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。
{"title":"Improved Model for GIC Calculation in the Mexican Power Grid","authors":"R. Caraballo, J. A. González‐Esparza, C. R. Pacheco, P. Corona‐Romero","doi":"10.1029/2022sw003202","DOIUrl":"https://doi.org/10.1029/2022sw003202","url":null,"abstract":"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.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"58 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":"135759985","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 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}
Xiaomin Luo, Zhuang Chen, Shengfeng Gu, Neng Yue, Tao Yue
Abstract Global Positioning System (GPS) Precise Point Positioning (PPP) with correct fixing ambiguity resolution (AR) can reach cm‐mm level positioning accuracy. However, this accuracy can be degraded by the geomagnetic storm effects. To comprehensively investigate the ambiguity resolved percentage (ARP) of GPS kinematic PPP, referred to as PPP‐ARP, under different intensities of geomagnetic storms, based on the Natural Resources Canada's Canadian Spatial Reference System (CSRS) PPP, this study for the first time gives the correlation between the PPP‐ARP and storm intensity using 67 storms occurred in the past 5 years of 2018–2022. Experimental results indicate that the PPP‐ARP decreases gradually as the increase of geomagnetic storm intensity. Under quiet and low geomagnetic conditions (Dst min > −50 nT), the PPP‐ARP of global GNSS stations can achieve more than 96%, while these during strong storms (Dst min ≤ −100 nT) are generally lower than 90.0%, especially for the PPP‐ARP of some stations located at low latitudes which are lower than 40.0%. The mechanism of PPP‐ARP decrease under geomagnetic storms is mainly due to the cycle slips and even loss of lock of GNSS signals caused by the storms induced ionospheric disturbances and scintillations. In addition, different from many previous studies, we found that the CSRS‐PPP with AR can achieve good positioning accuracy (3D RMS <0.2 m) even under strong geomagnetic storms.
{"title":"Studying the Fixing Rate of GPS PPP Ambiguity Resolution Under Different Geomagnetic Storm Intensities","authors":"Xiaomin Luo, Zhuang Chen, Shengfeng Gu, Neng Yue, Tao Yue","doi":"10.1029/2023sw003542","DOIUrl":"https://doi.org/10.1029/2023sw003542","url":null,"abstract":"Abstract Global Positioning System (GPS) Precise Point Positioning (PPP) with correct fixing ambiguity resolution (AR) can reach cm‐mm level positioning accuracy. However, this accuracy can be degraded by the geomagnetic storm effects. To comprehensively investigate the ambiguity resolved percentage (ARP) of GPS kinematic PPP, referred to as PPP‐ARP, under different intensities of geomagnetic storms, based on the Natural Resources Canada's Canadian Spatial Reference System (CSRS) PPP, this study for the first time gives the correlation between the PPP‐ARP and storm intensity using 67 storms occurred in the past 5 years of 2018–2022. Experimental results indicate that the PPP‐ARP decreases gradually as the increase of geomagnetic storm intensity. Under quiet and low geomagnetic conditions (Dst min > −50 nT), the PPP‐ARP of global GNSS stations can achieve more than 96%, while these during strong storms (Dst min ≤ −100 nT) are generally lower than 90.0%, especially for the PPP‐ARP of some stations located at low latitudes which are lower than 40.0%. The mechanism of PPP‐ARP decrease under geomagnetic storms is mainly due to the cycle slips and even loss of lock of GNSS signals caused by the storms induced ionospheric disturbances and scintillations. In addition, different from many previous studies, we found that the CSRS‐PPP with AR can achieve good positioning accuracy (3D RMS <0.2 m) even under strong geomagnetic storms.","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":"136199371","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}
Homayon Aryan, Jacob Bortnik, W. Kent Tobiska, Piyush Mehta, Rashmi Siddalingappa
Abstract It is believed that galactic cosmic rays and solar energetic particles are the two major sources of ionizing radiation. However, the radiation source may also be due to relativistic electrons that are associated with precipitation from the Van Allen radiation belts. In this study, we use Automated Radiation Measurements for Aerospace Safety (ARMAS) measurements to investigate the precipitation mechanism of energetic radiation belt electrons. ARMAS instruments are flown on agency‐sponsored (NASA, National Oceanic and Atmospheric Administration, National Science Foundation, Federal Aviation Administration, DOE) flights, commercial space transportation companies and airliners (>9 km) in automated radiation collection mode. We identified magnetic conjunction events between ARMAS and NASA's Van Allen Probes to study the highly variable, dynamic mesoscale radiation events observed by ARMAS instruments at aviation altitudes and their relationship to various plasma waves in the inner magnetosphere measured by the Van Allen Probes. The results show that there is a strong correlation between dose rates observed by ARMAS and plasmaspheric hiss wave power measured by the Van Allen Probes, but no such relationship with electromagnetic ion cyclotron waves and only a modest correlation with whistler mode chorus waves. These results suggest that the space environment could have a potentially significant effect on passenger safety.
{"title":"Enhanced Radiation Levels at Aviation Altitudes and Their Relationship to Plasma Waves in the Inner Magnetosphere","authors":"Homayon Aryan, Jacob Bortnik, W. Kent Tobiska, Piyush Mehta, Rashmi Siddalingappa","doi":"10.1029/2023sw003477","DOIUrl":"https://doi.org/10.1029/2023sw003477","url":null,"abstract":"Abstract It is believed that galactic cosmic rays and solar energetic particles are the two major sources of ionizing radiation. However, the radiation source may also be due to relativistic electrons that are associated with precipitation from the Van Allen radiation belts. In this study, we use Automated Radiation Measurements for Aerospace Safety (ARMAS) measurements to investigate the precipitation mechanism of energetic radiation belt electrons. ARMAS instruments are flown on agency‐sponsored (NASA, National Oceanic and Atmospheric Administration, National Science Foundation, Federal Aviation Administration, DOE) flights, commercial space transportation companies and airliners (>9 km) in automated radiation collection mode. We identified magnetic conjunction events between ARMAS and NASA's Van Allen Probes to study the highly variable, dynamic mesoscale radiation events observed by ARMAS instruments at aviation altitudes and their relationship to various plasma waves in the inner magnetosphere measured by the Van Allen Probes. The results show that there is a strong correlation between dose rates observed by ARMAS and plasmaspheric hiss wave power measured by the Van Allen Probes, but no such relationship with electromagnetic ion cyclotron waves and only a modest correlation with whistler mode chorus waves. These results suggest that the space environment could have a potentially significant effect on passenger safety.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"25 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":"135963223","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}
Mikhail Kruglyakov, Elena Marshalko, Alexey Kuvshinov, Maxim Smirnov, Ari Viljanen
Abstract We propose a novel approach to model the ground electric field (GEF) induced by laterally‐nonuniform ionospheric sources in real time. The approach exploits the multi‐site transfer function concept, continuous magnetic field measurements at multiple sites in the region of interest, and spatial modes describing the ionospheric source. We compared the modeled GEFs with those measured at two locations in Fennoscandia and observed good agreement between modeled and measured GEF. Besides, we compared GEF‐based geomagnetically induced current (GIC) with that measured at the Mäntsälä natural gas pipeline recording point and again observed remarkable agreement between modeled and measured GIC.
{"title":"Multi‐Site Transfer Function Approach for Real‐Time Modeling of the Ground Electric Field Induced by Laterally‐Nonuniform Ionospheric Source","authors":"Mikhail Kruglyakov, Elena Marshalko, Alexey Kuvshinov, Maxim Smirnov, Ari Viljanen","doi":"10.1029/2023sw003621","DOIUrl":"https://doi.org/10.1029/2023sw003621","url":null,"abstract":"Abstract We propose a novel approach to model the ground electric field (GEF) induced by laterally‐nonuniform ionospheric sources in real time. The approach exploits the multi‐site transfer function concept, continuous magnetic field measurements at multiple sites in the region of interest, and spatial modes describing the ionospheric source. We compared the modeled GEFs with those measured at two locations in Fennoscandia and observed good agreement between modeled and measured GEF. Besides, we compared GEF‐based geomagnetically induced current (GIC) with that measured at the Mäntsälä natural gas pipeline recording point and again observed remarkable agreement between modeled and measured GIC.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"33 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":"136009360","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}
Ya‐fei Shi, Cheng Yang, Jian Wang, Yu Zheng, Fan‐yi Meng, Leonid F. Chernogor
Abstract To achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory. The hybrid model decomposes the hmF2 time data into multiple subsequences through CEEMDAN and reconstructs the subsequences by sample entropy and correlation coefficient into high and low‐frequency sequences, which effectively shortens the calculation time of the model. Then, we determine the optimal hyperparameters of the LSTM models through ISOA, achieving high‐precision forecasting of the hmF2. In single‐step forecasting, the forecasting values of the hybrid model in diurnal and seasonal changes are highly consistent with the observation, which can better capture the severe changes in the hmF2. The model's RMSE, MAE, MAPE, and CC evaluation metrics are 15.86, 11.03 km, 4.76%, and 0.93 in the test set. Compared to IRI, GRU, and LSTM models, taking RMSE as an example, the forecasting accuracy of the models increased by 65.24%, 29.89%, and 29.60%, respectively. In multi‐step forecasting, the proposed model is better at forecasting the changing trend of hmF2, and the forecasting accuracies are significantly better than the IRI model. The data from multiple stations also verified the applicability of the proposed model for hmF2 forecasting. The above results indicate that the hybrid model has high accuracy in hmF2 short‐term forecasting and good applicability in multiple multi‐step forecasting, which can further improve the accurate forecasting of space weather.
{"title":"A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer","authors":"Ya‐fei Shi, Cheng Yang, Jian Wang, Yu Zheng, Fan‐yi Meng, Leonid F. Chernogor","doi":"10.1029/2023sw003581","DOIUrl":"https://doi.org/10.1029/2023sw003581","url":null,"abstract":"Abstract To achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory. The hybrid model decomposes the hmF2 time data into multiple subsequences through CEEMDAN and reconstructs the subsequences by sample entropy and correlation coefficient into high and low‐frequency sequences, which effectively shortens the calculation time of the model. Then, we determine the optimal hyperparameters of the LSTM models through ISOA, achieving high‐precision forecasting of the hmF2. In single‐step forecasting, the forecasting values of the hybrid model in diurnal and seasonal changes are highly consistent with the observation, which can better capture the severe changes in the hmF2. The model's RMSE, MAE, MAPE, and CC evaluation metrics are 15.86, 11.03 km, 4.76%, and 0.93 in the test set. Compared to IRI, GRU, and LSTM models, taking RMSE as an example, the forecasting accuracy of the models increased by 65.24%, 29.89%, and 29.60%, respectively. In multi‐step forecasting, the proposed model is better at forecasting the changing trend of hmF2, and the forecasting accuracies are significantly better than the IRI model. The data from multiple stations also verified the applicability of the proposed model for hmF2 forecasting. The above results indicate that the hybrid model has high accuracy in hmF2 short‐term forecasting and good applicability in multiple multi‐step forecasting, which can further improve the accurate forecasting of space weather.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"36 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":"135849004","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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