E. Yordanova, M. Temmer, M. Dumbović, C. Scolini, E. Paouris, A. L. E. Werner, A. P. Dimmock, L. Sorriso-Valvo
The propagation of geoeffective fast halo coronal mass ejections (CMEs) from solar cycle 24 has been investigated using the European Heliospheric Forecasting Information Asset (EUHFORIA), ENLIL, Drag-Based Model (DBM) and Effective Acceleration Model (EAM) models. For an objective comparison, a unified set of a small sample of CME events with similar characteristics has been selected. The same CME kinematic parameters have been used as input in the propagation models to compare their predicted arrival times and the speed of the interplanetary (IP) shocks associated with the CMEs. The performance assessment has been based on the application of an identical set of metrics. First, the modeling of the events has been done with default input concerning the background solar wind, as would be used in operations. The obtained CME arrival forecast deviates from the observations at L1, with a general underestimation of the arrival time and overestimation of the impact speed (mean absolute error [MAE]: 9.8 ± 1.8–14.6 ± 2.3 hr and 178 ± 22–376 ± 54 km/s). To address this discrepancy, we refine the models by simple changes of the density ratio (dcld) between the CME and IP space in the numerical, and the IP drag (γ) in the analytical models. This approach resulted in a reduced MAE in the forecast for the arrival time of 8.6 ± 2.2–13.5 ± 2.2 hr and the impact speed of 51 ± 6–243 ± 45 km/s. In addition, we performed multi-CME runs to simulate potential interactions. This leads, to even larger uncertainties in the forecast. Based on this study we suggest simple adjustments in the operational settings for improving the forecast of fast halo CMEs.
{"title":"Refined Modeling of Geoeffective Fast Halo CMEs During Solar Cycle 24","authors":"E. Yordanova, M. Temmer, M. Dumbović, C. Scolini, E. Paouris, A. L. E. Werner, A. P. Dimmock, L. Sorriso-Valvo","doi":"10.1029/2023sw003497","DOIUrl":"https://doi.org/10.1029/2023sw003497","url":null,"abstract":"The propagation of geoeffective fast halo coronal mass ejections (CMEs) from solar cycle 24 has been investigated using the European Heliospheric Forecasting Information Asset (EUHFORIA), ENLIL, Drag-Based Model (DBM) and Effective Acceleration Model (EAM) models. For an objective comparison, a unified set of a small sample of CME events with similar characteristics has been selected. The same CME kinematic parameters have been used as input in the propagation models to compare their predicted arrival times and the speed of the interplanetary (IP) shocks associated with the CMEs. The performance assessment has been based on the application of an identical set of metrics. First, the modeling of the events has been done with default input concerning the background solar wind, as would be used in operations. The obtained CME arrival forecast deviates from the observations at L1, with a general underestimation of the arrival time and overestimation of the impact speed (mean absolute error [MAE]: 9.8 ± 1.8–14.6 ± 2.3 hr and 178 ± 22–376 ± 54 km/s). To address this discrepancy, we refine the models by simple changes of the density ratio (<i>dcld</i>) between the CME and IP space in the numerical, and the IP drag (<i>γ</i>) in the analytical models. This approach resulted in a reduced MAE in the forecast for the arrival time of 8.6 ± 2.2–13.5 ± 2.2 hr and the impact speed of 51 ± 6–243 ± 45 km/s. In addition, we performed multi-CME runs to simulate potential interactions. This leads, to even larger uncertainties in the forecast. Based on this study we suggest simple adjustments in the operational settings for improving the forecast of fast halo CMEs.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"206 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139496117","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, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (Es), demonstrating an improvement over current methods. This includes a binary classification model to determine if Es is present, followed by a regression model to estimate the Es ordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which the Es layer occurs (hEs). Signal-to-noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the height (hEs) values are obtained from the global network of ground-based Digisonde ionosondes and are used as the “ground truth,” or target variables, during training. After corresponding the two data sets, a total of 36,521 samples are available for training and testing the models. The foEs CNN binary classification model achieved an accuracy of 74% and F1-score of 0.70. Mean absolute errors (MAE) of 0.63 MHz and 5.81 km along with root-mean squared errors (RMSE) of 0.95 MHz and 7.89 km were attained for estimating foEs and hEs, respectively, when it was known that Es was present. When combining the classification and regression models together for use in practical applications where it is unknown if Es is present, an foEs MAE and RMSE of 0.97 and 1.65 MHz, respectively, were realized. We implemented three other techniques for sporadic E characterization, and found that the CNN model appears to perform better.
{"title":"Detection and Classification of Sporadic E Using Convolutional Neural Networks","authors":"J. A. Ellis, D. J. Emmons, M. B. Cohen","doi":"10.1029/2023sw003669","DOIUrl":"https://doi.org/10.1029/2023sw003669","url":null,"abstract":"In this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (<i>E</i><sub><i>s</i></sub>), demonstrating an improvement over current methods. This includes a binary classification model to determine if <i>E</i><sub><i>s</i></sub> is present, followed by a regression model to estimate the <i>E</i><sub><i>s</i></sub> ordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which the <i>E</i><sub><i>s</i></sub> layer occurs (hEs). Signal-to-noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the height (hEs) values are obtained from the global network of ground-based Digisonde ionosondes and are used as the “ground truth,” or target variables, during training. After corresponding the two data sets, a total of 36,521 samples are available for training and testing the models. The foEs CNN binary classification model achieved an accuracy of 74% and F1-score of 0.70. Mean absolute errors (MAE) of 0.63 MHz and 5.81 km along with root-mean squared errors (RMSE) of 0.95 MHz and 7.89 km were attained for estimating foEs and hEs, respectively, when it was known that <i>E</i><sub><i>s</i></sub> was present. When combining the classification and regression models together for use in practical applications where it is unknown if <i>E</i><sub><i>s</i></sub> is present, an foEs MAE and RMSE of 0.97 and 1.65 MHz, respectively, were realized. We implemented three other techniques for sporadic E characterization, and found that the CNN model appears to perform better.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"5 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458790","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}
A. W. Smith, C. J. Rodger, D. H. Mac Manus, I. J. Rae, A. R. Fogg, C. Forsyth, P. Fisher, T. Petersen, M. Dalzell
Changes in the Earth's geomagnetic field induce geoelectric fields in the solid Earth. These electric fields drive Geomagnetically Induced Currents (GICs) in grounded, conducting infrastructure. These GICs can damage or degrade equipment if they are sufficiently intense—understanding and forecasting them is of critical importance. One of the key magnetospheric phenomena are Sudden Commencements (SCs). To examine the potential impact of SCs we evaluate the correlation between the measured maximum GICs and rate of change of the magnetic field (H′) in 75 power grid transformers across New Zealand between 2001 and 2020. The maximum observed H′ and GIC correlate well, with correlation coefficients (r2) around 0.7. We investigate the gradient of the relationship between H′ and GIC, finding a hot spot close to Dunedin: where a given H′ will drive the largest relative current (0.5 A nT−1 min). We observe strong intralocation variability, with the gradients varying by a factor of two or more at adjacent transformers. We find that GICs are (on average) greater if they are related to: (a) Storm Sudden Commencements (SSCs; 27% larger than Sudden Impulses, SIs); (b) SCs while New Zealand is on the dayside of the Earth (27% larger than the nightside); and (c) SCs with a predominantly East-West magnetic field change (14% larger than North-South equivalents). These results are attributed to the geology of New Zealand and the geometry of the power network. We extrapolate to find that transformers near Dunedin would see 2000 A or more during a theoretical extreme SC (H′ = 4000 nT min−1).
{"title":"Sudden Commencements and Geomagnetically Induced Currents in New Zealand: Correlations and Dependance","authors":"A. W. Smith, C. J. Rodger, D. H. Mac Manus, I. J. Rae, A. R. Fogg, C. Forsyth, P. Fisher, T. Petersen, M. Dalzell","doi":"10.1029/2023sw003731","DOIUrl":"https://doi.org/10.1029/2023sw003731","url":null,"abstract":"Changes in the Earth's geomagnetic field induce geoelectric fields in the solid Earth. These electric fields drive Geomagnetically Induced Currents (GICs) in grounded, conducting infrastructure. These GICs can damage or degrade equipment if they are sufficiently intense—understanding and forecasting them is of critical importance. One of the key magnetospheric phenomena are Sudden Commencements (SCs). To examine the potential impact of SCs we evaluate the correlation between the measured maximum GICs and rate of change of the magnetic field (<i>H</i>′) in 75 power grid transformers across New Zealand between 2001 and 2020. The maximum observed <i>H</i>′ and GIC correlate well, with correlation coefficients (<i>r</i><sup>2</sup>) around 0.7. We investigate the gradient of the relationship between <i>H</i>′ and GIC, finding a hot spot close to Dunedin: where a given <i>H</i>′ will drive the largest relative current (0.5 A nT<sup>−1</sup> min). We observe strong intralocation variability, with the gradients varying by a factor of two or more at adjacent transformers. We find that GICs are (on average) greater if they are related to: (a) Storm Sudden Commencements (SSCs; 27% larger than Sudden Impulses, SIs); (b) SCs while New Zealand is on the dayside of the Earth (27% larger than the nightside); and (c) SCs with a predominantly East-West magnetic field change (14% larger than North-South equivalents). These results are attributed to the geology of New Zealand and the geometry of the power network. We extrapolate to find that transformers near Dunedin would see 2000 A or more during a theoretical extreme SC (<i>H</i>′ = 4000 nT min<sup>−1</sup>).","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"21 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139411322","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}
A new one-dimensional variational (1D-Var) retrieval method for ionospheric GNSS radio occultation (GNSS-RO) measurements is described. The forward model implicit in the retrieval calculates the bending angles produced by a one-dimensional ionospheric electron density profile, modeled with multiple “Vary-Chap” layers. It is demonstrated that gradient based minimization techniques can be applied to this retrieval problem. The use of ionospheric bending angles is discussed. This approach circumvents the need for Differential Code Bias (DCB) estimates when using the measurements. This new, general retrieval method is applicable to both standard GNSS-RO retrieval problems, and the truncated geometry of EUMETSAT's Metop Second Generation (Metop-SG), which will provide GNSS-RO measurements up to about 600 km above the surface. The climatological a priori information used in the 1D-Var is effectively a starting point for the 1D-Var minimization, rather than a strong constraint on the final solution. In this paper the approach has been tested with 143 COSMIC-1 measurements. We find that the method converges in 135 of the cases, but around 25 of those have high “cost at convergence” values. In the companion paper (Elvidge et al., 2023), a full statistical analysis of the method, using over 10,000 COSMIC-2 measurements, has been made.
{"title":"One-Dimensional Variational Ionospheric Retrieval Using Radio Occultation Bending Angles: 1. Theory","authors":"I. D. Culverwell, S. B. Healy, S. Elvidge","doi":"10.1029/2023sw003572","DOIUrl":"https://doi.org/10.1029/2023sw003572","url":null,"abstract":"A new one-dimensional variational (1D-Var) retrieval method for ionospheric GNSS radio occultation (GNSS-RO) measurements is described. The forward model implicit in the retrieval calculates the bending angles produced by a one-dimensional ionospheric electron density profile, modeled with multiple “Vary-Chap” layers. It is demonstrated that gradient based minimization techniques can be applied to this retrieval problem. The use of ionospheric bending angles is discussed. This approach circumvents the need for Differential Code Bias (DCB) estimates when using the measurements. This new, general retrieval method is applicable to both standard GNSS-RO retrieval problems, and the truncated geometry of EUMETSAT's Metop Second Generation (Metop-SG), which will provide GNSS-RO measurements up to about 600 km above the surface. The climatological a priori information used in the 1D-Var is effectively a starting point for the 1D-Var minimization, rather than a strong constraint on the final solution. In this paper the approach has been tested with 143 COSMIC-1 measurements. We find that the method converges in 135 of the cases, but around 25 of those have high “cost at convergence” values. In the companion paper (Elvidge et al., 2023), a full statistical analysis of the method, using over 10,000 COSMIC-2 measurements, has been made.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"22 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139411527","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}
Culverwell et al. (2023, https://doi.org/10.22541/essoar.168614409.98641332) described a new one-dimensional variational (1D-Var) retrieval approach for ionospheric GNSS radio occultation (GNSS-RO) measurements. The approach maps a one-dimensional ionospheric electron density profile, modeled with multiple “Vary-Chap” layers, to bending angle space. This paper improves the computational performance of the 1D-Var retrieval using an improved background model and validates the approach by comparing with the COSMIC-2 profile retrievals, based on an Abel Transform inversion, and co-located (within 200 km) ionosonde observations using all suitable data from 2020. A three or four layer Vary-Chap in the 1D-Var retrieval shows improved performance compared to COSMIC-2 retrievals in terms of percentage error for the F2 peak parameters (NmF2 and hmF2). Furthermore, skill in retrieval (compared to COSMIC-2 profiles) throughout the bottomside (∼90–300 km) has been demonstrated. With a single Vary-Chap layer the performance is similar, but this improves by approximately 40% when using four-layers.
{"title":"One-Dimensional Variational Ionospheric Retrieval Using Radio Occultation Bending Angles: 2. Validation","authors":"S. Elvidge, S. B. Healy, I. D. Culverwell","doi":"10.1029/2023sw003571","DOIUrl":"https://doi.org/10.1029/2023sw003571","url":null,"abstract":"Culverwell et al. (2023, https://doi.org/10.22541/essoar.168614409.98641332) described a new one-dimensional variational (1D-Var) retrieval approach for ionospheric GNSS radio occultation (GNSS-RO) measurements. The approach maps a one-dimensional ionospheric electron density profile, modeled with multiple “Vary-Chap” layers, to bending angle space. This paper improves the computational performance of the 1D-Var retrieval using an improved background model and validates the approach by comparing with the COSMIC-2 profile retrievals, based on an Abel Transform inversion, and co-located (within 200 km) ionosonde observations using all suitable data from 2020. A three or four layer Vary-Chap in the 1D-Var retrieval shows improved performance compared to COSMIC-2 retrievals in terms of percentage error for the F2 peak parameters (NmF2 and hmF2). Furthermore, skill in retrieval (compared to COSMIC-2 profiles) throughout the bottomside (∼90–300 km) has been demonstrated. With a single Vary-Chap layer the performance is similar, but this improves by approximately 40% when using four-layers.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"28 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139411523","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}
F. Enengl, L. Spogli, D. Kotova, Y. Jin, K. Oksavik, N. Partamies, W. J. Miloch
We investigate the role of auroral particle precipitation in small-scale (below hundreds of meters) plasma structuring in the auroral ionosphere over the Arctic. In this scope, we analyze together data recorded by an Ionospheric Scintillation Monitor Receiver (ISMR) of Global Navigation Satellite System (GNSS) signals and by an All-Sky Imager located in Longyearbyen, Svalbard (Norway). We leverage on the raw GNSS samples provided at 50 Hz by the ISMR to evaluate amplitude and phase scintillation indices at 1 s time resolution and the Ionosphere-Free Linear Combination at 20 ms time resolution. The simultaneous use of the 1 s GNSS-based scintillation indices allows identifying the scale size of the irregularities involved in plasma structuring in the range of small (up to few hundreds of meters) and medium-scale size ranges (up to few kilometers) for GNSS frequencies and observational geometry. Additionally, they allow identifying the diffractive and refractive nature of fluctuations on the recorded GNSS signals. Six strong auroral events and their effects on plasma structuring are studied. Plasma structuring down to scales of hundreds of meters is seen when strong gradients in auroral emissions at 557.7 nm cross the line of sight between the GNSS satellite and receiver. Local magnetic field measurements confirm small-scale structuring processes coinciding with intensification of ionospheric currents. Since 557.7 nm emissions primarily originate from the ionospheric E-region, plasma instabilities from particle precipitation at E-region altitudes are considered to be responsible for the signatures of small-scale plasma structuring highlighted in the GNSS scintillation data.
我们研究了极光粒子沉淀在北极上空极光电离层小尺度（低于数百米）等离子体结构中的作用。在这一范围内，我们分析了电离层闪烁监测接收器（ISMR）记录的全球导航卫星系统（GNSS）信号和位于斯瓦尔巴群岛（挪威）朗伊尔边的全天空成像仪记录的数据。我们利用 ISMR 提供的 50 赫兹全球导航卫星系统原始样本，以 1 秒时间分辨率评估振幅和相位闪烁指数，以 20 毫秒时间分辨率评估无电离层线性组合。同时使用基于全球导航卫星系统的 1 秒闪烁指数，可以确定等离子体结构所涉及的不规则的尺度大小，其范围为全球导航卫星系统频率和观测几何的小尺度（最多几百米）和中尺度范围（最多几千米）。此外，它们还能识别记录的全球导航卫星系统信号波动的衍射和折射性质。研究了六个强极光事件及其对等离子体结构的影响。当 557.7 纳米极光发射的强梯度穿过全球导航卫星系统卫星和接收器之间的视线时，会出现等离子体结构化，其尺度可达数百米。当地磁场测量证实，小尺度结构化过程与电离层电流的增强相吻合。由于 557.7 nm 辐射主要来自电离层 E 区域，因此认为 E 区域高度的粒子沉降所产生的等离子体不稳定性是全球导航卫星系统闪烁数据中突出显示的小规模等离子体结构化特征的原因。
{"title":"Investigation of Ionospheric Small-Scale Plasma Structures Associated With Particle Precipitation","authors":"F. Enengl, L. Spogli, D. Kotova, Y. Jin, K. Oksavik, N. Partamies, W. J. Miloch","doi":"10.1029/2023sw003605","DOIUrl":"https://doi.org/10.1029/2023sw003605","url":null,"abstract":"We investigate the role of auroral particle precipitation in small-scale (below hundreds of meters) plasma structuring in the auroral ionosphere over the Arctic. In this scope, we analyze together data recorded by an Ionospheric Scintillation Monitor Receiver (ISMR) of Global Navigation Satellite System (GNSS) signals and by an All-Sky Imager located in Longyearbyen, Svalbard (Norway). We leverage on the raw GNSS samples provided at 50 Hz by the ISMR to evaluate amplitude and phase scintillation indices at 1 s time resolution and the Ionosphere-Free Linear Combination at 20 ms time resolution. The simultaneous use of the 1 s GNSS-based scintillation indices allows identifying the scale size of the irregularities involved in plasma structuring in the range of small (up to few hundreds of meters) and medium-scale size ranges (up to few kilometers) for GNSS frequencies and observational geometry. Additionally, they allow identifying the diffractive and refractive nature of fluctuations on the recorded GNSS signals. Six strong auroral events and their effects on plasma structuring are studied. Plasma structuring down to scales of hundreds of meters is seen when strong gradients in auroral emissions at 557.7 nm cross the line of sight between the GNSS satellite and receiver. Local magnetic field measurements confirm small-scale structuring processes coinciding with intensification of ionospheric currents. Since 557.7 nm emissions primarily originate from the ionospheric E-region, plasma instabilities from particle precipitation at E-region altitudes are considered to be responsible for the signatures of small-scale plasma structuring highlighted in the GNSS scintillation data.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"133 ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139411321","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}
This study evaluates the performance of deep learning approach in the prediction of the ionospheric total electron content (TEC) during magnetically quiet periods. Two deep learning techniques, long short-term memory (LSTM) and convolutional LSTM (ConvLSTM), are employed to predict TEC values 24 hr ahead in the vicinity of the Korean Peninsula (26.5°–40°N, 121°–134.5°E). The LSTM method predicts TEC at a single point based on time series of data at that point, whereas the ConvLSTM method simultaneously predicts TEC values at multiple points using spatiotemporal distribution of TEC. Both the LSTM and ConvLSTM models are trained using the complete regional TEC maps reconstructed by applying the Deep Convolutional Generative Adversarial Network–Poisson Blending (DCGAN-PB) method to observed TEC data. The training period spans from 2002 to 2018, and the model performance is evaluated using 2019 data. Our results show that the ConvLSTM method outperforms the LSTM method, generating more reliable TEC maps with smaller root mean square errors when compared to the ground truth (DCGAN-PB TEC maps). This outcome indicates that deep learning models can improve the prediction accuracy of TEC at a specific point by taking into account spatial information of TEC. We conclude that ConvLSTM is a reliable and efficient approach for the prompt ionospheric prediction.
{"title":"Deep Learning-Based Regional Ionospheric Total Electron Content Prediction—Long Short-Term Memory (LSTM) and Convolutional LSTM Approach","authors":"Se-Heon Jeong, Woo Kyoung Lee, Hyosub Kil, Soojeong Jang, Jeong-Heon Kim, Young-Sil Kwak","doi":"10.1029/2023sw003763","DOIUrl":"https://doi.org/10.1029/2023sw003763","url":null,"abstract":"This study evaluates the performance of deep learning approach in the prediction of the ionospheric total electron content (TEC) during magnetically quiet periods. Two deep learning techniques, long short-term memory (LSTM) and convolutional LSTM (ConvLSTM), are employed to predict TEC values 24 hr ahead in the vicinity of the Korean Peninsula (26.5°–40°N, 121°–134.5°E). The LSTM method predicts TEC at a single point based on time series of data at that point, whereas the ConvLSTM method simultaneously predicts TEC values at multiple points using spatiotemporal distribution of TEC. Both the LSTM and ConvLSTM models are trained using the complete regional TEC maps reconstructed by applying the Deep Convolutional Generative Adversarial Network–Poisson Blending (DCGAN-PB) method to observed TEC data. The training period spans from 2002 to 2018, and the model performance is evaluated using 2019 data. Our results show that the ConvLSTM method outperforms the LSTM method, generating more reliable TEC maps with smaller root mean square errors when compared to the ground truth (DCGAN-PB TEC maps). This outcome indicates that deep learning models can improve the prediction accuracy of TEC at a specific point by taking into account spatial information of TEC. We conclude that ConvLSTM is a reliable and efficient approach for the prompt ionospheric prediction.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"7 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139411529","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}
Taiji is proposed as a space-based gravitational wave (GW) observatory consisting of three spacecraft in a heliocentric orbit meanwhile with the distance of 3 million kilometers ahead of the Earth at about 20°. Free-falling test masses (TMs) are a key component of the interferometer for space-based GW detection in the 0.1mHz–1 Hz frequency range. Exposure to energetic particles in the space environment can lead to charging of the TMs and thus cause additional electrostatic forces and Lorentz forces that limit the sensitivity of the interferometer and may affect the quality of the scientific data. This study aims to model the charging of TMs during Galactic cosmic rays and solar proton events (SPEs) using the Monte Carlo simulation toolkit meanwhile with constructing the sophisticated 3D spacecraft. The results show that the total net charging rates are 34.48 +e/s and 33.85 +e/s on TM1 and TM2 during the solar minimum, and 9.58 +e/s on TM1 and 9.65 +e/s on TM2 during the solar maximum. We confirm that no matter for solar minimum or solar maximum, protons contribute to the largest proportion of the TMs charging rate. Furthermore, charging for five typical SPEs is also investigated, and the maximum TMs charging rate reaches 76,674 +e/s, indicating that sporadic SPEs have a high risk for TMs charging. Finally, the charging rates of a TM imitation are tested on ground by the 30–50 MeV proton irradiation experiment, and the experimental results show good consistence with the simulation results with the error <10%.
{"title":"Study on Test-Mass Charging for Taiji Gravitational Wave Observatory","authors":"Ruilong Han, Minghui Cai, Tao Yang, Liangliang Xu, Qing Xia, Xinyu Jia, Dawei Gao, Mengyao Li, Longlong Zhang, Hongwei Li, Jianwei Han","doi":"10.1029/2023sw003724","DOIUrl":"https://doi.org/10.1029/2023sw003724","url":null,"abstract":"Taiji is proposed as a space-based gravitational wave (GW) observatory consisting of three spacecraft in a heliocentric orbit meanwhile with the distance of 3 million kilometers ahead of the Earth at about 20°. Free-falling test masses (TMs) are a key component of the interferometer for space-based GW detection in the 0.1mHz–1 Hz frequency range. Exposure to energetic particles in the space environment can lead to charging of the TMs and thus cause additional electrostatic forces and Lorentz forces that limit the sensitivity of the interferometer and may affect the quality of the scientific data. This study aims to model the charging of TMs during Galactic cosmic rays and solar proton events (SPEs) using the Monte Carlo simulation toolkit meanwhile with constructing the sophisticated 3D spacecraft. The results show that the total net charging rates are 34.48 +e/s and 33.85 +e/s on TM1 and TM2 during the solar minimum, and 9.58 +e/s on TM1 and 9.65 +e/s on TM2 during the solar maximum. We confirm that no matter for solar minimum or solar maximum, protons contribute to the largest proportion of the TMs charging rate. Furthermore, charging for five typical SPEs is also investigated, and the maximum TMs charging rate reaches 76,674 +e/s, indicating that sporadic SPEs have a high risk for TMs charging. Finally, the charging rates of a TM imitation are tested on ground by the 30–50 MeV proton irradiation experiment, and the experimental results show good consistence with the simulation results with the error <10%.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"210 3 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139373998","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 paper, we constructed the Exospheric Temperature Models (ETM) on the basis of CHAMP and GRACE data using different empirical orthogonal functions (EOFs). The EOFs of the exospheric temperature can be derived either from satellite data directly or from the outputs of the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) and MSIS models by applying the Principal Component Analysis method. Then, the thermospheric mass densities calculated from ETM are used to compare with the observed data in order to evaluate the performance of different ETM models. It was found that all these three models can provide good specification of thermospheric density including day-night, seasonal, and latitudinal variations. However, the ETM based on CHAMP and GRACE data gives a better performance in modeling the Equatorial Thermospheric Anomaly and the Midnight Density Maximum features than the MSIS-ETM and TIEGCM-ETM. Specifically, independent SWARM-C data comparison showed that the Relative Deviations and corresponding Root-Mean-Square-Errors of our Texo models are less than 8.9% and 22.8%, much better than the MSIS-00 model.
{"title":"Evaluation of the Exospheric Temperature Modeling From Different Empirical Orthogonal Functions","authors":"Xu Yang, Libin Weng, Jiuhou Lei, Xiaoqian Zhu, Haibing Ruan, Dexin Ren, Zhongli Li, Ruoxi Li, Liangjie Chen","doi":"10.1029/2023sw003549","DOIUrl":"https://doi.org/10.1029/2023sw003549","url":null,"abstract":"In this paper, we constructed the Exospheric Temperature Models (ETM) on the basis of CHAMP and GRACE data using different empirical orthogonal functions (EOFs). The EOFs of the exospheric temperature can be derived either from satellite data directly or from the outputs of the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) and MSIS models by applying the Principal Component Analysis method. Then, the thermospheric mass densities calculated from ETM are used to compare with the observed data in order to evaluate the performance of different ETM models. It was found that all these three models can provide good specification of thermospheric density including day-night, seasonal, and latitudinal variations. However, the ETM based on CHAMP and GRACE data gives a better performance in modeling the Equatorial Thermospheric Anomaly and the Midnight Density Maximum features than the MSIS-ETM and TIEGCM-ETM. Specifically, independent SWARM-C data comparison showed that the Relative Deviations and corresponding Root-Mean-Square-Errors of our <i>T</i><sub>exo</sub> models are less than 8.9% and 22.8%, much better than the MSIS-00 model.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"207 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139374074","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}
Zhou Chen, Kang Wang, Haimeng Li, Wenti Liao, Rongxin Tang, Jing-song Wang, Xiaohua Deng
Geomagnetic storms induce ionospheric disturbances, affecting short-wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short-wave radio environment of the ionosphere. We use the Multi-Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi-LSTM networks, an auxiliary model, and convolutional processes for spatiotemporal modeling. Our validation shows the MSAP model outperforms the IRI-2016 model in predicting global TEC for the next 6 days in the test set. We assess its performance during 116 geomagnetic storm events, considering storm intensity, solar activity, month, and Universal Time (UT). The MSAP model exhibits a weak correlation with storm intensity and a strong correlation with solar activity. Monthly variation displays similar strong correlations in root mean square error (RMSE) and R2 for both models. For UT variation, the other metrics exhibit a weak correlation with the number of Global Navigation Satellite System stations, except for the RMSE of the MSAP and IRI-2016 models.
{"title":"Storm-Time Characteristics of Ionospheric Model (MSAP) Based on Multi-Algorithm Fusion","authors":"Zhou Chen, Kang Wang, Haimeng Li, Wenti Liao, Rongxin Tang, Jing-song Wang, Xiaohua Deng","doi":"10.1029/2022sw003360","DOIUrl":"https://doi.org/10.1029/2022sw003360","url":null,"abstract":"Geomagnetic storms induce ionospheric disturbances, affecting short-wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short-wave radio environment of the ionosphere. We use the Multi-Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi-LSTM networks, an auxiliary model, and convolutional processes for spatiotemporal modeling. Our validation shows the MSAP model outperforms the IRI-2016 model in predicting global TEC for the next 6 days in the test set. We assess its performance during 116 geomagnetic storm events, considering storm intensity, solar activity, month, and Universal Time (UT). The MSAP model exhibits a weak correlation with storm intensity and a strong correlation with solar activity. Monthly variation displays similar strong correlations in root mean square error (RMSE) and <i>R</i><sup>2</sup> for both models. For UT variation, the other metrics exhibit a weak correlation with the number of Global Navigation Satellite System stations, except for the RMSE of the MSAP and IRI-2016 models.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"121 2 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139079394","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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