Abstract Machine learning (ML) has been increasingly applied to space weather and ionosphere problems in recent years, with the goal of improving modeling and forecasting capabilities through a data‐driven modeling approach of nonlinear relationships. However, little work has been done to quantify the uncertainty of the results, lacking an indication of how confident and reliable the results of an ML system are. In this paper, we implement and analyze several uncertainty quantification approaches for an ML‐based model to forecast Vertical Total Electron Content (VTEC) 1‐day ahead and corresponding uncertainties with 95% confidence intervals (CI): (a) Super‐Ensemble of ML‐based VTEC models (SE), (b) Gradient Tree Boosting with quantile loss function (Quantile Gradient Boosting, QGB), (c) Bayesian neural network (BNN), and (d) BNN including data uncertainty (BNN + D). Techniques that consider only model parameter uncertainties (a and c) predict narrow CI and over‐optimistic results, whereas accounting for both model parameter and data uncertainties with the BNN + D approach leads to a wider CI and the most realistic uncertainties quantification of VTEC forecast. However, the BNN + D approach suffers from a high computational burden, while the QGB approach is the most computationally efficient solution with slightly less realistic uncertainties. The QGB CI are determined to a large extent from space weather indices, as revealed by the feature analysis. They exhibit variations related to daytime/nightime, solar irradiance, geomagnetic activity, and post‐sunset low‐latitude ionosphere enhancement.
{"title":"Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting","authors":"Randa Natras, Benedikt Soja, Michael Schmidt","doi":"10.1029/2023sw003483","DOIUrl":"https://doi.org/10.1029/2023sw003483","url":null,"abstract":"Abstract Machine learning (ML) has been increasingly applied to space weather and ionosphere problems in recent years, with the goal of improving modeling and forecasting capabilities through a data‐driven modeling approach of nonlinear relationships. However, little work has been done to quantify the uncertainty of the results, lacking an indication of how confident and reliable the results of an ML system are. In this paper, we implement and analyze several uncertainty quantification approaches for an ML‐based model to forecast Vertical Total Electron Content (VTEC) 1‐day ahead and corresponding uncertainties with 95% confidence intervals (CI): (a) Super‐Ensemble of ML‐based VTEC models (SE), (b) Gradient Tree Boosting with quantile loss function (Quantile Gradient Boosting, QGB), (c) Bayesian neural network (BNN), and (d) BNN including data uncertainty (BNN + D). Techniques that consider only model parameter uncertainties (a and c) predict narrow CI and over‐optimistic results, whereas accounting for both model parameter and data uncertainties with the BNN + D approach leads to a wider CI and the most realistic uncertainties quantification of VTEC forecast. However, the BNN + D approach suffers from a high computational burden, while the QGB approach is the most computationally efficient solution with slightly less realistic uncertainties. The QGB CI are determined to a large extent from space weather indices, as revealed by the feature analysis. They exhibit variations related to daytime/nightime, solar irradiance, geomagnetic activity, and post‐sunset low‐latitude ionosphere enhancement.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134935936","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}
Abstract Reliable short‐time prediction of thermospheric mass density along the satellite orbit is always essential but challenging for the operation of Low‐Earth orbit satellites. In this paper, three machine‐learning prediction algorithms are investigated, including the Bidirectional Long Short‐Term Memory, the Transformer, and the Light Gradient Boosting Machine (LightGBM) ensemble model of the above models. We use satellite data from CHAMP, GOCE, and SWARM‐C to evaluate the robustness and accuracy of different density variations. The comparison demonstrates that all models achieve compelling predictions and are much better than NRLMSISE‐00. The LightGBM ensemble model (LE‐model) consistently outperforms others in accuracy and stability. Furthermore, when the obtained density data from the newly launched satellites are limited, the trained LE‐model can provide a valid prediction for the new satellite orbit by transfer learning. This study offers a promising insight into the short‐time prediction of thermospheric mass density using ensemble‐transfer learning and may be advantageous to future research on space whether.
{"title":"The Short‐Time Prediction of Thermospheric Mass Density Based on Ensemble‐Transfer Learning","authors":"Peian Wang, Zhou Chen, Xiaohua Deng, Jing‐Song Wang, Rongxing Tang, Haimeng Li, Sheng Hong, Zhiping Wu","doi":"10.1029/2023sw003576","DOIUrl":"https://doi.org/10.1029/2023sw003576","url":null,"abstract":"Abstract Reliable short‐time prediction of thermospheric mass density along the satellite orbit is always essential but challenging for the operation of Low‐Earth orbit satellites. In this paper, three machine‐learning prediction algorithms are investigated, including the Bidirectional Long Short‐Term Memory, the Transformer, and the Light Gradient Boosting Machine (LightGBM) ensemble model of the above models. We use satellite data from CHAMP, GOCE, and SWARM‐C to evaluate the robustness and accuracy of different density variations. The comparison demonstrates that all models achieve compelling predictions and are much better than NRLMSISE‐00. The LightGBM ensemble model (LE‐model) consistently outperforms others in accuracy and stability. Furthermore, when the obtained density data from the newly launched satellites are limited, the trained LE‐model can provide a valid prediction for the new satellite orbit by transfer learning. This study offers a promising insight into the short‐time prediction of thermospheric mass density using ensemble‐transfer learning and may be advantageous to future research on space whether.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135568489","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}
Diptiranjan Rout, S. Patra, S. Kumar, D. Chakrabarty, G. D. Reeves, C. Stolle, K. Pandey, S. Chakraborty, E. A. Spencer
Abstract The total energy transfer from the solar wind to the magnetosphere is governed by the reconnection rate at the magnetosphere edges as the Z‐component of interplanetary magnetic field (IMF B z ) turns southward. The geomagnetic storm on 21–22 January 2005 is considered to be anomalous as the SYM‐H index that signifies the strength of ring current, decreases and had a sustained trough value of −101 nT lasting more than 6 hr under northward IMF B z conditions. In this work, the standard WINDMI model is utilized to estimate the growth and decay of magnetospheric currents by using several solar wind‐magnetosphere coupling functions. However, it is found that the WINDMI model driven by any of these coupling functions is not fully able to explain the decrease of SYM‐H under northward IMF B z . A dense plasma sheet along with signatures of a highly stretched magnetosphere was observed during this storm. The SYM‐H variations during the entire duration of the storm were only reproduced after modifying the WINDMI model to account for the effects of the dense plasma sheet. The limitations of directly driven models relying purely on the solar wind parameters and not accounting for the state of the magnetosphere are highlighted by this work.
当行星际磁场的Z分量(IMF B Z)向南转变时，太阳风向磁层的总能量转移受磁层边缘重联率的控制。2005年1月21日至22日的地磁风暴被认为是异常的，因为在北向的IMF B - z条件下，表征环电流强度的SYM - H指数下降，并且持续槽值为- 101 nT，持续时间超过6小时。在这项工作中，利用标准的WINDMI模型，通过几个太阳风-磁层耦合函数来估计磁层电流的增长和衰减。然而，我们发现由这些耦合函数驱动的WINDMI模型都不能完全解释北移的IMF B z下SYM‐H的减少。在这次风暴中观测到密集的等离子体层以及高度拉伸的磁层的特征。在整个风暴期间的SYM - H变化只有在修改了WINDMI模型以考虑到致密等离子体层的影响后才能重现。直接驱动模式的局限性仅仅依赖于太阳风参数，而不考虑磁层的状态。
{"title":"The Growth of Ring Current/SYM‐H Under Northward IMF <i>B</i><sub><i>z</i></sub> Conditions Present During the 21–22 January 2005 Geomagnetic Storm","authors":"Diptiranjan Rout, S. Patra, S. Kumar, D. Chakrabarty, G. D. Reeves, C. Stolle, K. Pandey, S. Chakraborty, E. A. Spencer","doi":"10.1029/2023sw003489","DOIUrl":"https://doi.org/10.1029/2023sw003489","url":null,"abstract":"Abstract The total energy transfer from the solar wind to the magnetosphere is governed by the reconnection rate at the magnetosphere edges as the Z‐component of interplanetary magnetic field (IMF B z ) turns southward. The geomagnetic storm on 21–22 January 2005 is considered to be anomalous as the SYM‐H index that signifies the strength of ring current, decreases and had a sustained trough value of −101 nT lasting more than 6 hr under northward IMF B z conditions. In this work, the standard WINDMI model is utilized to estimate the growth and decay of magnetospheric currents by using several solar wind‐magnetosphere coupling functions. However, it is found that the WINDMI model driven by any of these coupling functions is not fully able to explain the decrease of SYM‐H under northward IMF B z . A dense plasma sheet along with signatures of a highly stretched magnetosphere was observed during this storm. The SYM‐H variations during the entire duration of the storm were only reproduced after modifying the WINDMI model to account for the effects of the dense plasma sheet. The limitations of directly driven models relying purely on the solar wind parameters and not accounting for the state of the magnetosphere are highlighted by this work.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135810772","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}
Saba Javed, Nazish Rubab, Sadia Zaheer, Stefaan Poedts, Ghulam Jaffer
Abstract Surface charging at geosynchronous altitude is one of the major concerns for satellites and spacecrafts. Spacecraft anomalies are often associated with extreme surface charging events, especially during substorms in which the GEO plasma is better modeled as two temperatures non‐Maxwellian plasma. In such case, we employ two temperature q‐non‐extensive distribution function to determine the onset of spacecraft surface charging which becomes complex since many parameters control the surface charging. We developed a current balance equation which better explains the charging threshold in comparison to a Maxwellian distribution function. The effect of non‐extensive parameters, temperature and density ratio on the current balance equation has been explained. The modified current balance equation predicts the critical and anti‐critical temperatures for various space‐grade materials both analytically and numerically. A significant change is observed in the quantities characterizing the charging current, average yield and density ratio in the presence of non‐extensive two temperature electrons. The mechanism underlying different charging behaviors at or near the threshold is also indicated at various plasma parametric domains. Furthermore, the general conditions of potential jump are also obtained theoretically which predicts the sudden or smooth potential transition.
{"title":"Numerical Calculations of Charging Threshold at GEO Altitudes With Two Temperature Non‐Extensive Electrons","authors":"Saba Javed, Nazish Rubab, Sadia Zaheer, Stefaan Poedts, Ghulam Jaffer","doi":"10.1029/2022sw003412","DOIUrl":"https://doi.org/10.1029/2022sw003412","url":null,"abstract":"Abstract Surface charging at geosynchronous altitude is one of the major concerns for satellites and spacecrafts. Spacecraft anomalies are often associated with extreme surface charging events, especially during substorms in which the GEO plasma is better modeled as two temperatures non‐Maxwellian plasma. In such case, we employ two temperature q‐non‐extensive distribution function to determine the onset of spacecraft surface charging which becomes complex since many parameters control the surface charging. We developed a current balance equation which better explains the charging threshold in comparison to a Maxwellian distribution function. The effect of non‐extensive parameters, temperature and density ratio on the current balance equation has been explained. The modified current balance equation predicts the critical and anti‐critical temperatures for various space‐grade materials both analytically and numerically. A significant change is observed in the quantities characterizing the charging current, average yield and density ratio in the presence of non‐extensive two temperature electrons. The mechanism underlying different charging behaviors at or near the threshold is also indicated at various plasma parametric domains. Furthermore, the general conditions of potential jump are also obtained theoretically which predicts the sudden or smooth potential transition.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136159990","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}
Yuncong Li, Jingnan Guo, Salman Khaksarighiri, Mikhail Igorevich Dobynde, Jian Zhang, Bailiang Liu, Robert F. Wimmer‐Schweingruber
Abstract Astronauts will be facing many risks when they are away from Earth's environment, among which radiation is one of the most vital and troublesome issues. Space radiation exposure from energetic particles of Solar Energetic Particles (SEPs) and Galactic Cosmic Rays (GCRs) can adversely impact the Central Nervous System (CNS) by inducing acute (i.e., mission critical) and chronic (i.e., post‐mission) effects, respectively. Recently, Brain Response Functions (BRFs) based on a realistic brain structure have been developed to model cosmic‐ray induced dose in the brain (Khaksarighiri et al., 2020, https://doi.org/10.1016/j.lssr.2020.07.003 ). In this study, to quantify the radiation induced dose and evaluate the radiation risk to the CNS of the astronauts on the surface of Mars and Moon and in deep space, we use GCR/SEP spectral models together with Mars/Moon radiation transport codes to obtain the radiation field to which astronauts are exposed, and derive the absorbed dose in the brain with BRFs. Our calculations show that GCR induced absorbed dose per month in the brain does not reach the 30‐day limit for CNS (500 mGy) as defined by NASA on either Martian or lunar surface. Based on the spectra and frequency of historical extreme SEP events recorded at Earth as ground‐level enhancement events over past five solar cycles, our results suggest that the CNS of astronauts will be generally “safe” on the Martian surface, but those on the lunar surface or in deep space may face radiation risks in their CNS if not well shielded during such extreme events.
{"title":"The Impact of Space Radiation on Brains of Future Martian and Lunar Explorers","authors":"Yuncong Li, Jingnan Guo, Salman Khaksarighiri, Mikhail Igorevich Dobynde, Jian Zhang, Bailiang Liu, Robert F. Wimmer‐Schweingruber","doi":"10.1029/2023sw003470","DOIUrl":"https://doi.org/10.1029/2023sw003470","url":null,"abstract":"Abstract Astronauts will be facing many risks when they are away from Earth's environment, among which radiation is one of the most vital and troublesome issues. Space radiation exposure from energetic particles of Solar Energetic Particles (SEPs) and Galactic Cosmic Rays (GCRs) can adversely impact the Central Nervous System (CNS) by inducing acute (i.e., mission critical) and chronic (i.e., post‐mission) effects, respectively. Recently, Brain Response Functions (BRFs) based on a realistic brain structure have been developed to model cosmic‐ray induced dose in the brain (Khaksarighiri et al., 2020, https://doi.org/10.1016/j.lssr.2020.07.003 ). In this study, to quantify the radiation induced dose and evaluate the radiation risk to the CNS of the astronauts on the surface of Mars and Moon and in deep space, we use GCR/SEP spectral models together with Mars/Moon radiation transport codes to obtain the radiation field to which astronauts are exposed, and derive the absorbed dose in the brain with BRFs. Our calculations show that GCR induced absorbed dose per month in the brain does not reach the 30‐day limit for CNS (500 mGy) as defined by NASA on either Martian or lunar surface. Based on the spectra and frequency of historical extreme SEP events recorded at Earth as ground‐level enhancement events over past five solar cycles, our results suggest that the CNS of astronauts will be generally “safe” on the Martian surface, but those on the lunar surface or in deep space may face radiation risks in their CNS if not well shielded during such extreme events.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134935511","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}
Abstract The ionosphere is a crucial factor affecting Global Navigation Satellite System positioning. The Global Ionosphere Map (GIM) or the International Reference Ionosphere (IRI) model can be used for regional ionospheric correction. Since southern China is located near the electron density equatorial anomaly, this study evaluates the performance of the Wuhan University GIM (WHU‐GIM) and the IRI‐2020 from 2008 to 2020 over the China region. The comparison indicates that the Total Electron Content (TEC) from IRI‐2020 is lower than that from WHU‐GIM overall, the discrepancy is more obvious in high solar conditions and low‐latitude regions. The differential Slant TEC (dSTEC) during a phase‐arc with about 0.1 TECU accuracy derived from Global Positioning System (GPS) observations is used for model validation, the results show that the accuracies of WHU‐GIM and IRI‐2020 are 3.14 and 4.57 TECU, respectively. The dSTEC error is larger at low latitudes and decreases with increasing latitude. GPS‐derived TEC is taken for reference to evaluate the model reliability. Results show that both models can reproduce the diurnal TEC variations, but IRI‐2020 is more influenced by geomagnetic activities. The TEC correction percentage for IRI‐2020 is about 60%–80% under different ionospheric conditions, while for WHU‐GIM is 80%–90%. The Single‐Frequency Precise Point Positioning is performed with the ionosphere delay corrected by the two models, respectively. The positioning errors show that using IRI‐2020 has a lower accuracy, and the TEC discrepancy of the IRI‐2020 can cause a large bias in the up direction, especially at low‐latitude regions.
{"title":"A Comparison of a GNSS‐GIM and the IRI‐2020 Model Over China Under Different Ionospheric Conditions","authors":"Rong He, Min Li, Qiang Zhang, Qile Zhao","doi":"10.1029/2023sw003646","DOIUrl":"https://doi.org/10.1029/2023sw003646","url":null,"abstract":"Abstract The ionosphere is a crucial factor affecting Global Navigation Satellite System positioning. The Global Ionosphere Map (GIM) or the International Reference Ionosphere (IRI) model can be used for regional ionospheric correction. Since southern China is located near the electron density equatorial anomaly, this study evaluates the performance of the Wuhan University GIM (WHU‐GIM) and the IRI‐2020 from 2008 to 2020 over the China region. The comparison indicates that the Total Electron Content (TEC) from IRI‐2020 is lower than that from WHU‐GIM overall, the discrepancy is more obvious in high solar conditions and low‐latitude regions. The differential Slant TEC (dSTEC) during a phase‐arc with about 0.1 TECU accuracy derived from Global Positioning System (GPS) observations is used for model validation, the results show that the accuracies of WHU‐GIM and IRI‐2020 are 3.14 and 4.57 TECU, respectively. The dSTEC error is larger at low latitudes and decreases with increasing latitude. GPS‐derived TEC is taken for reference to evaluate the model reliability. Results show that both models can reproduce the diurnal TEC variations, but IRI‐2020 is more influenced by geomagnetic activities. The TEC correction percentage for IRI‐2020 is about 60%–80% under different ionospheric conditions, while for WHU‐GIM is 80%–90%. The Single‐Frequency Precise Point Positioning is performed with the ionosphere delay corrected by the two models, respectively. The positioning errors show that using IRI‐2020 has a lower accuracy, and the TEC discrepancy of the IRI‐2020 can cause a large bias in the up direction, especially at low‐latitude regions.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135849001","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}
Xiangning Chu, Jacob Bortnik, Wen Li, Xiao‐Chen Shen, Qianli Ma, Donglai Ma, David Malaspina, Sheng Huang
Abstract Whistler‐mode chorus waves play an essential role in the acceleration and loss of energetic electrons in the Earth’s inner magnetosphere, with the more intense waves producing the most dramatic effects. However, it is challenging to predict the amplitude of strong chorus waves due to the imbalanced nature of the data set, that is, there are many more non‐chorus data points than strong chorus waves. Thus, traditional models usually underestimate chorus wave amplitudes significantly during active times. Using an imbalanced regressive (IR) method, we develop a neural network model of lower‐band (LB) chorus waves using 7‐year observations from the EMFISIS instrument onboard Van Allen Probes. The feature selection process suggests that the auroral electrojet index alone captures most of the variations of chorus waves. The large amplitude of strong chorus waves can be predicted for the first time. Furthermore, our model shows that the equatorial LB chorus’s spatiotemporal evolution is similar to the drift path of substorm‐injected electrons. We also show that the chorus waves have a peak amplitude at the equator in the source MLT near midnight, but toward noon, there is a local minimum in amplitude at the equator with two off‐equator amplitude peaks in both hemispheres, likely caused by the bifurcated drift paths of substorm injections on the dayside. The IR‐based chorus model will improve radiation belt prediction by providing chorus wave distributions, especially storm‐time strong chorus. Since data imbalance is ubiquitous and inherent in space physics and other physical systems, imbalanced regressive methods deserve more attention in space physics.
{"title":"Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression","authors":"Xiangning Chu, Jacob Bortnik, Wen Li, Xiao‐Chen Shen, Qianli Ma, Donglai Ma, David Malaspina, Sheng Huang","doi":"10.1029/2023sw003524","DOIUrl":"https://doi.org/10.1029/2023sw003524","url":null,"abstract":"Abstract Whistler‐mode chorus waves play an essential role in the acceleration and loss of energetic electrons in the Earth’s inner magnetosphere, with the more intense waves producing the most dramatic effects. However, it is challenging to predict the amplitude of strong chorus waves due to the imbalanced nature of the data set, that is, there are many more non‐chorus data points than strong chorus waves. Thus, traditional models usually underestimate chorus wave amplitudes significantly during active times. Using an imbalanced regressive (IR) method, we develop a neural network model of lower‐band (LB) chorus waves using 7‐year observations from the EMFISIS instrument onboard Van Allen Probes. The feature selection process suggests that the auroral electrojet index alone captures most of the variations of chorus waves. The large amplitude of strong chorus waves can be predicted for the first time. Furthermore, our model shows that the equatorial LB chorus’s spatiotemporal evolution is similar to the drift path of substorm‐injected electrons. We also show that the chorus waves have a peak amplitude at the equator in the source MLT near midnight, but toward noon, there is a local minimum in amplitude at the equator with two off‐equator amplitude peaks in both hemispheres, likely caused by the bifurcated drift paths of substorm injections on the dayside. The IR‐based chorus model will improve radiation belt prediction by providing chorus wave distributions, especially storm‐time strong chorus. Since data imbalance is ubiquitous and inherent in space physics and other physical systems, imbalanced regressive methods deserve more attention in space physics.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135567704","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}
Manoj Nair, Rob Redmon, Li‐Yin Young, Arnaud Chulliat, Belinda Trotta, Christine Chung, Greg Lipstein, Isaac Slavitt
Abstract Enhanced interaction between solar‐wind and Earth's magnetosphere can cause space weather and geomagnetic storms that have the potential to damage critical technologies, such as magnetic navigation, radio communications, and power grids. The severity of a geomagnetic storm is measured using the disturbance‐storm‐time ( Dst ) index. The Dst index is calculated by averaging the horizontal component of the magnetic field observed at four near‐equatorial observatories and is used to drive geomagnetic disturbance models. As a key specification of the magnetospheric dynamics, the Dst index is used to drive geomagnetic disturbance models such as the High Definition Geomagnetic Model—Real Time. Since 1975, forecasting models have been proposed to forecast Dst solely from solar wind observations at the Lagrangian‐1 position. However, while the recent Machine‐Learning (ML) models generally perform better than other approaches, many are unsuitable for operational use. Recent exponential growth in data‐science research and the democratization of ML tools have opened up the possibility of crowd‐sourcing specific problem‐solving tasks with clear constraints and evaluation metrics. To this end, National Oceanic and Atmospheric Administration (NOAA)'s National Centers for Environmental Information and the University of Colorado's Cooperative Institute for Research in Environmental Sciences conducted an open data‐science challenge called “MagNet: Model the Geomagnetic Field.” The challenge attracted 622 participants, resulting in 1,197 model submissions that used various ML approaches. The top models that met the evaluation criteria are operationally viable and retrainable and suitable for NOAA's operational needs. The paper summarizes the competition results and lessons learned.
{"title":"MagNet—A Data‐Science Competition to Predict Disturbance Storm‐Time Index (<i>Dst</i>) From Solar Wind Data","authors":"Manoj Nair, Rob Redmon, Li‐Yin Young, Arnaud Chulliat, Belinda Trotta, Christine Chung, Greg Lipstein, Isaac Slavitt","doi":"10.1029/2023sw003514","DOIUrl":"https://doi.org/10.1029/2023sw003514","url":null,"abstract":"Abstract Enhanced interaction between solar‐wind and Earth's magnetosphere can cause space weather and geomagnetic storms that have the potential to damage critical technologies, such as magnetic navigation, radio communications, and power grids. The severity of a geomagnetic storm is measured using the disturbance‐storm‐time ( Dst ) index. The Dst index is calculated by averaging the horizontal component of the magnetic field observed at four near‐equatorial observatories and is used to drive geomagnetic disturbance models. As a key specification of the magnetospheric dynamics, the Dst index is used to drive geomagnetic disturbance models such as the High Definition Geomagnetic Model—Real Time. Since 1975, forecasting models have been proposed to forecast Dst solely from solar wind observations at the Lagrangian‐1 position. However, while the recent Machine‐Learning (ML) models generally perform better than other approaches, many are unsuitable for operational use. Recent exponential growth in data‐science research and the democratization of ML tools have opened up the possibility of crowd‐sourcing specific problem‐solving tasks with clear constraints and evaluation metrics. To this end, National Oceanic and Atmospheric Administration (NOAA)'s National Centers for Environmental Information and the University of Colorado's Cooperative Institute for Research in Environmental Sciences conducted an open data‐science challenge called “MagNet: Model the Geomagnetic Field.” The challenge attracted 622 participants, resulting in 1,197 model submissions that used various ML approaches. The top models that met the evaluation criteria are operationally viable and retrainable and suitable for NOAA's operational needs. The paper summarizes the competition results and lessons learned.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135568488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Caraballo, J. A. González‐Esparza, C. R. Pacheco, P. Corona‐Romero
Abstract We present the first observations of geomagnetically induced currents (GIC) in the Mexican power grid and an improved model to calculate them. The new model comprises ca. 250 substations working at various voltage levels, a methodology to estimate geomagnetic disturbances ( δB ) at different points throughout the Mexican territory, and a 1D piecewise model that considers lateral variations in the ground conductivity. This is an improvement of a former uniform conductivity model presented previously to calculate our first GIC estimates (Caraballo et al., 2020). We compared the observed and calculated GIC between August and November 2021 at a coastal 400 kV substation. During this interval, five geomagnetic storms occurred (G1 and G2). The observed GIC exceeded 10 A during the most strong event; this shows a clear grid response even under weak geomagnetic perturbations that occurred during the solar minimum. Further comparison with the results of the former model suggests that the new 1D piecewise model yields better GIC estimates for the Mexican power grid.
摘要本文首次观测到墨西哥电网的地磁感应电流(GIC)，并提出了一种改进的地磁感应电流计算模型。新模型包括大约250个在不同电压水平下工作的变电站，一种估算墨西哥境内不同地点地磁扰动(δB)的方法，以及一个考虑地面电导率横向变化的一维分段模型。这是对之前提出的用于计算我们的第一个GIC估计的均匀电导率模型的改进(Caraballo et al.， 2020)。我们比较了2021年8月至11月在沿海400千伏变电站观测和计算的GIC。在此期间，共发生了5次地磁风暴(G1和G2)。在最强事件期间，观测到的GIC超过10 A;这显示了一个清晰的网格响应，即使是在太阳极小期发生的微弱地磁扰动下。与前模型结果的进一步比较表明，新的一维分段模型对墨西哥电网产生了更好的GIC估计。
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Abstract Accurate 1‐day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long‐term dependencies. This study develops a highly accurate model for 1‐day global TEC forecasting. We utilized generative TEC data augmentation based on the International Global Navigation Satellite Service (IGS) data set from 1998 to 2017 to enhance the model's prediction ability. Our model takes the TEC sequence of the previous 2 days as input and predicts the global TEC value for each hourly step of the next day. We compared the performance of our model with 1‐day predicted ionospheric products provided by both the Center for Orbit Determination in Europe (C1PG) and Beihang University (B1PG). We proposed a two‐step framework: (a) a time series generative model to produce realistic synthetic TEC data for training, and (b) an auto‐correlation‐based transformer model designed to capture long‐range dependencies in the TEC sequence. Experiments demonstrate that our model significantly improves 1‐day forecast accuracy over prior approaches. On the 2018 benchmark data set, the global root mean squared error (RMSE) of our model is reduced to 1.17 TEC units (TECU), while the RMSE of the C1PG model is 2.07 TECU. Reliability is higher in middle and high latitudes but lower in low latitudes (RMSE < 2.5 TECU), indicating room for improvement. This study highlights the potential of using data augmentation and auto‐correlation‐based transformer models trained on synthetic data to achieve high‐quality 1‐day global TEC forecasting.
{"title":"Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting","authors":"Yuhuan Yuan, Guozhen Xia, Xinmiao Zhang, Chen Zhou","doi":"10.1029/2023sw003472","DOIUrl":"https://doi.org/10.1029/2023sw003472","url":null,"abstract":"Abstract Accurate 1‐day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long‐term dependencies. This study develops a highly accurate model for 1‐day global TEC forecasting. We utilized generative TEC data augmentation based on the International Global Navigation Satellite Service (IGS) data set from 1998 to 2017 to enhance the model's prediction ability. Our model takes the TEC sequence of the previous 2 days as input and predicts the global TEC value for each hourly step of the next day. We compared the performance of our model with 1‐day predicted ionospheric products provided by both the Center for Orbit Determination in Europe (C1PG) and Beihang University (B1PG). We proposed a two‐step framework: (a) a time series generative model to produce realistic synthetic TEC data for training, and (b) an auto‐correlation‐based transformer model designed to capture long‐range dependencies in the TEC sequence. Experiments demonstrate that our model significantly improves 1‐day forecast accuracy over prior approaches. On the 2018 benchmark data set, the global root mean squared error (RMSE) of our model is reduced to 1.17 TEC units (TECU), while the RMSE of the C1PG model is 2.07 TECU. Reliability is higher in middle and high latitudes but lower in low latitudes (RMSE < 2.5 TECU), indicating room for improvement. This study highlights the potential of using data augmentation and auto‐correlation‐based transformer models trained on synthetic data to achieve high‐quality 1‐day global TEC forecasting.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136169143","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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