 100 V.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"9 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140324454","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}
Noe Lugaz, Brett Carter, Jennifer L. Gannon, Huixin Liu, Steve K. Morley, Shasha Zou
Peer reviewing is the foundation of modern scholarship, with external specialists being asked to fairly check and evaluate submitted work. This difficult and often time-consuming activity is performed voluntarily, with the understanding that one's own scholarship shall benefit down the line from a careful analysis of its assumption, results, accuracy, and yes, language, as we are now evaluating someone else's work. At Space Weather, we pride ourselves on a fair but quick review process yielding high-quality articles with a time from submission to first decision of about 2 months. This would not be possible without the hard work of all our reviewers. Once a year, we take the occasion to name these reviewers to thank them for their service to the journal and the community.
Space Weather relies on experts on diverse topics, ranging from plasma and space physics, to engineering, policy, and historical records. Over the course of 2023, authors submitted a record 340 manuscripts to Space Weather and 181 articles were published in our open access journal. With the solar maximum of solar cycle 25 expected within a year, we expect this growth to continue.
In 2023, due to the increase in the number of submissions (which nearly doubled since 2017), Space Weather decided to rely more heavily on Associate Editors (AEs) and invited eight new AEs in addition to Xinan Yue, who has been serving since 2016. Those are Andrew Akala, Tanja Amerstorfer, Robyn Fiori, John Bosco Habarulema, Huiun Le, Vincent Maget, Romina Nikoukar, and Fang Shen. Most submissions are still handled throughout the review by one of the six editors, but for 15%–25% of them, one of the AEs will identify reviewers and make a recommendation to one of the editors. This hybrid model allows Space Weather to keep a clear editorial line, while enabling that the workload on the editors is manageable. This in turn ensures that each submitted article receives a timely and well-grounded decision.
2023 was the last year of Huixin Liu as an editor after close to 6 years of service and we would like to sincerely thank her for her service and contributions to the journal. We are pleased to be joined by a new editor: Jiuhou Lei.
Four hundred eight-six researchers performed 947 reviews, often for multiple revisions of the same manuscript and for multiple manuscripts. Their names are given below, with those who have reviewed three or more manuscripts italicized.
{"title":"Thank You to Our Peer Reviewers in 2023","authors":"Noe Lugaz, Brett Carter, Jennifer L. Gannon, Huixin Liu, Steve K. Morley, Shasha Zou","doi":"10.1029/2024sw003929","DOIUrl":"https://doi.org/10.1029/2024sw003929","url":null,"abstract":"<p>Peer reviewing is the foundation of modern scholarship, with external specialists being asked to fairly check and evaluate submitted work. This difficult and often time-consuming activity is performed voluntarily, with the understanding that one's own scholarship shall benefit down the line from a careful analysis of its assumption, results, accuracy, and yes, language, as we are now evaluating someone else's work. At <i>Space Weather</i>, we pride ourselves on a fair but quick review process yielding high-quality articles with a time from submission to first decision of about 2 months. This would not be possible without the hard work of all our reviewers. Once a year, we take the occasion to name these reviewers to thank them for their service to the journal and the community.</p>\u0000<p><i>Space Weather</i> relies on experts on diverse topics, ranging from plasma and space physics, to engineering, policy, and historical records. Over the course of 2023, authors submitted a record 340 manuscripts to <i>Space Weather</i> and 181 articles were published in our open access journal. With the solar maximum of solar cycle 25 expected within a year, we expect this growth to continue.</p>\u0000<p>In 2023, due to the increase in the number of submissions (which nearly doubled since 2017), <i>Space Weather</i> decided to rely more heavily on Associate Editors (AEs) and invited eight new AEs in addition to Xinan Yue, who has been serving since 2016. Those are Andrew Akala, Tanja Amerstorfer, Robyn Fiori, John Bosco Habarulema, Huiun Le, Vincent Maget, Romina Nikoukar, and Fang Shen. Most submissions are still handled throughout the review by one of the six editors, but for 15%–25% of them, one of the AEs will identify reviewers and make a recommendation to one of the editors. This hybrid model allows <i>Space Weather</i> to keep a clear editorial line, while enabling that the workload on the editors is manageable. This in turn ensures that each submitted article receives a timely and well-grounded decision.</p>\u0000<p>2023 was the last year of Huixin Liu as an editor after close to 6 years of service and we would like to sincerely thank her for her service and contributions to the journal. We are pleased to be joined by a new editor: Jiuhou Lei.</p>\u0000<p>Four hundred eight-six researchers performed 947 reviews, often for multiple revisions of the same manuscript and for multiple manuscripts. Their names are given below, with those who have reviewed three or more manuscripts italicized.</p>\u0000<p>A. Kouloumvakos</p>\u0000<p>Aaron Ridley</p>\u0000<p>Agri Faturahman</p>\u0000<p>Alessio Pignalberi</p>\u0000<p><i>Alex Chartier</i></p>\u0000<p>Alex Hands</p>\u0000<p>Alex Marcuello</p>\u0000<p>Alexa Halford</p>\u0000<p>Alexander Boyd</p>\u0000<p>Alexander Engell</p>\u0000<p>Alexey Danilov</p>\u0000<p><i>Alison Moraes</i></p>\u0000<p>Alisson Lago</p>\u0000<p>Allison Jaynes</p>\u0000<p>Ambelu Tebabal</p>\u0000<p>Amy Keesee</p>\u0000<p><i>Andong Hu</i></p>\u0000<p>Andres Aibar</p>\u0000<p>Andres Calabia</p>\u0000<p>Andrew Dimmock</p>\u0000<p>Andrew Smith</p>\u0000<p>Andrew Sun</p>\u0000<p","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"21 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140312874","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}
Cheng Sheng, Yue Deng, Daniel T. Welling, Steven K. Morley
Previous simulation efforts on geomagnetic disturbances (GMDs) and geomagnetically induced currents (GICs) mostly rely on global magnetohydrodynamics models, which explicitly calculate the magnetospheric currents and carry certain assumptions about the ionosphere currents. Therefore, the role of ionospheric and thermospheric processes to GMDs has not been fully evaluated. In this study, Global Ionosphere Thermosphere Model simulations for an idealized storm event have been conducted. Simply, the high-latitude electrodynamic forcing (potential pattern and particle precipitation) has been specified by empirical models. GMDs due to neutral-wind driven currents have been compared to those caused by magnetospheric convection driven currents during both the main and recovery phases. At locations where the high-latitude electric potential is dominant, neutral-wind driven currents are found to contribute to about 10%–30% of the total GMDs. During the recovery phase when the ion-convection pattern retreats to high latitudes, neutral-wind driven currents become the primary sources for GMDs at middle latitudes on the dayside due to the “flywheel” effect and the large dayside conductance. Our result strongly suggests that ionospheric and thermospheric processes should not be neglected when estimating GMDs and therefore GICs.
{"title":"Geomagnetic Disturbances Due To Neutral-Wind-Driven Ionospheric Currents","authors":"Cheng Sheng, Yue Deng, Daniel T. Welling, Steven K. Morley","doi":"10.1029/2023sw003750","DOIUrl":"https://doi.org/10.1029/2023sw003750","url":null,"abstract":"Previous simulation efforts on geomagnetic disturbances (GMDs) and geomagnetically induced currents (GICs) mostly rely on global magnetohydrodynamics models, which explicitly calculate the magnetospheric currents and carry certain assumptions about the ionosphere currents. Therefore, the role of ionospheric and thermospheric processes to GMDs has not been fully evaluated. In this study, Global Ionosphere Thermosphere Model simulations for an idealized storm event have been conducted. Simply, the high-latitude electrodynamic forcing (potential pattern and particle precipitation) has been specified by empirical models. GMDs due to neutral-wind driven currents have been compared to those caused by magnetospheric convection driven currents during both the main and recovery phases. At locations where the high-latitude electric potential is dominant, neutral-wind driven currents are found to contribute to about 10%–30% of the total GMDs. During the recovery phase when the ion-convection pattern retreats to high latitudes, neutral-wind driven currents become the primary sources for GMDs at middle latitudes on the dayside due to the “flywheel” effect and the large dayside conductance. Our result strongly suggests that ionospheric and thermospheric processes should not be neglected when estimating GMDs and therefore GICs.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140202865","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}
Armando Collado-Villaverde, Pablo Muñoz, Consuelo Cid
The use of Deep Learning models to forecast geomagnetic storms is achieving great results. However, the evaluation of these models is mainly supported on generic regression metrics (such as the Root Mean Squared Error or the Coefficient of Determination), which are not able to properly capture the specific particularities of geomagnetic storms forecasting. Particularly, they do not provide insights during the high activity periods. To overcome this issue, we introduce the Binned Forecasting Error to provide a more accurate assessment of models' performance across the different intensity levels of a geomagnetic storm. This metric facilitates a robust comparison of different forecasting models, presenting a true representation of a model's predictive capabilities while being resilient to different storms duration. In this direction, for enabling fair comparison among models, it is important to standardize the sets of geomagnetic storms for model training, validation and testing. To do this, we have started from the current sets used in the literature for forecasting the SYM-H, enriching them with newer storms not considered previously, focusing not only on disturbances caused by Coronal Mass Ejections but also addressing High-Speed Streams. To operationalize the evaluation framework, a comparative study is conducted between a baseline neural network model and a persistence model, showcasing the effectiveness of the new metric in evaluating forecasting performance during intense geomagnetic storms. Finally, we propose the use of preliminary measurements from ACE to evaluate the model performance in settings closer to an operational real-time scenario, where the forecasting models are expected to operate.
{"title":"A Framework for Evaluating Geomagnetic Indices Forecasting Models","authors":"Armando Collado-Villaverde, Pablo Muñoz, Consuelo Cid","doi":"10.1029/2024sw003868","DOIUrl":"https://doi.org/10.1029/2024sw003868","url":null,"abstract":"The use of Deep Learning models to forecast geomagnetic storms is achieving great results. However, the evaluation of these models is mainly supported on generic regression metrics (such as the Root Mean Squared Error or the Coefficient of Determination), which are not able to properly capture the specific particularities of geomagnetic storms forecasting. Particularly, they do not provide insights during the high activity periods. To overcome this issue, we introduce the Binned Forecasting Error to provide a more accurate assessment of models' performance across the different intensity levels of a geomagnetic storm. This metric facilitates a robust comparison of different forecasting models, presenting a true representation of a model's predictive capabilities while being resilient to different storms duration. In this direction, for enabling fair comparison among models, it is important to standardize the sets of geomagnetic storms for model training, validation and testing. To do this, we have started from the current sets used in the literature for forecasting the SYM-H, enriching them with newer storms not considered previously, focusing not only on disturbances caused by Coronal Mass Ejections but also addressing High-Speed Streams. To operationalize the evaluation framework, a comparative study is conducted between a baseline neural network model and a persistence model, showcasing the effectiveness of the new metric in evaluating forecasting performance during intense geomagnetic storms. Finally, we propose the use of preliminary measurements from ACE to evaluate the model performance in settings closer to an operational real-time scenario, where the forecasting models are expected to operate.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"18 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140181985","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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