Pub Date : 2024-12-01Epub Date: 2024-11-30DOI: 10.1029/2024SW004108
Mahith Madhanakumar, Andres Spicher, Juha Vierinen, Kjellmar Oksavik, Anthea J Coster, Devin Ray Huyghebaert, Carley J Martin, Ingemar Häggström, Larry J Paxton
A multi-instrument study is conducted at the dayside polar ionosphere to investigate the spatio-temporal evolution of scintillation in Global Navigation Satellite System (GNSS) signals during non-storm conditions. Bursts of intense amplitude and phase scintillation started to occur at 9 MLT and persisted for more than 1 hour implying the simultaneous existence of Fresnel and large-scale sized irregularities of significant strength in the pre-noon sector. Measurements from the EISCAT radar in Svalbard (ESR) revealed the presence of dense plasma structures with significant gradients in regions of strong Joule heating/fast flows and soft precipitation when scintillation was enhanced. Plasma structuring down to Fresnel scales were observed both in the auroral oval as well as inside the polar cap with the associated amplitude scintillation exhibiting similar strengths regardless of whether the density structures were in regions of active auroral dynamics or not. The observations are placed within the context of different sources of free energy, providing insights into the important mechanisms that generate irregularities capable of perturbing GNSS signal properties in the dayside ionosphere. Furthermore, a strong negative excursion in the interplanetary magnetic field (IMF) component during the northward turning of led to the transport of a depleted region of plasma density into the post-noon sector that significantly weakened both amplitude and phase scintillation.
{"title":"The Growth and Decay of Intense GNSS Amplitude and Phase Scintillation During Non-Storm Conditions.","authors":"Mahith Madhanakumar, Andres Spicher, Juha Vierinen, Kjellmar Oksavik, Anthea J Coster, Devin Ray Huyghebaert, Carley J Martin, Ingemar Häggström, Larry J Paxton","doi":"10.1029/2024SW004108","DOIUrl":"https://doi.org/10.1029/2024SW004108","url":null,"abstract":"<p><p>A multi-instrument study is conducted at the dayside polar ionosphere to investigate the spatio-temporal evolution of scintillation in Global Navigation Satellite System (GNSS) signals during non-storm conditions. Bursts of intense amplitude and phase scintillation started to occur at <math> <mrow><mrow><mo>∼</mo></mrow> </mrow> </math> 9 MLT and persisted for more than 1 hour implying the simultaneous existence of Fresnel and large-scale sized irregularities of significant strength in the pre-noon sector. Measurements from the EISCAT radar in Svalbard (ESR) revealed the presence of dense plasma structures with significant gradients in regions of strong Joule heating/fast flows and soft precipitation when scintillation was enhanced. Plasma structuring down to Fresnel scales were observed both in the auroral oval as well as inside the polar cap with the associated amplitude scintillation exhibiting similar strengths regardless of whether the density structures were in regions of active auroral dynamics or not. The observations are placed within the context of different sources of free energy, providing insights into the important mechanisms that generate irregularities capable of perturbing GNSS signal properties in the dayside ionosphere. Furthermore, a strong negative excursion in the interplanetary magnetic field (IMF) <math> <mrow> <mrow><msub><mi>B</mi> <mi>y</mi></msub> </mrow> </mrow> </math> component during the northward turning of <math> <mrow> <mrow><msub><mi>B</mi> <mi>z</mi></msub> </mrow> </mrow> </math> led to the transport of a depleted region of plasma density into the post-noon sector that significantly weakened both amplitude and phase scintillation.</p>","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"22 12","pages":"e2024SW004108"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11607637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-11-27DOI: 10.1029/2024SW004126
S Tulasi Ram, B Veenadhari, A P Dimri, J Bulusu, M Bagiya, S Gurubaran, N Parihar, B Remya, G Seemala, Rajesh Singh, S Sripathi, S V Singh, G Vichare
One of the most intense geomagnetic storms of recent times occurred on 10-11 May 2024. With a peak negative excursion of Sym-H below -500 nT, this storm is the second largest of the space era. Solar wind energy transferred through radiation and mass coupling affected the entire Geospace. Our study revealed that the dayside magnetopause was compressed below the geostationary orbit (6.6 RE) for continuously ∼6 hr due to strong Solar Wind Dynamic Pressure (SWDP). Tremendous compression pushed the bow-shock also to below the geostationary orbit for a few minutes. Magnetohydrodynamic models suggest that the magnetopause location could be as low as 3.3RE. We show that a unique combination of high SWDP (≥15 nPa) with an intense eastward interplanetary electric field (IEFY ≥ 2.5 mV/m) within a super-dense Interplanetary Coronal Mass Ejection lasted for 409 min-is the key factor that led to the strong ring current at much closer to the Earth causing such an intense storm. Severe electrodynamic disturbances led to a strong positive ionospheric storm with more than 100% increase in dayside ionospheric Total Electron Content (TEC), affecting GPS positioning/navigation. Further, an HF radio blackout was found to occur in the 2-12 MHz frequency band due to strong D- and E-region ionization resulting from a solar flare prior to this storm.
{"title":"Super-Intense Geomagnetic Storm on 10-11 May 2024: Possible Mechanisms and Impacts.","authors":"S Tulasi Ram, B Veenadhari, A P Dimri, J Bulusu, M Bagiya, S Gurubaran, N Parihar, B Remya, G Seemala, Rajesh Singh, S Sripathi, S V Singh, G Vichare","doi":"10.1029/2024SW004126","DOIUrl":"https://doi.org/10.1029/2024SW004126","url":null,"abstract":"<p><p>One of the most intense geomagnetic storms of recent times occurred on 10-11 May 2024. With a peak negative excursion of Sym-H below -500 nT, this storm is the second largest of the space era. Solar wind energy transferred through radiation and mass coupling affected the entire Geospace. Our study revealed that the dayside magnetopause was compressed below the geostationary orbit (6.6 RE) for continuously ∼6 hr due to strong Solar Wind Dynamic Pressure (SWDP). Tremendous compression pushed the bow-shock also to below the geostationary orbit for a few minutes. Magnetohydrodynamic models suggest that the magnetopause location could be as low as 3.3RE. We show that a unique combination of high SWDP (≥15 nPa) with an intense eastward interplanetary electric field (IEF<sub>Y</sub> ≥ 2.5 mV/m) within a super-dense Interplanetary Coronal Mass Ejection lasted for 409 min-is the key factor that led to the strong ring current at much closer to the Earth causing such an intense storm. Severe electrodynamic disturbances led to a strong positive ionospheric storm with more than 100% increase in dayside ionospheric Total Electron Content (TEC), affecting GPS positioning/navigation. Further, an HF radio blackout was found to occur in the 2-12 MHz frequency band due to strong D- and E-region ionization resulting from a solar flare prior to this storm.</p>","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"22 12","pages":"e2024SW004126"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-11-28DOI: 10.1029/2024SW003873
S P Moraes-Santos, C M N Cândido, F Becker-Guedes, B Nava, V Klausner, C Borries, F S Chingarandi, T O Osanyin
This study investigates the Brazilian low-latitude ionospheric response to CIR/HSS-driven geomagnetic storms during the declining phase of solar cycle 24, from 2016 to 2017. In this period the geomagnetic storms were mostly moderate, SymHmin ≈ -72 nT, AEmax ≈ 1580 nT, Vswmax ≈ 690 km/s and lasted, on average, for 6 days. We analyze the variations in Vertical Total Electron Content (VTEC) at three representative regions: bele, over the equatorial region; boav and cuib, at the northern and southern crests of the Equatorial Ionization Anomaly. Our findings reveal the role of High-Speed Solar Wind Streams and Corotating Interaction Region-driven geomagnetic storms. The VTEC intensifications were up to 30 TECu, during the daytime and nighttime. Additionally, three categories of nighttime enhancements were observed and analyzed with distinct characteristics and levels of pre-reversal strengthening; Depletions up to 20 TECu also occurred during the day and nighttime. The delay between the storm commencement and the positive and negative variations were, on average, 7 and 20 hours, respectively. We discuss the Prompt Penetration Electric Fields and Disturbance Dynamo Electric Fields following the magnetic reconnection between Earth's and interplanetary magnetic field, using observational data and modeling. Furthermore, this study presents catalogs of low-latitude ionospheric storms, providing detailed information for space weather applications and ionospheric modeling.
{"title":"Influence of Solar Wind High-Speed Streams on the Brazilian Low-Latitude Ionosphere During the Descending Phase of Solar Cycle 24.","authors":"S P Moraes-Santos, C M N Cândido, F Becker-Guedes, B Nava, V Klausner, C Borries, F S Chingarandi, T O Osanyin","doi":"10.1029/2024SW003873","DOIUrl":"https://doi.org/10.1029/2024SW003873","url":null,"abstract":"<p><p>This study investigates the Brazilian low-latitude ionospheric response to CIR/HSS-driven geomagnetic storms during the declining phase of solar cycle 24, from 2016 to 2017. In this period the geomagnetic storms were mostly moderate, SymH<sub>min</sub> ≈ -72 nT, AE<sub>max</sub> ≈ 1580 nT, Vsw<sub>max</sub> ≈ 690 km/s and lasted, on average, for 6 days. We analyze the variations in Vertical Total Electron Content (VTEC) at three representative regions: bele, over the equatorial region; boav and cuib, at the northern and southern crests of the Equatorial Ionization Anomaly. Our findings reveal the role of High-Speed Solar Wind Streams and Corotating Interaction Region-driven geomagnetic storms. The VTEC intensifications were up to 30 TECu, during the daytime and nighttime. Additionally, three categories of nighttime enhancements were observed and analyzed with distinct characteristics and levels of pre-reversal strengthening; Depletions up to 20 TECu also occurred during the day and nighttime. The delay between the storm commencement and the positive and negative variations were, on average, 7 and 20 hours, respectively. We discuss the Prompt Penetration Electric Fields and Disturbance Dynamo Electric Fields following the magnetic reconnection between Earth's and interplanetary magnetic field, using observational data and modeling. Furthermore, this study presents catalogs of low-latitude ionospheric storms, providing detailed information for space weather applications and ionospheric modeling.</p>","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"22 12","pages":"e2024SW003873"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11604352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01Epub Date: 2024-11-26DOI: 10.1029/2024SW004084
Qingyu Zhu, Gang Lu, Sarah Vines, Marc Hairston
During the second recovery phase of the 13-14 March 2022 storm, intense high-latitude neutral mass density spikes are detected by satellites at ∼500 km in both hemispheres. These density spikes, accurately modeled by the Global Ionospheric Thermosphere Model (GITM), are identified as high-latitude neutral mass density anomalies (HDAs). The GITM simulation indicates that these HDAs, which extends over the polar region, are important features in high-latitude neutral density. Furthermore, GITM reveals that these HDAs are manifestations of transpolar traveling atmospheric disturbances triggered on the dawn side. Moreover, GITM also reveals significant interhemispheric asymmetries (IHAs) in the magnitude, propagation speed, and propagation direction of HDAs, which are linked to the IHAs in the distribution and magnitude of Joule heating deposited as well as the thermospheric background conditions. This study presents a dynamic perspective on the IHA of storm-time high-latitude neutral density variations that is particularly helpful to the proper interpretation of satellite observations.
{"title":"Interhemispheric Asymmetry in the High-Latitude Neutral Density Variations During the 13-14 March 2022 Storm.","authors":"Qingyu Zhu, Gang Lu, Sarah Vines, Marc Hairston","doi":"10.1029/2024SW004084","DOIUrl":"10.1029/2024SW004084","url":null,"abstract":"<p><p>During the second recovery phase of the 13-14 March 2022 storm, intense high-latitude neutral mass density spikes are detected by satellites at ∼500 km in both hemispheres. These density spikes, accurately modeled by the Global Ionospheric Thermosphere Model (GITM), are identified as high-latitude neutral mass density anomalies (HDAs). The GITM simulation indicates that these HDAs, which extends over the polar region, are important features in high-latitude neutral density. Furthermore, GITM reveals that these HDAs are manifestations of transpolar traveling atmospheric disturbances triggered on the dawn side. Moreover, GITM also reveals significant interhemispheric asymmetries (IHAs) in the magnitude, propagation speed, and propagation direction of HDAs, which are linked to the IHAs in the distribution and magnitude of Joule heating deposited as well as the thermospheric background conditions. This study presents a dynamic perspective on the IHA of storm-time high-latitude neutral density variations that is particularly helpful to the proper interpretation of satellite observations.</p>","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"22 11","pages":"e2024SW004084"},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11590173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The critical frequency of ionospheric F2 layer (foF2) is an important ionospheric characteristic parameter. In this paper, a deep learning model based on Bidirectional long short‐term memory (BiLSTM) and attention mechanism is implemented for predicting the foF2 parameter. The inputs of models are the foF2 of globally available ionospheric ionosonde stations, geographic longitude and latitude, world time (UT), geomagnetic activity index, and solar activity index from 2015 to 2017. The superiority of the model is analyzed from different latitudes, seasons, and geomagnetic conditions. The results show that the prediction performance of the Bidirectional long short‐term memory model based on attention mechanism (BiLSTM‐Attention) is better than other models. The performance of the prediction model is optimal at high latitudes. The root mean square error (RMSE) and correlation coefficient (R) of the BiLSTM‐Attention model are 0.539 MHZ and 0.908 MHz at high latitudes, respectively. In terms of RMSE, it is 25.243%, 18.209%, and 11.203% lower than those of the international reference ionosphere (IRI), LSTM, and BiLSTM models, respectively. The prediction results of the four seasons show that the models are more applicable in winter. Compared with the IRI model, the RMSE of the BiLSTM‐Attention model in spring, summer, autumn, and winter is reduced by 24.344%, 21.181%, 25.058%, and 30.948%, respectively. The prediction effect of the BiLSTM‐Attention model is improved in the magnetic quiet period, the magnetic moderate period and the magnetic storm period. Also, the improvement effect is more obvious in the magnetostatic day, and the RMSE is reduced by 27.462% compared with the IRI model.
{"title":"Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism","authors":"Jun Tang, Dengpan Yang, Mingfei Ding","doi":"10.1029/2023sw003508","DOIUrl":"https://doi.org/10.1029/2023sw003508","url":null,"abstract":"Abstract The critical frequency of ionospheric F2 layer (foF2) is an important ionospheric characteristic parameter. In this paper, a deep learning model based on Bidirectional long short‐term memory (BiLSTM) and attention mechanism is implemented for predicting the foF2 parameter. The inputs of models are the foF2 of globally available ionospheric ionosonde stations, geographic longitude and latitude, world time (UT), geomagnetic activity index, and solar activity index from 2015 to 2017. The superiority of the model is analyzed from different latitudes, seasons, and geomagnetic conditions. The results show that the prediction performance of the Bidirectional long short‐term memory model based on attention mechanism (BiLSTM‐Attention) is better than other models. The performance of the prediction model is optimal at high latitudes. The root mean square error (RMSE) and correlation coefficient (R) of the BiLSTM‐Attention model are 0.539 MHZ and 0.908 MHz at high latitudes, respectively. In terms of RMSE, it is 25.243%, 18.209%, and 11.203% lower than those of the international reference ionosphere (IRI), LSTM, and BiLSTM models, respectively. The prediction results of the four seasons show that the models are more applicable in winter. Compared with the IRI model, the RMSE of the BiLSTM‐Attention model in spring, summer, autumn, and winter is reduced by 24.344%, 21.181%, 25.058%, and 30.948%, respectively. The prediction effect of the BiLSTM‐Attention model is improved in the magnetic quiet period, the magnetic moderate period and the magnetic storm period. Also, the improvement effect is more obvious in the magnetostatic day, and the RMSE is reduced by 27.462% compared with the IRI model.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"34 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135714654","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}
Zheng Wang, Meiyi Zhan, Pengdong Gao, Guojun Wang, Chu Qiu, Quan Qi, Jiankui Shi, Xiao Wang
Abstract An intelligent Spread‐F image detection and classification method is presented in this paper based on an ionogram image set using deep learning models. The ionogram images from the Hainan station, spanning from 2002 to 2015, have been manually labeled into five categories, resulting in a unique ionogram image set for supervised learning models. To balance the number of different types, simulated noises were added to these images. Based on 80,000 samples with Spread‐F and 20,000 samples without, numerous experiments have been conducted to train VGG, ResNet, EfficientNet, ViT, MobileNet, and other networks. The results on the test set indicate that these models except VGG have a good ability of exacting features of different types, leading to a high level of accuracy in detecting Spread‐F and a relatively accurate classification of it. The ionogram images in 2016 are then employed as another test set to further examine the performance of the trained models. Both quantitative and qualitative analyses have demonstrated the results obtained by deep learning models are highly consistent with manual identification.
{"title":"Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning Method","authors":"Zheng Wang, Meiyi Zhan, Pengdong Gao, Guojun Wang, Chu Qiu, Quan Qi, Jiankui Shi, Xiao Wang","doi":"10.1029/2023sw003498","DOIUrl":"https://doi.org/10.1029/2023sw003498","url":null,"abstract":"Abstract An intelligent Spread‐F image detection and classification method is presented in this paper based on an ionogram image set using deep learning models. The ionogram images from the Hainan station, spanning from 2002 to 2015, have been manually labeled into five categories, resulting in a unique ionogram image set for supervised learning models. To balance the number of different types, simulated noises were added to these images. Based on 80,000 samples with Spread‐F and 20,000 samples without, numerous experiments have been conducted to train VGG, ResNet, EfficientNet, ViT, MobileNet, and other networks. The results on the test set indicate that these models except VGG have a good ability of exacting features of different types, leading to a high level of accuracy in detecting Spread‐F and a relatively accurate classification of it. The ionogram images in 2016 are then employed as another test set to further examine the performance of the trained models. Both quantitative and qualitative analyses have demonstrated the results obtained by deep learning models are highly consistent with manual identification.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"57 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135410145","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}
D. Conde, F. L. Castillo, C. Escobar, C. García, J. E. García, V. Sanz, B. Zaldívar, J. J. Curto, S. Marsal, J. M. Torta
Abstract Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high‐latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground‐based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non‐linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine‐learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM‐H index characterizing geomagnetic storms multiple‐hour ahead, using public interplanetary magnetic field (IMF) data from the Sun‐Earth L1 Lagrange point and SYM‐H data. We implement a type of machine‐learning model called long short‐term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep‐learning model in the context of forecasting the SYM‐H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper‐parameters of the LSTM network and robustness tests.
{"title":"Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning","authors":"D. Conde, F. L. Castillo, C. Escobar, C. García, J. E. García, V. Sanz, B. Zaldívar, J. J. Curto, S. Marsal, J. M. Torta","doi":"10.1029/2023sw003474","DOIUrl":"https://doi.org/10.1029/2023sw003474","url":null,"abstract":"Abstract Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high‐latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground‐based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non‐linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine‐learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM‐H index characterizing geomagnetic storms multiple‐hour ahead, using public interplanetary magnetic field (IMF) data from the Sun‐Earth L1 Lagrange point and SYM‐H data. We implement a type of machine‐learning model called long short‐term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep‐learning model in the context of forecasting the SYM‐H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper‐parameters of the LSTM network and robustness tests.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"35 5-6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135714650","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}
C. Kay, T. Nieves‐Chinchilla, S. J. Hofmeister, E. Palmerio, V. E. Ledvina
Abstract Coronal mass ejections (CMEs) and high speed streams (HSSs) are large‐scale transient structures that routinely propagate away from the Sun. Individually, they can cause space weather effects at the Earth, or elsewhere in space, but many of the largest events occur when these structures interact during their interplanetary propagation. We present the initial coupling of Open Solar Physics Rapid Ensemble Information (OSPREI), a model for CME evolution, with Mostly Empirical Operational Wind with a High Speed Stream, a time‐dependent HSS model that can serve as a background for the OSPREI CME. We present several improvements made to OSPREI in order to take advantage of the new time‐dependent, higher‐dimension background. This includes an update in the drag calculation and the ability to determine the rotation of a yaw‐like angle. We present several theoretical case studies, describing the difference in the CME behavior between a HSS background and a quiescent one. This behavior includes interplanetary CME propagation, expansion, deformation, and rotation, as well as the formation of a CME‐driven sheath. We also determine how the CME behavior changes with the HSS size and initial front distance. Generally, for a fast CME, we see that the drag is greatly reduced within the HSS, leading to faster CMEs and shorter travel times. The drag reappears stronger if the CME reaches the stream interaction region or upstream solar wind, leading to a stronger shock with more compression until the CME sufficiently decelerates. We model a CME–HSS interaction event observed by Parker Solar Probe in January 2022. The model improvements create a better match to the observed in situ profiles.
{"title":"A Series of Advances in Analytic Interplanetary CME Modeling","authors":"C. Kay, T. Nieves‐Chinchilla, S. J. Hofmeister, E. Palmerio, V. E. Ledvina","doi":"10.1029/2023sw003647","DOIUrl":"https://doi.org/10.1029/2023sw003647","url":null,"abstract":"Abstract Coronal mass ejections (CMEs) and high speed streams (HSSs) are large‐scale transient structures that routinely propagate away from the Sun. Individually, they can cause space weather effects at the Earth, or elsewhere in space, but many of the largest events occur when these structures interact during their interplanetary propagation. We present the initial coupling of Open Solar Physics Rapid Ensemble Information (OSPREI), a model for CME evolution, with Mostly Empirical Operational Wind with a High Speed Stream, a time‐dependent HSS model that can serve as a background for the OSPREI CME. We present several improvements made to OSPREI in order to take advantage of the new time‐dependent, higher‐dimension background. This includes an update in the drag calculation and the ability to determine the rotation of a yaw‐like angle. We present several theoretical case studies, describing the difference in the CME behavior between a HSS background and a quiescent one. This behavior includes interplanetary CME propagation, expansion, deformation, and rotation, as well as the formation of a CME‐driven sheath. We also determine how the CME behavior changes with the HSS size and initial front distance. Generally, for a fast CME, we see that the drag is greatly reduced within the HSS, leading to faster CMEs and shorter travel times. The drag reappears stronger if the CME reaches the stream interaction region or upstream solar wind, leading to a stronger shock with more compression until the CME sufficiently decelerates. We model a CME–HSS interaction event observed by Parker Solar Probe in January 2022. The model improvements create a better match to the observed in situ profiles.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"121 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135810216","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 solar wind which arrives at any location in the solar system is, in principle, relatable to the outflow of solar plasma from a single source location. This source location, itself usually being part of a larger coronal hole, is traceable to 1 R S along the Sun's magnetic field, in which the entire path from 1 R S to a location in the heliosphere is referred to as the solar wind connectivity. While not directly measurable, the connectivity between the near‐Earth solar wind is of particular importance to space weather. The solar wind solar source region can be obtained by leveraging near‐sun magnetic field models and a model of the interplanetary solar wind. In this article, we present a method for making an ensemble forecast of the connectivity presented as a probability distribution obtained from a weighted collection of individual forecasts from the combined Air Force Data Assimilative Photospheric Flux Transport‐Wang Sheeley Arge (ADAPT‐WSA) model. The ADAPT model derives the photospheric magnetic field from synchronic magnetogram data, using flux transport physics and ongoing data assimilation processes. The WSA model uses a coupled set of potential field type models to derive the coronal magnetic field, and an empirical relationship to derive the terminal solar wind speed observed at Earth. Our method produces an arbitrary 2D probability distribution capable of reflecting complex source configurations with minimal assumptions about the distribution structure, prepared in a computationally efficient manner.
{"title":"Ensemble Forecasts of Solar Wind Connectivity to 1 <i>R</i><sub><i>s</i></sub> Using ADAPT‐WSA","authors":"D. E. da Silva, S. Wallace, C. N. Arge, S. Jones","doi":"10.1029/2023sw003554","DOIUrl":"https://doi.org/10.1029/2023sw003554","url":null,"abstract":"Abstract The solar wind which arrives at any location in the solar system is, in principle, relatable to the outflow of solar plasma from a single source location. This source location, itself usually being part of a larger coronal hole, is traceable to 1 R S along the Sun's magnetic field, in which the entire path from 1 R S to a location in the heliosphere is referred to as the solar wind connectivity. While not directly measurable, the connectivity between the near‐Earth solar wind is of particular importance to space weather. The solar wind solar source region can be obtained by leveraging near‐sun magnetic field models and a model of the interplanetary solar wind. In this article, we present a method for making an ensemble forecast of the connectivity presented as a probability distribution obtained from a weighted collection of individual forecasts from the combined Air Force Data Assimilative Photospheric Flux Transport‐Wang Sheeley Arge (ADAPT‐WSA) model. The ADAPT model derives the photospheric magnetic field from synchronic magnetogram data, using flux transport physics and ongoing data assimilation processes. The WSA model uses a coupled set of potential field type models to derive the coronal magnetic field, and an empirical relationship to derive the terminal solar wind speed observed at Earth. Our method produces an arbitrary 2D probability distribution capable of reflecting complex source configurations with minimal assumptions about the distribution structure, prepared in a computationally efficient manner.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"45 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":"136009491","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. Waszewski, J. S. Morgan, R. Chhetri, R. Ekers, M. C. M. Cheung, N. D. R. Bhat, M. Johnston‐Hollitt
Abstract We have conducted a blind search in 49 consecutive days of interplanetary scintillation observations made by the Murchison Widefield Array from mid‐2019, with overlapping daily observations approximately East and South‐East of the Sun at an elongation of ∼30° and a field of view of 30°. These observations detect an unprecedented density of sources. In spite of these observations being taken at sunspot minimum, this search has revealed several interesting transitory features characterized by elevated scintillation levels. One solar wind enhancement is captured in two observations several hours apart, allowing its radial movement away from the Sun to be measured. We present here a methodology for measuring the plane‐of‐sky velocity for the moving heliospheric structure. The plane‐of‐sky velocity was inferred as 0.66 ± 0.147 hr −1 , or 480 ± 106 kms −1 assuming a distance of 1AU. After cross‐referencing our observed structure with multiple catalogs of heliospheric events, we propose that the likely source of our observed structure is a stream‐interaction region originating from a low‐latitude coronal hole. This work demonstrates the power of widefield interplanetary scintillation observations to capture detailed features in the heliosphere which are otherwise unresolvable and go undetected.
{"title":"Resolving Moving Heliospheric Structures Using Interplanetary Scintillation Observations With the Murchison Widefield Array","authors":"A. Waszewski, J. S. Morgan, R. Chhetri, R. Ekers, M. C. M. Cheung, N. D. R. Bhat, M. Johnston‐Hollitt","doi":"10.1029/2023sw003570","DOIUrl":"https://doi.org/10.1029/2023sw003570","url":null,"abstract":"Abstract We have conducted a blind search in 49 consecutive days of interplanetary scintillation observations made by the Murchison Widefield Array from mid‐2019, with overlapping daily observations approximately East and South‐East of the Sun at an elongation of ∼30° and a field of view of 30°. These observations detect an unprecedented density of sources. In spite of these observations being taken at sunspot minimum, this search has revealed several interesting transitory features characterized by elevated scintillation levels. One solar wind enhancement is captured in two observations several hours apart, allowing its radial movement away from the Sun to be measured. We present here a methodology for measuring the plane‐of‐sky velocity for the moving heliospheric structure. The plane‐of‐sky velocity was inferred as 0.66 ± 0.147 hr −1 , or 480 ± 106 kms −1 assuming a distance of 1AU. After cross‐referencing our observed structure with multiple catalogs of heliospheric events, we propose that the likely source of our observed structure is a stream‐interaction region originating from a low‐latitude coronal hole. This work demonstrates the power of widefield interplanetary scintillation observations to capture detailed features in the heliosphere which are otherwise unresolvable and go undetected.","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":"134934305","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}