Jiahui Hu, Aurora López Rubio, A. Chartier, S. McDonald, S. Datta‐Barua
In this work we quantify the representation error of the algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), which estimates global neutral winds and ion drifts given time‐varying plasma densities. SAMI3 (Sami3 is A Model of the Ionosphere) serves as the background climate model and pseudo‐measurements for the EMPIRE observation system. This configuration allows the data assimilation inputs to be self‐consistent between each other and with the validation data. The estimated neutral winds and ion drifts are compared to the Horizontal Wind Model (HWM14) and SAMI3 “truth.” For both the quiet period on 25 August 2018 and subs7equent storm on 26 August, the EMPIRE estimation of ion drifts is better at low‐to‐mid geomagnetic latitudes with mean error up to 20 m/s. For the high latitudes (poleward of ±60° magnetic), the mean errors exceed 50 m/s with variances up to 200 m/s, and the relative errors are higher than the “truth.” At latitudes of ±87°, the large errors are attributed to a boundary effect. However, the neutral wind mean errors peak at 20 m/s at mid‐latitudes (40°–60° magnetic), with larger uncertainties, then converge to 0 approaching higher latitudes. By conducting this study, we define a method for obtaining the representation error covariance for future use of EMPIRE with SAMI3 as background.
{"title":"Quantification of Representation Error in the Neutral Winds and Ion Drifts Using Data Assimilation","authors":"Jiahui Hu, Aurora López Rubio, A. Chartier, S. McDonald, S. Datta‐Barua","doi":"10.1029/2023sw003609","DOIUrl":"https://doi.org/10.1029/2023sw003609","url":null,"abstract":"In this work we quantify the representation error of the algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), which estimates global neutral winds and ion drifts given time‐varying plasma densities. SAMI3 (Sami3 is A Model of the Ionosphere) serves as the background climate model and pseudo‐measurements for the EMPIRE observation system. This configuration allows the data assimilation inputs to be self‐consistent between each other and with the validation data. The estimated neutral winds and ion drifts are compared to the Horizontal Wind Model (HWM14) and SAMI3 “truth.” For both the quiet period on 25 August 2018 and subs7equent storm on 26 August, the EMPIRE estimation of ion drifts is better at low‐to‐mid geomagnetic latitudes with mean error up to 20 m/s. For the high latitudes (poleward of ±60° magnetic), the mean errors exceed 50 m/s with variances up to 200 m/s, and the relative errors are higher than the “truth.” At latitudes of ±87°, the large errors are attributed to a boundary effect. However, the neutral wind mean errors peak at 20 m/s at mid‐latitudes (40°–60° magnetic), with larger uncertainties, then converge to 0 approaching higher latitudes. By conducting this study, we define a method for obtaining the representation error covariance for future use of EMPIRE with SAMI3 as background.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"43 23","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141045114","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}
Purely data-driven ionospheric modeling fails to adequately obey fundamental physical laws. To overcome this shortcoming, we propose a novel ionospheric inversion model, Physics-Informed Neural Network based on fully physical models SAMI3 (PINN-SAMI3). The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal-spatial distribution of ionospheric plasma parameters. The objective of this study is to investigate the feasibility of integrating physical models with machine learning for ionospheric modeling. The PINN-SAMI3 framework enforces physical laws through the multiple ion species of continuity, momentum, temperature equations in the magnetic dipole coordinate system. The simulation results show that if sparse ion densities are used as training data, it is possible to retrieve ionospheric electron densities, ion velocities and ion temperatures, respectively. The optimal physical constraints have been also investigated for different inversion quantities. Furthermore, the impact of incorporating E × B velocity terms on inversion results during the periods of ionospheric calm and geomagnetic storm is analyzed. The PINN-SAMI3 achieves good inversion results even using sparse data in comparison to the traditional artificial neural networks (ANN). The framework will contribute to advance the future space weather prediction capability with artificial intelligence (AI).
纯粹由数据驱动的电离层建模无法充分遵循基本物理定律。为了克服这一缺陷，我们提出了一种新的电离层反演模型，即基于完全物理模型 SAMI3 的物理信息神经网络（PINN-SAMI3）。该模型将电离层物理模型 SAMI3 的支配方程纳入神经网络，以重建电离层等离子体参数的时空分布。本研究的目的是调查将物理模型与机器学习相结合用于电离层建模的可行性。PINN-SAMI3 框架通过磁偶极坐标系中的多离子连续性、动量、温度方程来执行物理定律。模拟结果表明，如果使用稀疏离子密度作为训练数据，就有可能分别检索出电离层电子密度、离子速度和离子温度。还研究了不同反演量的最佳物理约束条件。此外，还分析了在电离层平静期和地磁风暴期加入 E × B 速度项对反演结果的影响。与传统的人工神经网络（ANN）相比，即使使用稀疏数据，PINN-SAMI3 也能获得良好的反演结果。该框架将有助于利用人工智能（AI）提高未来空间天气预报能力。
{"title":"A Novel Ionospheric Inversion Model: PINN-SAMI3 (Physics Informed Neural Network Based on SAMI3)","authors":"Jiayu Ma, Haiyang Fu, J. D. Huba, Yaqiu Jin","doi":"10.1029/2023sw003823","DOIUrl":"https://doi.org/10.1029/2023sw003823","url":null,"abstract":"Purely data-driven ionospheric modeling fails to adequately obey fundamental physical laws. To overcome this shortcoming, we propose a novel ionospheric inversion model, Physics-Informed Neural Network based on fully physical models SAMI3 (PINN-SAMI3). The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal-spatial distribution of ionospheric plasma parameters. The objective of this study is to investigate the feasibility of integrating physical models with machine learning for ionospheric modeling. The PINN-SAMI3 framework enforces physical laws through the multiple ion species of continuity, momentum, temperature equations in the magnetic dipole coordinate system. The simulation results show that if sparse ion densities are used as training data, it is possible to retrieve ionospheric electron densities, ion velocities and ion temperatures, respectively. The optimal physical constraints have been also investigated for different inversion quantities. Furthermore, the impact of incorporating <b>E</b> × <b>B</b> velocity terms on inversion results during the periods of ionospheric calm and geomagnetic storm is analyzed. The PINN-SAMI3 achieves good inversion results even using sparse data in comparison to the traditional artificial neural networks (ANN). The framework will contribute to advance the future space weather prediction capability with artificial intelligence (AI).","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"4 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140572525","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}
Kara L. Kniezewski, Samuel J. Schonfeld, Carl J. Henney
A new method to nowcast spectral irradiance in extreme ultraviolet (EUV) and far ultraviolet (FUV) bands is presented here, utilizing only solar photospheric magnetograms and the Mg II index (i.e., the core-to-wing ratio). The EUV and FUV modeling outlined here is a direct extension of the SIFT (Solar Indices Forecasting Tool) model, based on Henney et al. (2015, https://doi.org/10.1002/2014sw001118). SIFT estimates solar activity indices using the earth-side solar photospheric magnetic field sums from global magnetic maps generated by the ADAPT (Air Force Data Assimilative Photospheric Flux Transport) model. Utilizing strong and weak magnetic field sums from ADAPT maps, Henney et al. (2015, https://doi.org/10.1002/2014sw001118) showed that EUV & FUV observations can also be well modeled using this technique. However, the original forecasting method required a recent observation of each SIFT model output to determine and apply a 0-day offset. The new method described here expands the SIFT and ADAPT modeling to nowcast the observed Mg II index with a Pearson correlation coefficient of 0.982. By correlating the Mg II model-observation difference with the model-observation difference in the EUV & FUV channels, Mg II can be used to apply the 0-day offset correction yielding improvements in modeling each of the 37 studied EUV & FUV bands. With daily global photospheric magnetic maps and Mg II index observations, this study provides an improved method of nowcasting EUV & FUV bands used to drive thermospheric and ionospheric modeling.
{"title":"Nowcasting Solar EUV Irradiance With Photospheric Magnetic Fields and the Mg II Index","authors":"Kara L. Kniezewski, Samuel J. Schonfeld, Carl J. Henney","doi":"10.1029/2023sw003772","DOIUrl":"https://doi.org/10.1029/2023sw003772","url":null,"abstract":"A new method to nowcast spectral irradiance in extreme ultraviolet (EUV) and far ultraviolet (FUV) bands is presented here, utilizing only solar photospheric magnetograms and the Mg II index (i.e., the core-to-wing ratio). The EUV and FUV modeling outlined here is a direct extension of the SIFT (Solar Indices Forecasting Tool) model, based on Henney et al. (2015, https://doi.org/10.1002/2014sw001118). SIFT estimates solar activity indices using the earth-side solar photospheric magnetic field sums from global magnetic maps generated by the ADAPT (Air Force Data Assimilative Photospheric Flux Transport) model. Utilizing strong and weak magnetic field sums from ADAPT maps, Henney et al. (2015, https://doi.org/10.1002/2014sw001118) showed that EUV & FUV observations can also be well modeled using this technique. However, the original forecasting method required a recent observation of each SIFT model output to determine and apply a 0-day offset. The new method described here expands the SIFT and ADAPT modeling to nowcast the observed Mg II index with a Pearson correlation coefficient of 0.982. By correlating the Mg II model-observation difference with the model-observation difference in the EUV & FUV channels, Mg II can be used to apply the 0-day offset correction yielding improvements in modeling each of the 37 studied EUV & FUV bands. With daily global photospheric magnetic maps and Mg II index observations, this study provides an improved method of nowcasting EUV & FUV bands used to drive thermospheric and ionospheric modeling.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140572630","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 suppression of high-energy cosmic rays, known as Forbush decreases (FDs), represents a promising factor in influencing the global electric circuit (GEC) system. Researchers have delved into these effects by examining variations, often disruptive, of the potential gradient (PG) in ground-based measurements taken in fair weather regions. In this paper, we aim to investigate deviations observed in the diurnal curve of the PG, as compared to the mean values derived from fair weather conditions, during both mild and strong Forbush decreases. Unlike the traditional classification of FDs, which are based on ground level neutron monitor data, we classify FDs using measurements of the Alpha Magnetic Spectrometer (AMS-02) on the International Space Station. To conduct our analysis, we employ the superposed epoch method, focusing on PGs collected between January 2010 and December 2019 at a specific station situated at a low latitude and high altitude: the Complejo Astronómico El Leoncito (CASLEO) in Argentina (31.78°S, 2,550 m above sea level). Our findings reveal that for events associated with FDs having flux amplitude (A) decrease ≤10%, no significant change in the PG is observed. However, for FDs with A > 10%, a clear increase in the PG is seen. For these A > 10% events, we also find a good correlation between the variation of Dst and Kp indices and the variation of PG.
{"title":"Effects of Forbush Decreases on the Global Electric Circuit","authors":"J. Tacza, G. Li, J.-P. Raulin","doi":"10.1029/2023sw003852","DOIUrl":"https://doi.org/10.1029/2023sw003852","url":null,"abstract":"The suppression of high-energy cosmic rays, known as Forbush decreases (FDs), represents a promising factor in influencing the global electric circuit (GEC) system. Researchers have delved into these effects by examining variations, often disruptive, of the potential gradient (PG) in ground-based measurements taken in fair weather regions. In this paper, we aim to investigate deviations observed in the diurnal curve of the PG, as compared to the mean values derived from fair weather conditions, during both mild and strong Forbush decreases. Unlike the traditional classification of FDs, which are based on ground level neutron monitor data, we classify FDs using measurements of the Alpha Magnetic Spectrometer (AMS-02) on the International Space Station. To conduct our analysis, we employ the superposed epoch method, focusing on PGs collected between January 2010 and December 2019 at a specific station situated at a low latitude and high altitude: the Complejo Astronómico El Leoncito (CASLEO) in Argentina (31.78°S, 2,550 m above sea level). Our findings reveal that for events associated with FDs having flux amplitude (<i>A</i>) decrease ≤10%, no significant change in the PG is observed. However, for FDs with <i>A</i> > 10%, a clear increase in the PG is seen. For these <i>A</i> > 10% events, we also find a good correlation between the variation of Dst and Kp indices and the variation of PG.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"68 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592164","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}
V. Sai Gowtam, Hyunju Connor, Bharat S. R. Kunduri, Joachim Raeder, Karl M. Laundal, S. Tulasi Ram, Dogacan S. Ozturk, Donald Hampton, Shibaji Chakraborty, Charles Owolabi, Amy Keesee
We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.
{"title":"Calculating the High-Latitude Ionospheric Electrodynamics Using a Machine Learning-Based Field-Aligned Current Model","authors":"V. Sai Gowtam, Hyunju Connor, Bharat S. R. Kunduri, Joachim Raeder, Karl M. Laundal, S. Tulasi Ram, Dogacan S. Ozturk, Donald Hampton, Shibaji Chakraborty, Charles Owolabi, Amy Keesee","doi":"10.1029/2023sw003683","DOIUrl":"https://doi.org/10.1029/2023sw003683","url":null,"abstract":"We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (Φ<sub><i>PC</i></sub>) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"52 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140591964","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}
Erik M. Vandegriff, Daniel T. Welling, Agnit Mukhopadhyay, Andrew P. Dimmock, Steven K. Morley, Ramon E. Lopez
One of the prominent effects of space weather is the formation of rapid geomagnetic field variations on Earth's surface driven by the magnetosphere-ionosphere system. These geomagnetic disturbances (GMDs) cause geomagnetically induced currents to run through ground conducting systems. In particular, localized GMDs (LGMDs) can be high amplitude and can have an effect on scale sizes less than 100 km, making them hazardous to power grids and difficult to predict. In this study, we examine the ability of the Space Weather Modeling Framework (SWMF) to reproduce LGMDs in the 7 September 2017 event using both existing and new metrics to quantify the success of the model against observation. We show that the high-resolution SWMF can reproduce LGMDs driven by ionospheric sources, but struggles to reproduce LGMDs driven by substorm effects. We calculate the global maxima of the magnetic fluctuations to show instances when the SWMF captures LGMDs at the correct times but not the correct locations. To remedy these shortcomings we suggest model developments that will directly impact the ability of the SWMF to reproduce LGMDs, most importantly updating the ionospheric conductance calculation from empirical to physics-based.
{"title":"Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics","authors":"Erik M. Vandegriff, Daniel T. Welling, Agnit Mukhopadhyay, Andrew P. Dimmock, Steven K. Morley, Ramon E. Lopez","doi":"10.1029/2023sw003799","DOIUrl":"https://doi.org/10.1029/2023sw003799","url":null,"abstract":"One of the prominent effects of space weather is the formation of rapid geomagnetic field variations on Earth's surface driven by the magnetosphere-ionosphere system. These geomagnetic disturbances (GMDs) cause geomagnetically induced currents to run through ground conducting systems. In particular, localized GMDs (LGMDs) can be high amplitude and can have an effect on scale sizes less than 100 km, making them hazardous to power grids and difficult to predict. In this study, we examine the ability of the Space Weather Modeling Framework (SWMF) to reproduce LGMDs in the 7 September 2017 event using both existing and new metrics to quantify the success of the model against observation. We show that the high-resolution SWMF can reproduce LGMDs driven by ionospheric sources, but struggles to reproduce LGMDs driven by substorm effects. We calculate the global maxima of the magnetic fluctuations to show instances when the SWMF captures LGMDs at the correct times but not the correct locations. To remedy these shortcomings we suggest model developments that will directly impact the ability of the SWMF to reproduce LGMDs, most importantly updating the ionospheric conductance calculation from empirical to physics-based.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"100 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140591950","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}
On 3 February 2022, SpaceX launched 49 Starlink satellites, 38 of which unexpectedly de-orbited. Although this event was attributed to space weather, definitive causality remained elusive because space weather conditions were not extreme. In this study, we identify solar sources of the interplanetary coronal mass ejections that were responsible for the geomagnetic storms around the time of launch of the Starlink satellites and for the first time, investigate their impact on Earth's magnetosphere using magnetohydrodynamic modeling. The model results demonstrate that the satellites were launched into an already disturbed space environment that persisted over several days. However, on performing comparative satellite orbital decay analyses, we find that space weather alone was not responsible but conspired together with a low-altitude insertion and low satellite mass-to-area ratio to precipitate this unusual loss. Our work bridges space weather causality across the Sun–Earth system—with relevance for space-based human technologies.
{"title":"The Loss of Starlink Satellites in February 2022: How Moderate Geomagnetic Storms Can Adversely Affect Assets in Low-Earth Orbit","authors":"Yoshita Baruah, Souvik Roy, Suvadip Sinha, Erika Palmerio, Sanchita Pal, Denny M. Oliveira, Dibyendu Nandy","doi":"10.1029/2023sw003716","DOIUrl":"https://doi.org/10.1029/2023sw003716","url":null,"abstract":"On 3 February 2022, SpaceX launched 49 Starlink satellites, 38 of which unexpectedly de-orbited. Although this event was attributed to space weather, definitive causality remained elusive because space weather conditions were not extreme. In this study, we identify solar sources of the interplanetary coronal mass ejections that were responsible for the geomagnetic storms around the time of launch of the Starlink satellites and for the first time, investigate their impact on Earth's magnetosphere using magnetohydrodynamic modeling. The model results demonstrate that the satellites were launched into an already disturbed space environment that persisted over several days. However, on performing comparative satellite orbital decay analyses, we find that space weather alone was not responsible but conspired together with a low-altitude insertion and low satellite mass-to-area ratio to precipitate this unusual loss. Our work bridges space weather causality across the Sun–Earth system—with relevance for space-based human technologies.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592170","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}
Victoriya V. Forsythe, Dieter Bilitza, Angeline G. Burrell, Kenneth F. Dymond, Bruce A. Fritz, Sarah E. McDonald
The International Reference Ionosphere (IRI) model is widely used in the ionospheric community and considered the gold standard for empirical ionospheric models. The development of this model was initiated in the late 1960s using the FORTRAN language; for its programming approach, the model outputs were calculated separately for each given geographic location and time stamp. The Consultative Committee on International Radio (CCIR) and International Union of Radio Science (URSI) coefficients provide the skeleton of the IRI model, as they define the global distribution of the maximum useable ionospheric frequency foF2 and the propagation factor M(3,000)F2. At the U.S. Naval Research Laboratory, a novel Python tool was developed that enables global runs of the IRI model with significantly lower computational overhead. This was made possible through the Python rebuild of the core IRI component (which calculates ionospheric critical frequency using the CCIR or URSI coefficients), taking advantage of NumPy matrix multiplication instead of using cyclic addition. This paper explains in detail this new approach and introduces all components of the PyIRI package.
国际参考电离层（IRI）模型在电离层界得到广泛应用，被认为是经验电离层模型的黄金标准。该模型的开发始于 20 世纪 60 年代末，使用的是 FORTRAN 语言；其编程方法是对每个给定的地理位置和时间戳分别计算模型输出。国际无线电咨询委员会（CCIR）和国际无线电科学联合会（URSI）的系数为 IRI 模型提供了骨架，因为它们定义了电离层最大可用频率 foF2 和传播因子 M(3,000)F2 的全球分布。美国海军研究实验室开发了一种新颖的 Python 工具，能够在全球范围内运行 IRI 模型，大大降低了计算开销。通过对 IRI 核心组件（使用 CCIR 或 URSI 系数计算电离层临界频率）进行 Python 重构，利用 NumPy 矩阵乘法而不是循环加法的优势，这一切成为可能。本文详细解释了这种新方法，并介绍了 PyIRI 软件包的所有组件。
{"title":"PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python","authors":"Victoriya V. Forsythe, Dieter Bilitza, Angeline G. Burrell, Kenneth F. Dymond, Bruce A. Fritz, Sarah E. McDonald","doi":"10.1029/2023sw003739","DOIUrl":"https://doi.org/10.1029/2023sw003739","url":null,"abstract":"The International Reference Ionosphere (IRI) model is widely used in the ionospheric community and considered the gold standard for empirical ionospheric models. The development of this model was initiated in the late 1960s using the FORTRAN language; for its programming approach, the model outputs were calculated separately for each given geographic location and time stamp. The Consultative Committee on International Radio (CCIR) and International Union of Radio Science (URSI) coefficients provide the skeleton of the IRI model, as they define the global distribution of the maximum useable ionospheric frequency <i>fo</i>F2 and the propagation factor <i>M</i>(3,000)F2. At the U.S. Naval Research Laboratory, a novel Python tool was developed that enables global runs of the IRI model with significantly lower computational overhead. This was made possible through the Python rebuild of the core IRI component (which calculates ionospheric critical frequency using the CCIR or URSI coefficients), taking advantage of NumPy matrix multiplication instead of using cyclic addition. This paper explains in detail this new approach and introduces all components of the PyIRI package.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"61 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592169","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}
Darcy Cordell, Ian R. Mann, Hannah Parry, Martyn J. Unsworth, Ryan Cui, Colin Clark, Eva Kelemen, Ryan MacMullin
During space weather events, geomagnetic disturbances (GMDs) induce geoelectric fields which drive geomagnetically induced currents (GICs) through electrically-grounded power transmission lines. Alberta, Canada—located near the auroral zone and thus prone to large GMDs—has a dense network of magnetometer stations and surface impedance measurements to better characterize the GMD and ground conductivity, respectively. GIC monitoring devices were recently installed at five substation transformer neutrals, providing a unique opportunity to compare data to modeled GICs. GICs are modeled across the >240 kV provincial power transmission network during a moderate GMD event on 24 April 2023. GIC monitoring devices measured larger neutral-to-ground currents than expected up to 117 Amps during peak storm time, providing unequivocal evidence linking network GICs with GMDs. The model performs reasonably well (correlation coefficients >0.5; performance parameter >0.15) at four of five substations, but generally underestimates peak GIC values (sometimes by a factor >2), suggesting that the present model underrepresents overall network risk. The model performs poorly at one of the five substations (correlation = 0.46; performance parameter = 0.10), the reasons for which may be due to simplifications and/or unknowns in network parameters. Despite these underestimates, during this GMD, the model predicts the largest GIC at substations located in the northeastern part of the province (240 kV) or around Edmonton (500 kV)—regions which have significant electrical and industrial infrastructure. Further refinement of the network model with transformer resistances, more line and earthing resistances, and/or including lower voltage levels is necessary to improve data fit.
{"title":"Modeling Geomagnetically Induced Currents in the Alberta Power Network: Comparison and Validation Using Hall Probe Measurements During a Magnetic Storm","authors":"Darcy Cordell, Ian R. Mann, Hannah Parry, Martyn J. Unsworth, Ryan Cui, Colin Clark, Eva Kelemen, Ryan MacMullin","doi":"10.1029/2023sw003813","DOIUrl":"https://doi.org/10.1029/2023sw003813","url":null,"abstract":"During space weather events, geomagnetic disturbances (GMDs) induce geoelectric fields which drive geomagnetically induced currents (GICs) through electrically-grounded power transmission lines. Alberta, Canada—located near the auroral zone and thus prone to large GMDs—has a dense network of magnetometer stations and surface impedance measurements to better characterize the GMD and ground conductivity, respectively. GIC monitoring devices were recently installed at five substation transformer neutrals, providing a unique opportunity to compare data to modeled GICs. GICs are modeled across the >240 kV provincial power transmission network during a moderate GMD event on 24 April 2023. GIC monitoring devices measured larger neutral-to-ground currents than expected up to 117 Amps during peak storm time, providing unequivocal evidence linking network GICs with GMDs. The model performs reasonably well (correlation coefficients >0.5; performance parameter >0.15) at four of five substations, but generally underestimates peak GIC values (sometimes by a factor >2), suggesting that the present model underrepresents overall network risk. The model performs poorly at one of the five substations (correlation = 0.46; performance parameter = 0.10), the reasons for which may be due to simplifications and/or unknowns in network parameters. Despite these underestimates, during this GMD, the model predicts the largest GIC at substations located in the northeastern part of the province (240 kV) or around Edmonton (500 kV)—regions which have significant electrical and industrial infrastructure. Further refinement of the network model with transformer resistances, more line and earthing resistances, and/or including lower voltage levels is necessary to improve data fit.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"14 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592165","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}
Simon James Walker, Karl Magnus Laundal, Jone Peter Reistad, Anders Ohma, Spencer Mark Hatch, Gareth Chisham, Margot Decotte
The boundaries of the auroral oval and auroral electrojets are an important source of information for understanding the coupling between the solar wind and the near-earth plasma environment. Of these two types of boundaries the auroral electrojet boundaries have received comparatively little attention, and even less attention has been given to the connection between the two. Here we introduce a technique for estimating the electrojet boundaries, and other properties such as total current and peak current, from 1-D latitudinal profiles of the eastward component of equivalent current sheet density. We apply this technique to a preexisting database of such currents along the 105° magnetic meridian, estimated using ground-based magnetometers, producing a total of 11 years of 1-min resolution electrojet boundaries during the period 2000–2020. Using statistics and conjunction events we compare our electrojet boundary data set with an existing electrojet boundary data set, based on Swarm satellite measurements, and auroral oval proxies based on particle precipitation and field-aligned currents. This allows us to validate our data set and investigate the feasibility of an auroral oval proxy based on electrojet boundaries. Through this investigation we find the proton precipitation auroral oval is a closer match with the electrojet boundaries. However, the bimodal nature of the electrojet boundaries as we approach the noon and midnight discontinuities makes an average electrojet oval poorly defined. With this and the direct comparisons differing from the statistics, defining the proton auroral oval from electrojet boundaries across all local and universal times is challenging.
{"title":"A Comparison of Auroral Oval Proxies With the Boundaries of the Auroral Electrojets","authors":"Simon James Walker, Karl Magnus Laundal, Jone Peter Reistad, Anders Ohma, Spencer Mark Hatch, Gareth Chisham, Margot Decotte","doi":"10.1029/2023sw003689","DOIUrl":"https://doi.org/10.1029/2023sw003689","url":null,"abstract":"The boundaries of the auroral oval and auroral electrojets are an important source of information for understanding the coupling between the solar wind and the near-earth plasma environment. Of these two types of boundaries the auroral electrojet boundaries have received comparatively little attention, and even less attention has been given to the connection between the two. Here we introduce a technique for estimating the electrojet boundaries, and other properties such as total current and peak current, from 1-D latitudinal profiles of the eastward component of equivalent current sheet density. We apply this technique to a preexisting database of such currents along the 105° magnetic meridian, estimated using ground-based magnetometers, producing a total of 11 years of 1-min resolution electrojet boundaries during the period 2000–2020. Using statistics and conjunction events we compare our electrojet boundary data set with an existing electrojet boundary data set, based on <i>Swarm</i> satellite measurements, and auroral oval proxies based on particle precipitation and field-aligned currents. This allows us to validate our data set and investigate the feasibility of an auroral oval proxy based on electrojet boundaries. Through this investigation we find the proton precipitation auroral oval is a closer match with the electrojet boundaries. However, the bimodal nature of the electrojet boundaries as we approach the noon and midnight discontinuities makes an average electrojet oval poorly defined. With this and the direct comparisons differing from the statistics, defining the proton auroral oval from electrojet boundaries across all local and universal times is challenging.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"54 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592168","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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