Space Weather最新文献_第3页

Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation 基于太阳光层磁场观测的傅立叶神经算子对日冕结构的预测

Space Weather Pub Date : 2024-05-01 DOI: 10.1029/2024sw003875

Jingmin Zhao, Xueshang Feng

{"title":"Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation","authors":"Jingmin Zhao, Xueshang Feng","doi":"10.1029/2024sw003875","DOIUrl":"https://doi.org/10.1029/2024sw003875","url":null,"abstract":"This paper constructs the structures of the solar corona (SC) using Fourier neural operators (FNO) based on solar photospheric magnetic field observation. The purpose is to learn the mapping between two infinite‐dimensional function spaces, which takes the photospheric magnetic field as input and the magnetohydrodynamic (MHD) solar wind plasma parameters as output, from a finite collection of input‐output pairs. The FNO‐SC model is established using MHD simulated results of 36 Carrington rotations (CRs) from 2008, 2009, and 2020. The performance of the FNO‐SC model is tested for 6 CRs during various phases of the solar activity such as descending, minimum, and ascending phases to generate the 3D structures of the SC. With the MHD simulations as references, the average structure similarity index measure (SSIM) value for the magnetic field topology from 1 to 3Rs is around 0.88. From 1 to 20Rs, the SSIM values for the number density and radial speed surpass 0.9. Relative to OMNI observations, the mean absolute percentage error for the radial speed generated from the FNO‐SC model does not exceed 0.25. These results indicate that the FNO‐SC model effectively captures the solar coronal structures typical of the periods investigated, by recovering the MHD simulations as well as the observations. The FNO‐SC model is further trained with enriched data from the maximum phase to assess the capability of modeling such a situation. The FNO‐SC model costs 48.7 s for a single CR prediction, and thus facilitates real‐time space weather forecasting.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":" 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ionospheric Nowcasting Over Italy Through Data Assimilation: A Synergy Between IRI UP and IONORING 通过数据同化对意大利上空进行电离层预报：IRI UP 与 IONORING 的协同作用

Space Weather Pub Date : 2024-05-01 DOI: 10.1029/2023sw003838

A. Pignalberi, C. Cesaroni, M. Pietrella, M. Pezzopane, L. Spogli, C. Marcocci, E. Pica

{"title":"Ionospheric Nowcasting Over Italy Through Data Assimilation: A Synergy Between IRI UP and IONORING","authors":"A. Pignalberi, C. Cesaroni, M. Pietrella, M. Pezzopane, L. Spogli, C. Marcocci, E. Pica","doi":"10.1029/2023sw003838","DOIUrl":"https://doi.org/10.1029/2023sw003838","url":null,"abstract":"An accurate modeling of the ionosphere electron density is pivotal to guarantee the effective operation of communication and navigation systems, particularly during Space Weather events. Despite the crucial contribution of empirical models like the International Reference Ionosphere (IRI), their limitations in predicting ionospheric variability, especially under geomagnetically disturbed conditions, are acknowledged. The solution proposed in this work involves integrating real‐time, spatially distributed ionospheric measurements into climatological models through data assimilation. To enhance our predictive capabilities, we present an upgrade of the IRI UP data‐assimilation method, incorporating real‐time vertical total electron content (vTEC) maps from the IONORING algorithm for nowcasting ionospheric conditions over Italy. This approach involves updating the IRI F2‐layer peak electron density description through ionospheric indices, to finally produce real‐time maps over Italy of the ordinary critical frequency of the F2‐layer, foF2, which is crucial for radio‐propagation applications. The IRI UP–IONORING method performance has been evaluated against different climatological and nowcasting models, and under different Space Weather conditions, by showing promising outcomes which encourages its inclusion in the portfolio of ionospheric real‐time products available over Italy. The validation analysis highlighted also what are the current limitations of the IRI UP–IONORING method, particularly during nighttime for severely disturbed conditions, suggesting avenues for future enhancements.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"82 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141143392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal Variability of Equatorial Ionization Anomaly Crest Locations Extracted From Global Ionospheric Maps 从全球电离层地图中提取的赤道电离异常峰位置的时变性

Space Weather Pub Date : 2024-05-01 DOI: 10.1029/2023sw003737

Corina Dunn, X. Meng, O. Verkhoglyadova

{"title":"Temporal Variability of Equatorial Ionization Anomaly Crest Locations Extracted From Global Ionospheric Maps","authors":"Corina Dunn, X. Meng, O. Verkhoglyadova","doi":"10.1029/2023sw003737","DOIUrl":"https://doi.org/10.1029/2023sw003737","url":null,"abstract":"The Equatorial Ionization Anomaly (EIA) crest location is known to vary over a variety of temporal scales. For the first time we perform a statistical survey of the temporal variation of the EIA crest location viewed globally and spanning 20 years. We extract the crest location for double‐peaked EIAs from a data set of total electron content intensifications identified on global ionospheric maps from 2003 to 2022. We show that the dominant temporal variations of the crest latitude are annual and semi‐diurnal for the northern crest, and annual and diurnal for the southern crest. For the annual variation, we find that both crests move poleward in local summer and equatorward in local winter, which is more pronounced for the southern crest than the northern crest, and more pronounced at solar minimum than solar maximum. For the diurnal and semi‐diurnal variations in universal time, both crests dip southward around 15UT and the northern crest additionally dips southward around 2.5UT. We consider apparent universal time dependence to be a proxy for the longitudinal distribution of the crest geomagnetic latitude, which exhibits the known wave‐number‐four longitudinal structure of EIA crests. In local time, the EIA crests form earlier than 10LT and move poleward to their maximum distance at 14LT, and remain at constant latitude until 18LT. Solar cycle modulation on the diurnal/semi‐diurnal variations and the local time evolution of the crest latitude is minimal.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"43 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141141310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The 2022 Starlink Geomagnetic Storms: Global Thermospheric Response to a High‐Latitude Ionospheric Driver 2022 年星链地磁风暴：全球热层对高纬度电离层驱动因素的响应

Space Weather Pub Date : 2024-02-01 DOI: 10.1029/2023sw003748

D. Billett, Kian Sartipzadeh, M. F. Ivarsen, Elisabetta Iorﬁda, E. Doornbos, Emine Ceren, Kalafatoglu Eyig¨uler, K. Pandey, Kathryn A McWilliams, M. F. Ivarsen, E. C. K. Eyiguler

{"title":"The 2022 Starlink Geomagnetic Storms: Global Thermospheric Response to a High‐Latitude Ionospheric Driver","authors":"D. Billett, Kian Sartipzadeh, M. F. Ivarsen, Elisabetta Iorﬁda, E. Doornbos, Emine Ceren, Kalafatoglu Eyig¨uler, K. Pandey, Kathryn A McWilliams, M. F. Ivarsen, E. C. K. Eyiguler","doi":"10.1029/2023sw003748","DOIUrl":"https://doi.org/10.1029/2023sw003748","url":null,"abstract":"In this study, we present ionospheric observations of field‐aligned currents from AMPERE and the ESA Swarm A satellite, in conjunction with high‐resolution thermospheric density measurements from accelerometers on board Swarm C and GRACE‐FO, for the third and 4 February 2022 geomagnetic storms that led to the loss of 38 Starlink internet satellites. We study the global storm time response of the thermospheric density enhancements, including their decay and latitudinal distribution. We find that the thermospheric density enhances globally in response to high‐latitude energy input from the magnetosphere‐solar wind system and takes at least a full day to recover to pre‐storm density levels. We also find that the greatest density perturbations occur at polar latitudes consistent with the magnetosphere‐ionosphere dayside cusp, and that there appeared to be a saturation of the thermospheric density during the geomagnetic storm on the fourth. Our results highlight the critical importance of high‐latitude ionospheric observations when diagnosing potentially hazardous conditions for low‐Earth‐orbit satellites.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"56 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139880936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing Thermospheric Neutral Density Models Using GEODYN's Precision Orbit Determination 利用 GEODYN 的精确轨道测定评估热层中性密度模型

Space Weather Pub Date : 2024-02-01 DOI: 10.1029/2023sw003603

Zachary C. Waldron, K. Garcia‐Sage, J. Thayer, E. Sutton, Vishal, Ray, D. Rowlands, F. LeMoine, S. Luthcke, M. Kuznetsova, Rebecca Ringuette, L. Rastaetter, G. Berland

{"title":"Assessing Thermospheric Neutral Density Models Using GEODYN's Precision Orbit Determination","authors":"Zachary C. Waldron, K. Garcia‐Sage, J. Thayer, E. Sutton, Vishal, Ray, D. Rowlands, F. LeMoine, S. Luthcke, M. Kuznetsova, Rebecca Ringuette, L. Rastaetter, G. Berland","doi":"10.1029/2023sw003603","DOIUrl":"https://doi.org/10.1029/2023sw003603","url":null,"abstract":"This study focuses on utilizing the increasing availability of satellite trajectory data from global navigation satellite system‐enabled low‐Earth orbiting satellites and their precision orbit determination (POD) solutions to expand and refine thermospheric model validation capabilities. The research introduces an updated interface for the GEODYN‐II POD software, leveraging high‐precision space geodetic POD to investigate satellite drag and assess density models. This work presents a case study to examine five models (NRLMSIS2.0, DTM2020, JB2008, TIEGCM, and CTIPe) using precise science orbit (PSO) solutions of the Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2). The PSO is used as tracking measurements to construct orbit fits, enabling an evaluation according to each model's ability to redetermine the orbit. Relative in‐track deviations, quantified by in‐track residuals and root‐mean‐square errors (RMSe), are treated as proxies for model densities that differ from an unknown true density. The study investigates assumptions related to the treatment of the drag coefficient and leverages them to eliminate bias and effectively scale model density. Assessment results and interpretations are dictated by the timescale at which the scaling occurs. DTM2020 requires the least scaling (∼−7%) to achieve orbit fits closely matching the PSO within an in‐track RMSe of 7 m when scaled over 2 weeks and 2 m when scaled daily. The remaining models require substantial scaling of the mean density offset (∼30 − 75%) to construct orbit fits that meet the aforementioned RMSe criteria. All models exhibit slight over or under‐sensitivity to geomagnetic activity according to trends in their 24‐hr scaling factors.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"130 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139875721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing Thermospheric Neutral Density Models Using GEODYN's Precision Orbit Determination 利用 GEODYN 的精确轨道测定评估热层中性密度模型

Space Weather Pub Date : 2024-02-01 DOI: 10.1029/2023sw003603

Zachary C. Waldron, K. Garcia‐Sage, J. Thayer, E. Sutton, Vishal, Ray, D. Rowlands, F. LeMoine, S. Luthcke, M. Kuznetsova, Rebecca Ringuette, L. Rastaetter, G. Berland

{"title":"Assessing Thermospheric Neutral Density Models Using GEODYN's Precision Orbit Determination","authors":"Zachary C. Waldron, K. Garcia‐Sage, J. Thayer, E. Sutton, Vishal, Ray, D. Rowlands, F. LeMoine, S. Luthcke, M. Kuznetsova, Rebecca Ringuette, L. Rastaetter, G. Berland","doi":"10.1029/2023sw003603","DOIUrl":"https://doi.org/10.1029/2023sw003603","url":null,"abstract":"This study focuses on utilizing the increasing availability of satellite trajectory data from global navigation satellite system‐enabled low‐Earth orbiting satellites and their precision orbit determination (POD) solutions to expand and refine thermospheric model validation capabilities. The research introduces an updated interface for the GEODYN‐II POD software, leveraging high‐precision space geodetic POD to investigate satellite drag and assess density models. This work presents a case study to examine five models (NRLMSIS2.0, DTM2020, JB2008, TIEGCM, and CTIPe) using precise science orbit (PSO) solutions of the Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2). The PSO is used as tracking measurements to construct orbit fits, enabling an evaluation according to each model's ability to redetermine the orbit. Relative in‐track deviations, quantified by in‐track residuals and root‐mean‐square errors (RMSe), are treated as proxies for model densities that differ from an unknown true density. The study investigates assumptions related to the treatment of the drag coefficient and leverages them to eliminate bias and effectively scale model density. Assessment results and interpretations are dictated by the timescale at which the scaling occurs. DTM2020 requires the least scaling (∼−7%) to achieve orbit fits closely matching the PSO within an in‐track RMSe of 7 m when scaled over 2 weeks and 2 m when scaled daily. The remaining models require substantial scaling of the mean density offset (∼30 − 75%) to construct orbit fits that meet the aforementioned RMSe criteria. All models exhibit slight over or under‐sensitivity to geomagnetic activity according to trends in their 24‐hr scaling factors.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"8 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139815974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3‐D Ionospheric Imaging Over the South American Region With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS‐SA) 利用新的基于 TEC 的电离层数据同化系统（TIDAS-SA）对南美洲地区上空的电离层进行三维成像

Space Weather Pub Date : 2024-02-01 DOI: 10.1029/2023sw003792

Ercha Aa, Shun‐Rong Zhang, Philip J. Erickson, Wenbin Wang, A. Coster, W. Rideout

{"title":"3‐D Ionospheric Imaging Over the South American Region With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS‐SA)","authors":"Ercha Aa, Shun‐Rong Zhang, Philip J. Erickson, Wenbin Wang, A. Coster, W. Rideout","doi":"10.1029/2023sw003792","DOIUrl":"https://doi.org/10.1029/2023sw003792","url":null,"abstract":"This study has developed a new TEC‐based ionospheric data assimilation system for 3‐D regional ionospheric imaging over the South American sector (TIDAS‐SA) (45°S–15°N, 35°–85°W, and 100–800 km). The TIDAS‐SA data assimilation system utilizes a hybrid Ensemble‐Variational approach to incorporate a diverse set of ionospheric data sources, including dense ground‐based Global Navigation Satellite System (GNSS) line‐of‐sight Total Electron Content (TEC) data, radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate‐2 (COSMIC‐2), and altimeter TEC data from the JASON‐3 satellite. TIDAS‐SA can produce a reanalyzed three‐dimensional (3‐D) electron density spatial variation with a high time cadence, yielding spatial‐temporal resolution of 1° (latitude) × 1° (longitude) × 20 km (altitude) × 5 min. This allows us to reconstruct and study the 3‐D ionospheric morphology with multi‐scale structures. The performance of the data assimilation system is validated against independent ionosonde and in situ measurements through an experiment for a strong geomagnetic storm event on 03–04 November 2021. The results demonstrate that TIDAS‐SA can provide detailed and altitude‐resolved information that accurately characterizes the storm‐time ionospheric disturbances in vertical and horizontal domains over the equatorial and low‐latitude regions of South America.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139875790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The 2022 Starlink Geomagnetic Storms: Global Thermospheric Response to a High‐Latitude Ionospheric Driver 2022 年星链地磁风暴：全球热层对高纬度电离层驱动因素的响应

Space Weather Pub Date : 2024-02-01 DOI: 10.1029/2023sw003748

D. Billett, Kian Sartipzadeh, M. F. Ivarsen, Elisabetta Iorﬁda, E. Doornbos, Emine Ceren, Kalafatoglu Eyig¨uler, K. Pandey, Kathryn A McWilliams, M. F. Ivarsen, E. C. K. Eyiguler

{"title":"The 2022 Starlink Geomagnetic Storms: Global Thermospheric Response to a High‐Latitude Ionospheric Driver","authors":"D. Billett, Kian Sartipzadeh, M. F. Ivarsen, Elisabetta Iorﬁda, E. Doornbos, Emine Ceren, Kalafatoglu Eyig¨uler, K. Pandey, Kathryn A McWilliams, M. F. Ivarsen, E. C. K. Eyiguler","doi":"10.1029/2023sw003748","DOIUrl":"https://doi.org/10.1029/2023sw003748","url":null,"abstract":"In this study, we present ionospheric observations of field‐aligned currents from AMPERE and the ESA Swarm A satellite, in conjunction with high‐resolution thermospheric density measurements from accelerometers on board Swarm C and GRACE‐FO, for the third and 4 February 2022 geomagnetic storms that led to the loss of 38 Starlink internet satellites. We study the global storm time response of the thermospheric density enhancements, including their decay and latitudinal distribution. We find that the thermospheric density enhances globally in response to high‐latitude energy input from the magnetosphere‐solar wind system and takes at least a full day to recover to pre‐storm density levels. We also find that the greatest density perturbations occur at polar latitudes consistent with the magnetosphere‐ionosphere dayside cusp, and that there appeared to be a saturation of the thermospheric density during the geomagnetic storm on the fourth. Our results highlight the critical importance of high‐latitude ionospheric observations when diagnosing potentially hazardous conditions for low‐Earth‐orbit satellites.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"535 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139820947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification 利用贝叶斯深度学习方法和不确定性量化预测 SYM-H 指数

Space Weather Pub Date : 2024-02-01 DOI: 10.1029/2023sw003824

Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, V. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing

{"title":"Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification","authors":"Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, V. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing","doi":"10.1029/2023sw003824","DOIUrl":"https://doi.org/10.1029/2023sw003824","url":null,"abstract":"We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short‐term forecasts of the SYM‐H index based on 1‐ and 5‐min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM‐H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1‐ and 5‐min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. When predicting the SYM‐H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"16 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139817958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification 利用贝叶斯深度学习方法和不确定性量化预测 SYM-H 指数

Space Weather Pub Date : 2024-02-01 DOI: 10.1029/2023sw003824

Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, V. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing

{"title":"Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification","authors":"Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, V. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing","doi":"10.1029/2023sw003824","DOIUrl":"https://doi.org/10.1029/2023sw003824","url":null,"abstract":"We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short‐term forecasts of the SYM‐H index based on 1‐ and 5‐min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM‐H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1‐ and 5‐min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. When predicting the SYM‐H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"124 1-3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139877757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0