On 3 February 2022, 38 satellites launched by SpaceX re-entered the atmosphere and were subsequently destroyed. An investigation found that a minor geomagnetic storm occurred on 3–4 February 2022 led to a neutral density enhancement and large atmospheric drag. To better understand the responses of the thermosphere to geomagnetic storms, the method proposed by Li et al. (2023, https://doi.org/10.1029/2022ja030988) was employed to extract exospheric temperature (Tex) from ionosonde electron density profiles (∼150–200 km) in Beijing (geolocation: 39.56°N; 116.2°E; geomagnetic location: 30.16°N; 172.08°W) station. The retrieved Tex was plugged into the NRLMSISE-00 model to calculate the corresponding neutral density. Derived results showed a ∼2%–7% enhancement in Tex and a ∼15%–38% enhancement in neutral density at 430 km. The relative deviation in neutral density on the satellites’ orbital trajectory ranges from ∼10% (210 km) to ∼35% (500 km) on 3 February, and from ∼13% (210 km) to ∼60% (500 km) on 4 February. Furthermore, the neutral density reproduced the variations observed by the SWARM-C satellite fairly well both on quiet and disturbed days. These results suggest that even a minor geomagnetic storm can cause significant changes in neutral temperature and neutral density at middle latitudes. Additionally, the application of our inversion method, combined with the global, long-term and real-time ionospheric observations from ionosondes, provides an opportunity to improve the capability of thermosphere forecasting and nowcasting.
{"title":"The Daytime Variations of Thermospheric Temperature and Neutral Density Over Beijing During Minor Geomagnetic Storm on 3–4 February 2022","authors":"Shaoyang Li, Zhipeng Ren, Tingting Yu, Guangming Chen, Guozhu Li, Biqiang Zhao, Xinan Yue, Yong Wei","doi":"10.1029/2023sw003677","DOIUrl":"https://doi.org/10.1029/2023sw003677","url":null,"abstract":"On 3 February 2022, 38 satellites launched by SpaceX re-entered the atmosphere and were subsequently destroyed. An investigation found that a minor geomagnetic storm occurred on 3–4 February 2022 led to a neutral density enhancement and large atmospheric drag. To better understand the responses of the thermosphere to geomagnetic storms, the method proposed by Li et al. (2023, https://doi.org/10.1029/2022ja030988) was employed to extract exospheric temperature (Tex) from ionosonde electron density profiles (∼150–200 km) in Beijing (geolocation: 39.56°N; 116.2°E; geomagnetic location: 30.16°N; 172.08°W) station. The retrieved Tex was plugged into the NRLMSISE-00 model to calculate the corresponding neutral density. Derived results showed a ∼2%–7% enhancement in Tex and a ∼15%–38% enhancement in neutral density at 430 km. The relative deviation in neutral density on the satellites’ orbital trajectory ranges from ∼10% (210 km) to ∼35% (500 km) on 3 February, and from ∼13% (210 km) to ∼60% (500 km) on 4 February. Furthermore, the neutral density reproduced the variations observed by the SWARM-C satellite fairly well both on quiet and disturbed days. These results suggest that even a minor geomagnetic storm can cause significant changes in neutral temperature and neutral density at middle latitudes. Additionally, the application of our inversion method, combined with the global, long-term and real-time ionospheric observations from ionosondes, provides an opportunity to improve the capability of thermosphere forecasting and nowcasting.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"4 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139659274","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}
Ionospheric total electron content (TEC) is a key indicator of the space environment. Geophysical forcing from above and below drives its spatial and temporal variations. A full understanding of physical and chemical principles, available and well-representable driving inputs, and capable computational power are required for physical models to reproduce simulations that agree with observations, which may be challenging at times. Recently, data-driven approaches, such as deep learning, have therefore surged as means for TEC prediction. Owing to the fact that the geophysical world possesses a sequential nature in time and space, Transformer architectures are proposed and evaluated for sequence-to-sequence TEC predictions in this study. We discuss the impacts of time lengths of choice during the training process and analyze what the neural network has learned regarding the data sets. Our results suggest that 12-layer, 128-hidden-unit Transformer architectures sufficiently provide multi-step global TEC predictions for 48 hr with an overall root-mean-square error (RMSE) of ∼1.8 TECU. The hourly variation of RMSE increases from 0.6 TECU to about 2.0 TECU during the prediction time frame.
{"title":"Forecasting of Global Ionosphere Maps With Multi-Day Lead Time Using Transformer-Based Neural Networks","authors":"Chung-Yu Shih, Cissi Ying-tsen Lin, Shu-Yu Lin, Cheng-Hung Yeh, Yu-Ming Huang, Feng-Nan Hwang, Chia-Hui Chang","doi":"10.1029/2023sw003579","DOIUrl":"https://doi.org/10.1029/2023sw003579","url":null,"abstract":"Ionospheric total electron content (TEC) is a key indicator of the space environment. Geophysical forcing from above and below drives its spatial and temporal variations. A full understanding of physical and chemical principles, available and well-representable driving inputs, and capable computational power are required for physical models to reproduce simulations that agree with observations, which may be challenging at times. Recently, data-driven approaches, such as deep learning, have therefore surged as means for TEC prediction. Owing to the fact that the geophysical world possesses a sequential nature in time and space, Transformer architectures are proposed and evaluated for sequence-to-sequence TEC predictions in this study. We discuss the impacts of time lengths of choice during the training process and analyze what the neural network has learned regarding the data sets. Our results suggest that 12-layer, 128-hidden-unit Transformer architectures sufficiently provide multi-step global TEC predictions for 48 hr with an overall root-mean-square error (RMSE) of ∼1.8 TECU. The hourly variation of RMSE increases from 0.6 TECU to about 2.0 TECU during the prediction time frame.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"278 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139659287","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}
Angélica M. Castillo, Yuri Y. Shprits, Nikita A. Aseev, Artem Smirnov, Alexander Drozdov, Sebastian Cervantes, Ingo Michaelis, Marina García Peñaranda, Dedong Wang
Low Earth Orbit satellites offer extensive data of the radiation belt region, but utilizing these observations is challenging due to potential contamination and difficulty of intercalibration with spacecraft measurements at Highly Elliptic Orbit that can observe all equatorial pitch-angles. This study introduces a new intercalibration method for satellite measurements of energetic electrons in the radiation belts using a Data assimilation (DA) approach. We demonstrate our technique by intercalibrating the electron flux measurements of the National Oceanic and Atmospheric Administration (NOAA) Polar-orbiting Operational Environmental Satellites (POES) NOAA-15,-16,-17,-18,-19, and MetOp-02 against Van Allen Probes observations from October 2012 to September 2013. We use a reanalysis of the radiation belts obtained by assimilating Van Allen Probes and Geostationary Operational Environmental Satellites observations into 3-D Versatile Electron Radiation Belt (VERB-3D) code simulations via a standard Kalman filter. We compare the reanalysis to the POES data set and estimate the flux ratios at each time, location, and energy. From these ratios, we derive energy and L* dependent recalibration coefficients. To validate our results, we analyze on-orbit conjunctions between POES and Van Allen Probes. The conjunction recalibration coefficients and the data-assimilative estimated coefficients show strong agreement, indicating that the differences between POES and Van Allen Probes observations remain within a factor of two. Additionally, the use of DA allows for improved statistics, as the possible comparisons are increased 10-fold. Data-assimilative intercalibration of satellite observations is an efficient approach that enables intercalibration of large data sets using short periods of data.
低地球轨道卫星提供了辐射带区域的大量数据，但由于潜在的污染以及难以与可观测所有赤道俯仰角的高椭圆轨道航天器测量数据进行相互校准，利用这些观测数据具有挑战性。本研究采用数据同化（DA）方法，为辐射带高能电子卫星测量引入了一种新的相互校准方法。我们将美国国家海洋和大气管理局（NOAA）极轨运行环境卫星（POES）NOAA-15、-16、-17、-18、-19 和 MetOp-02 的电子通量测量数据与范艾伦探测器 2012 年 10 月至 2013 年 9 月的观测数据进行相互校准，以此演示我们的技术。我们使用通过标准卡尔曼滤波器将范艾伦探测器和地球静止业务环境卫星的观测数据同化到三维多功能电子辐射带（VERB-3D）代码模拟中得到的辐射带再分析。我们将再分析结果与 POES 数据集进行比较，并估算出每个时间、地点和能量的通量比。根据这些比率，我们得出了与能量和 L* 有关的重新校准系数。为了验证我们的结果，我们分析了 POES 和范艾伦探测器之间的在轨会合。会合重新校准系数和数据同化估算系数显示出很强的一致性，表明 POES 和 Van Allen Probes 观测结果之间的差异保持在 2 倍以内。此外，由于可能进行的比较增加了 10 倍，因此使用 DA 可以改进统计数据。卫星观测的数据同化相互校准是一种有效的方法，可以利用短期数据对大型数据集进行相互校准。
{"title":"Can We Intercalibrate Satellite Measurements by Means of Data Assimilation? An Attempt on LEO Satellites","authors":"Angélica M. Castillo, Yuri Y. Shprits, Nikita A. Aseev, Artem Smirnov, Alexander Drozdov, Sebastian Cervantes, Ingo Michaelis, Marina García Peñaranda, Dedong Wang","doi":"10.1029/2023sw003624","DOIUrl":"https://doi.org/10.1029/2023sw003624","url":null,"abstract":"Low Earth Orbit satellites offer extensive data of the radiation belt region, but utilizing these observations is challenging due to potential contamination and difficulty of intercalibration with spacecraft measurements at Highly Elliptic Orbit that can observe all equatorial pitch-angles. This study introduces a new intercalibration method for satellite measurements of energetic electrons in the radiation belts using a Data assimilation (DA) approach. We demonstrate our technique by intercalibrating the electron flux measurements of the National Oceanic and Atmospheric Administration (NOAA) Polar-orbiting Operational Environmental Satellites (POES) NOAA-15,-16,-17,-18,-19, and MetOp-02 against Van Allen Probes observations from October 2012 to September 2013. We use a reanalysis of the radiation belts obtained by assimilating Van Allen Probes and Geostationary Operational Environmental Satellites observations into 3-D Versatile Electron Radiation Belt (VERB-3D) code simulations via a standard Kalman filter. We compare the reanalysis to the POES data set and estimate the flux ratios at each time, location, and energy. From these ratios, we derive energy and <i>L</i>* dependent recalibration coefficients. To validate our results, we analyze on-orbit conjunctions between POES and Van Allen Probes. The conjunction recalibration coefficients and the data-assimilative estimated coefficients show strong agreement, indicating that the differences between POES and Van Allen Probes observations remain within a factor of two. Additionally, the use of DA allows for improved statistics, as the possible comparisons are increased 10-fold. Data-assimilative intercalibration of satellite observations is an efficient approach that enables intercalibration of large data sets using short periods of data.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"4 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560049","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}
X. Meng, O. P. Verkhoglyadova, S. C. Chapman, N. W. Watkins, M. Cafolla
Global ionospheric total electron content (TEC) maps exhibit TEC intensifications and depletions of various sizes and shapes. Characterizing key features on TEC maps and understanding their dynamic coupling with external drivers can significantly benefit space weather forecasting. However, comprehensive analysis of ionospheric structuring over decades of TEC maps is currently lacking due to large data volume. We develop feature extraction software based on image processing techniques to extract TEC intensification regions, that is, contiguous regions with sufficiently elevated TEC values than surrounding areas, from global TEC maps. Applying the software to the Jet Propulsion Laboratory Global Ionospheric Map data, we generate a TEC intensification data set for years 2003–2022 and carry out a statistical study on the number and strength of TEC intensifications. We find that the majority of the TEC maps (about 86%) are characterized with one or two intensification(s), while the rest of the TEC maps have three or more intensifications. Both the number and strength of TEC intensifications exhibit semi-annual variation that peaks near equinoxes and dips near solstices, as well as an annual asymmetry with larger values around December solstice compared to June solstice. The number and strength of intensifications increase with enhanced solar extreme-violet irradiance. The strength of intensifications also increases with elevated geomagnetic activity, but the number of intensifications does not. In addition, the number of intensifications is not correlated with the strength of intensifications.
{"title":"Statistical Characteristics of Total Electron Content Intensifications on Global Ionospheric Maps","authors":"X. Meng, O. P. Verkhoglyadova, S. C. Chapman, N. W. Watkins, M. Cafolla","doi":"10.1029/2023sw003695","DOIUrl":"https://doi.org/10.1029/2023sw003695","url":null,"abstract":"Global ionospheric total electron content (TEC) maps exhibit TEC intensifications and depletions of various sizes and shapes. Characterizing key features on TEC maps and understanding their dynamic coupling with external drivers can significantly benefit space weather forecasting. However, comprehensive analysis of ionospheric structuring over decades of TEC maps is currently lacking due to large data volume. We develop feature extraction software based on image processing techniques to extract TEC intensification regions, that is, contiguous regions with sufficiently elevated TEC values than surrounding areas, from global TEC maps. Applying the software to the Jet Propulsion Laboratory Global Ionospheric Map data, we generate a TEC intensification data set for years 2003–2022 and carry out a statistical study on the number and strength of TEC intensifications. We find that the majority of the TEC maps (about 86%) are characterized with one or two intensification(s), while the rest of the TEC maps have three or more intensifications. Both the number and strength of TEC intensifications exhibit semi-annual variation that peaks near equinoxes and dips near solstices, as well as an annual asymmetry with larger values around December solstice compared to June solstice. The number and strength of intensifications increase with enhanced solar extreme-violet irradiance. The strength of intensifications also increases with elevated geomagnetic activity, but the number of intensifications does not. In addition, the number of intensifications is not correlated with the strength of intensifications.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"11 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139552962","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}
Pengdong Gao, Jinhui Cai, Zheng Wang, Chu Qiu, Guojun Wang, Quan Qi, Bo Wang, Jiankui Shi, Xiao Wang, Kai Ding
An intelligent high-definition and short-term prediction of ionograms with/without Spread-F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio-temporal ConvGRU network and a super-resolution EDSR network. Our prediction is based on spatio-temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super-parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread-F.
{"title":"Prediction of Ionograms With/Without Spread-F at Hainan by a Combined Spatio-Temporal Neural Network","authors":"Pengdong Gao, Jinhui Cai, Zheng Wang, Chu Qiu, Guojun Wang, Quan Qi, Bo Wang, Jiankui Shi, Xiao Wang, Kai Ding","doi":"10.1029/2023sw003727","DOIUrl":"https://doi.org/10.1029/2023sw003727","url":null,"abstract":"An intelligent high-definition and short-term prediction of ionograms with/without Spread-F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio-temporal ConvGRU network and a super-resolution EDSR network. Our prediction is based on spatio-temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super-parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread-F.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"27 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139553181","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}
Predicting the impacts of coronal mass ejections (CMEs) is a major focus of current space weather forecasting efforts. Typically, CME properties are reconstructed from stereoscopic coronal images and then used to forward model a CME's interplanetary evolution. Knowing the uncertainty in the coronal reconstructions is then a critical factor in determining the uncertainty of any predictions. A growing number of catalogs of coronal CME reconstructions exist, but no extensive comparison between these catalogs has yet been performed. Here we develop a Living List of Attributes Measured in Any Coronal Reconstruction (LLAMACoRe), an online collection of individual catalogs, which we intend to continually update. In this first version, we use results from 24 different catalogs with 3D reconstructions using Solar Terrestrial Relations Observatory observations between 2007 and 2014. We have collated the individual catalogs, determining which reconstructions correspond to the same events. LLAMACoRe contains 2,954 reconstructions for 1,862 CMEs. Of these, 511 CMEs contain multiple reconstructions from different catalogs. Using the best-constrained values for each CME, we find that the combined catalog reproduces the generally known solar cycle trends. We determine the typical difference we would expect between two independent reconstructions of the same event and find values of 4.0° in the latitude, 8.0° in the longitude, 24.0° in the tilt, 9.3° in the angular width, 0.1 in the shape parameter κ, 115 km/s in the velocity, and 2.5 × 1015 g in the mass. These remain the most probable values over the solar cycle, though we find more extreme outliers in the deviation toward solar maximum.
{"title":"Collection, Collation, and Comparison of 3D Coronal CME Reconstructions","authors":"C. Kay, E. Palmerio","doi":"10.1029/2023sw003796","DOIUrl":"https://doi.org/10.1029/2023sw003796","url":null,"abstract":"Predicting the impacts of coronal mass ejections (CMEs) is a major focus of current space weather forecasting efforts. Typically, CME properties are reconstructed from stereoscopic coronal images and then used to forward model a CME's interplanetary evolution. Knowing the uncertainty in the coronal reconstructions is then a critical factor in determining the uncertainty of any predictions. A growing number of catalogs of coronal CME reconstructions exist, but no extensive comparison between these catalogs has yet been performed. Here we develop a Living List of Attributes Measured in Any Coronal Reconstruction (LLAMACoRe), an online collection of individual catalogs, which we intend to continually update. In this first version, we use results from 24 different catalogs with 3D reconstructions using Solar Terrestrial Relations Observatory observations between 2007 and 2014. We have collated the individual catalogs, determining which reconstructions correspond to the same events. LLAMACoRe contains 2,954 reconstructions for 1,862 CMEs. Of these, 511 CMEs contain multiple reconstructions from different catalogs. Using the best-constrained values for each CME, we find that the combined catalog reproduces the generally known solar cycle trends. We determine the typical difference we would expect between two independent reconstructions of the same event and find values of 4.0° in the latitude, 8.0° in the longitude, 24.0° in the tilt, 9.3° in the angular width, 0.1 in the shape parameter <i>κ</i>, 115 km/s in the velocity, and 2.5 × 10<sup>15</sup> g in the mass. These remain the most probable values over the solar cycle, though we find more extreme outliers in the deviation toward solar maximum.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"28 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560051","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}
In this work, a model for calculating the galactic cosmic rays (GCRs) radiation dose rate is developed. The model is based on a GCR modulation model, which is established by Shen and Qin, and the fluence-dose conversion coefficients (FDCCs) published by the International Commission on Radiological Protection (ICRP). With the model, the radiation absorbed dose rate of GCRs near the lunar surface over long time periods is calculated and compared with the observation data from the Cosmic Ray Telescope for the Effects of Radiation and the Lunar Lander Neutron and Dosimetry. First, the energy spectrum of GCRs at 1 AU in the ecliptic, where the lunar orbit is located, is computed using the GCR modulation model. Then, using the FDCCs of ICRP 123, the absorbed dose rates of 15 human organs/tissues at the lunar orbit position are calculated to represent the general absorbed dose rate of the body (in water). Furthermore, considering the albedo radiation (excluding neutrons) and using the water-silicon conversion coefficients, the total absorbed dose rates of GCRs near the lunar surface (in silicon) are calculated, it is shown that our modeling results generally agree with the observations from spacecraft. This work is useful for future manned space exploration to the Moon or other celestial bodies in the solar system.
{"title":"Long-Term Variation of the Galactic Cosmic Ray Radiation Dose Rates","authors":"D. Lyu, G. Qin, Z.-N. Shen","doi":"10.1029/2023sw003804","DOIUrl":"https://doi.org/10.1029/2023sw003804","url":null,"abstract":"In this work, a model for calculating the galactic cosmic rays (GCRs) radiation dose rate is developed. The model is based on a GCR modulation model, which is established by Shen and Qin, and the fluence-dose conversion coefficients (FDCCs) published by the International Commission on Radiological Protection (ICRP). With the model, the radiation absorbed dose rate of GCRs near the lunar surface over long time periods is calculated and compared with the observation data from the Cosmic Ray Telescope for the Effects of Radiation and the Lunar Lander Neutron and Dosimetry. First, the energy spectrum of GCRs at 1 AU in the ecliptic, where the lunar orbit is located, is computed using the GCR modulation model. Then, using the FDCCs of ICRP 123, the absorbed dose rates of 15 human organs/tissues at the lunar orbit position are calculated to represent the general absorbed dose rate of the body (in water). Furthermore, considering the albedo radiation (excluding neutrons) and using the water-silicon conversion coefficients, the total absorbed dose rates of GCRs near the lunar surface (in silicon) are calculated, it is shown that our modeling results generally agree with the observations from spacecraft. This work is useful for future manned space exploration to the Moon or other celestial bodies in the solar system.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560048","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}
Xiaoyue Li, Senthamizh Pavai Valliappan, Daria Shukhobodskaia, Mark D. Butala, Luciano Rodriguez, Jasmina Magdalenic, Véronique Delouille
Current magnetohydrodynamics (MHD) models largely rely on synoptic magnetograms, such as the ones produced by the Global Oscillation Network Group (GONG). Magnetograms are currently available mostly from the front side of the Sun, which significantly reduces the accuracy of MHD modeling. Extreme Ultraviolet (EUV) images can instead be obtained from other vantage points. To investigate the potential, we explore the possibility of using EUV information from the Atmospheric Imaging Assembly (AIA) to directly generate the input for the state-of-the-art 3D MHD model European Heliospheric FORecasting Information Asset (EUHFORIA). Toward this goal, we develop a method called Transfer-Solar-GAN which combines a conditional generative adversarial network with a transfer learning approach to overcome training data set limitations. The source domain data set is constructed from multiple pairs of the central portion of co-registered AIA and Helioseismic and Magnetic Imager (HMI) line of sight (LOS) full-disk images, while the target domain is constructed from pairs of portions of AIA and GONG sine-latitude synoptic maps that we call segments. We evaluate Transfer-Solar-GAN by comparing modeled and measured solar wind velocity and magnetic field density parameters at the L1 Lagrange point and along the Parker Solar Probe (PSP) trajectory which were determined with EUHFORIA using both empirical GONG and artificial-intelligence (AI)-synthetic synoptic magnetograms as inputs. Our results demonstrate that the Transfer-Solar-GAN model can provide the necessary information to run solar physics models by EUV information. Our proposed model is trained with only 528 paired image segments and enforces a reliable data division strategy.
{"title":"A Transfer Learning Method to Generate Synthetic Synoptic Magnetograms","authors":"Xiaoyue Li, Senthamizh Pavai Valliappan, Daria Shukhobodskaia, Mark D. Butala, Luciano Rodriguez, Jasmina Magdalenic, Véronique Delouille","doi":"10.1029/2023sw003499","DOIUrl":"https://doi.org/10.1029/2023sw003499","url":null,"abstract":"Current magnetohydrodynamics (MHD) models largely rely on synoptic magnetograms, such as the ones produced by the Global Oscillation Network Group (GONG). Magnetograms are currently available mostly from the front side of the Sun, which significantly reduces the accuracy of MHD modeling. Extreme Ultraviolet (EUV) images can instead be obtained from other vantage points. To investigate the potential, we explore the possibility of using EUV information from the Atmospheric Imaging Assembly (AIA) to directly generate the input for the state-of-the-art 3D MHD model European Heliospheric FORecasting Information Asset (EUHFORIA). Toward this goal, we develop a method called Transfer-Solar-GAN which combines a conditional generative adversarial network with a transfer learning approach to overcome training data set limitations. The source domain data set is constructed from multiple pairs of the central portion of co-registered AIA and Helioseismic and Magnetic Imager (HMI) line of sight (LOS) full-disk images, while the target domain is constructed from pairs of portions of AIA and GONG sine-latitude synoptic maps that we call <i>segments</i>. We evaluate Transfer-Solar-GAN by comparing modeled and measured solar wind velocity and magnetic field density parameters at the <i>L</i><sub>1</sub> Lagrange point and along the Parker Solar Probe (PSP) trajectory which were determined with EUHFORIA using both empirical GONG and artificial-intelligence (AI)-synthetic synoptic magnetograms as inputs. Our results demonstrate that the Transfer-Solar-GAN model can provide the necessary information to run solar physics models by EUV information. Our proposed model is trained with only 528 paired image segments and enforces a reliable data division strategy.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"204 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139517501","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}
Alison Moraes, Jonas Sousasantos, Emanoel Costa, Bruno Augusto Pereira, Fabiano Rodrigues, João Francisco Galera Monico
Signals recorded by two stations in the Brazilian region: [Fortaleza (3.74°S, 38.57°W) and Inconfidentes (22.31°S, 46.32°W)], receiving L1 transmissions from different geostationary satellites, were used to evaluate the amplitude scintillation index S4 and several characteristics of scintillation events (continuous record with S4 > 0.2) during nighttime hours (18:00 LT–02:00 LT) in the years 2014–2016. The effects from solar activity, season, and local time on the number of scintillation events per night, maximum scintillation, scintillation event duration, and spacing between consecutive events will be discussed. The results indicate that: (a) scintillation occurs from September to March in both links; (b) the most likely numbers of observed scintillation events per night were two or three, particularly during the first 2 years; (c) on average, the first scintillation event usually had larger maximum S4 values when compared to those of the later ones along the night; (d) the first scintillation event had a longer mean duration than the succeeding ones in a given night; (e) the durations of scintillation events, regardless of their numbers per night and the location, decreased with local time; (f) the opposite dependence of spacings between consecutive events on local time was observed; (g) the cumulative distribution functions of the scintillation onset time indicated a strong dependence on the dip latitude of the station; and (h) early occurrences of onset times are directly related to the increased probability of the occurrence of multiple scintillation events.
{"title":"Characterization of Scintillation Events With Basis on L1 Transmissions From Geostationary SBAS Satellites","authors":"Alison Moraes, Jonas Sousasantos, Emanoel Costa, Bruno Augusto Pereira, Fabiano Rodrigues, João Francisco Galera Monico","doi":"10.1029/2023sw003656","DOIUrl":"https://doi.org/10.1029/2023sw003656","url":null,"abstract":"Signals recorded by two stations in the Brazilian region: [Fortaleza (3.74°S, 38.57°W) and Inconfidentes (22.31°S, 46.32°W)], receiving L1 transmissions from different geostationary satellites, were used to evaluate the amplitude scintillation index <i>S</i><sub>4</sub> and several characteristics of scintillation events (continuous record with <i>S</i><sub>4</sub> > 0.2) during nighttime hours (18:00 LT–02:00 LT) in the years 2014–2016. The effects from solar activity, season, and local time on the number of scintillation events per night, maximum scintillation, scintillation event duration, and spacing between consecutive events will be discussed. The results indicate that: (a) scintillation occurs from September to March in both links; (b) the most likely numbers of observed scintillation events per night were two or three, particularly during the first 2 years; (c) on average, the first scintillation event usually had larger maximum <i>S</i><sub>4</sub> values when compared to those of the later ones along the night; (d) the first scintillation event had a longer mean duration than the succeeding ones in a given night; (e) the durations of scintillation events, regardless of their numbers per night and the location, decreased with local time; (f) the opposite dependence of spacings between consecutive events on local time was observed; (g) the cumulative distribution functions of the scintillation onset time indicated a strong dependence on the dip latitude of the station; and (h) early occurrences of onset times are directly related to the increased probability of the occurrence of multiple scintillation events.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"56 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139517337","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 F10.7 solar radiation flux is a well-known parameter that is closely linked to solar activity, serving as a key index for measuring the level of solar activity. In this study, the Variational Mode Decomposition (VMD) and Long Short-term Memory (LSTM) network are combined to construct a VMD-LSTM model for predicting F10.7 values. The F10.7 sequence is decomposed into several intrinsic mode functions (IMF) by VMD, then the LSTM neural network is utilized to forecast each IMF. All IMF prediction results are aggregated to obtain the final F10.7 value. The data sets from 1957 to 2008 are used for training and the data sets from 2009 to 2019 are used for testing. The results show that the VMD-LSTM model achieves an annual average root mean square error of only 4.47 sfu and an annual average correlation coefficient (R) of 0.99 during solar cycle 24, which is significantly better than the accuracy of the LSTM model (W. Zhang et al., 2022, https://doi.org/10.3390/universe8010030), the AR model (Du, 2020, https://doi.org/10.1007/s11207-020-01689-x), and the BP model (Xiao et al., 2017, https://doi.org/10.11728/cjss2017.01.001). The VMD-LSTM model exhibits strong predictive capability for the F10.7 index during solar cycle 24.
{"title":"F10.7 Daily Forecast Using LSTM Combined With VMD Method","authors":"Yuhang Hao, Jianyong Lu, Guangshuai Peng, Ming Wang, Jingyuan Li, Guanchun Wei","doi":"10.1029/2023sw003552","DOIUrl":"https://doi.org/10.1029/2023sw003552","url":null,"abstract":"The <i>F</i><sub>10.7</sub> solar radiation flux is a well-known parameter that is closely linked to solar activity, serving as a key index for measuring the level of solar activity. In this study, the Variational Mode Decomposition (VMD) and Long Short-term Memory (LSTM) network are combined to construct a VMD-LSTM model for predicting <i>F</i><sub>10.7</sub> values. The <i>F</i><sub>10.7</sub> sequence is decomposed into several intrinsic mode functions (IMF) by VMD, then the LSTM neural network is utilized to forecast each IMF. All IMF prediction results are aggregated to obtain the final <i>F</i><sub>10.7</sub> value. The data sets from 1957 to 2008 are used for training and the data sets from 2009 to 2019 are used for testing. The results show that the VMD-LSTM model achieves an annual average root mean square error of only 4.47 sfu and an annual average correlation coefficient (<i>R</i>) of 0.99 during solar cycle 24, which is significantly better than the accuracy of the LSTM model (W. Zhang et al., 2022, https://doi.org/10.3390/universe8010030), the AR model (Du, 2020, https://doi.org/10.1007/s11207-020-01689-x), and the BP model (Xiao et al., 2017, https://doi.org/10.11728/cjss2017.01.001). The VMD-LSTM model exhibits strong predictive capability for the <i>F</i><sub>10.7</sub> index during solar cycle 24.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139496084","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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