Space Weather最新文献

{"title":"Assimilating Space-Based Thermospheric Neutral Density (TND) Data Into the TIE-GCM Coupled Model During Periods With Low and High Solar Activity","authors":"Mona Kosary, Saeed Farzaneh, Maike Schumacher, Ehsan Forootan","doi":"10.1029/2023sw003811","DOIUrl":"https://doi.org/10.1029/2023sw003811","url":null,"abstract":"The global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space-based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space-based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm-C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm-B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE-GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"298 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140591954","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}

{"title":"Accuracy of Global Geospace Simulations: Influence of Solar Wind Monitor Location and Solar Wind Driving","authors":"Q. Al Shidi, T. I. Pulkkinen, D. Welling, G. Toth","doi":"10.1029/2023sw003747","DOIUrl":"https://doi.org/10.1029/2023sw003747","url":null,"abstract":"Some space weather models, such as the Space Weather Modeling Framework (SWMF) used in this study, use solar wind propagated from the first Lagrange point (L1) to the bow shock nose (BSN) to forecast geomagnetic storms. The SWMF is a highly coupled framework of space weather models that include multiple facets of the Geospace environment, such as the magnetosphere and ionosphere. The propagated solar wind measurements are used as a boundary condition for SWMF. The solar wind propagation method is a timeshift based on the calculated phase front normal (PFN) which leads to some uncertainties. For example, the propagated solar wind could have evolved during this timeshift. We use a data set of 123 geomagnetic storms between 2010 and 2019 run by the SWMF Geospace configuration to analyze the impact solar wind propagation and solar wind driving has on the geomagnetic indices. We look at the probability distributions of errors in SYM-H, cross polar cap potential (CPCP), and auroral electrojet indices AL and AU. Through studying the median errors (MdE), standard deviations and standardized regression coefficients, we find that the errors depend on the propagation parameters. Among the results, we show that the accuracy of the simulated SYM-H depends on the spacecraft distance from the Sun-Earth line. We also quantify the dependence of the standard deviation in SYM-H errors on the PFN and solar wind pressure. These statistics provide an insight into how the propagation method affects the final product of the simulation, which are the geomagnetic indices.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"51 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140324426","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}

{"title":"Geoelectric Field Estimations During Geomagnetic Storm in North China From SinoProbe Magnetotelluric Impedances","authors":"Qihe Shao, Ying Liu, Yinhe Luo, Graham Heinson, Yixian Xu, Jinsong Du, Chao Chen","doi":"10.1029/2023sw003758","DOIUrl":"https://doi.org/10.1029/2023sw003758","url":null,"abstract":"Evaluating the impact of geomagnetic disturbances on power grid infrastructure is critical to mitigate the risk posed by geomagnetically induced currents (GICs). In this paper, the geoelectric field and induced voltage distribution in North China were estimated from the SinoProbe magnetotelluric (MT) impedance data together with the geomagnetic observatory data of six INTERMAGNET stations recorded during the significant geomagnetic storm of 17th March 2015. The measured impedances from 119 SinoProbe MT sites were convolved with geomagnetic observatory data to account for the Earth's complex three-dimensional electrical resistivity structure. The resultant geoelectric field was then used to model the induced voltage distribution across the regional power transmission network in North China. Due to the large inter-site distances of the SinoProbe MT program, the derived geoelectric field is mostly homogeneous, except in the Ordos Basin that displays a polarization of the geoelectric field, and with higher magnitudes in the orogenic belts. The estimated geoelectric fields in Taihang-Lvliang, Yanshan, and Luxi orogenic belts of North China were large (>1 V/km) during the storm, due to high-resistivity lithosphere resulting in large voltage gradients in the Earth. However, in relation to locations of major power transmission lines, only the central part of North China experienced induced voltages exceeding 100 V.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"9 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140324454","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}

{"title":"Thank You to Our Peer Reviewers in 2023","authors":"Noe Lugaz, Brett Carter, Jennifer L. Gannon, Huixin Liu, Steve K. Morley, Shasha Zou","doi":"10.1029/2024sw003929","DOIUrl":"https://doi.org/10.1029/2024sw003929","url":null,"abstract":"<p>Peer reviewing is the foundation of modern scholarship, with external specialists being asked to fairly check and evaluate submitted work. This difficult and often time-consuming activity is performed voluntarily, with the understanding that one's own scholarship shall benefit down the line from a careful analysis of its assumption, results, accuracy, and yes, language, as we are now evaluating someone else's work. At <i>Space Weather</i>, we pride ourselves on a fair but quick review process yielding high-quality articles with a time from submission to first decision of about 2 months. This would not be possible without the hard work of all our reviewers. Once a year, we take the occasion to name these reviewers to thank them for their service to the journal and the community.</p>\u0000<p><i>Space Weather</i> relies on experts on diverse topics, ranging from plasma and space physics, to engineering, policy, and historical records. Over the course of 2023, authors submitted a record 340 manuscripts to <i>Space Weather</i> and 181 articles were published in our open access journal. With the solar maximum of solar cycle 25 expected within a year, we expect this growth to continue.</p>\u0000<p>In 2023, due to the increase in the number of submissions (which nearly doubled since 2017), <i>Space Weather</i> decided to rely more heavily on Associate Editors (AEs) and invited eight new AEs in addition to Xinan Yue, who has been serving since 2016. Those are Andrew Akala, Tanja Amerstorfer, Robyn Fiori, John Bosco Habarulema, Huiun Le, Vincent Maget, Romina Nikoukar, and Fang Shen. Most submissions are still handled throughout the review by one of the six editors, but for 15%–25% of them, one of the AEs will identify reviewers and make a recommendation to one of the editors. This hybrid model allows <i>Space Weather</i> to keep a clear editorial line, while enabling that the workload on the editors is manageable. This in turn ensures that each submitted article receives a timely and well-grounded decision.</p>\u0000<p>2023 was the last year of Huixin Liu as an editor after close to 6 years of service and we would like to sincerely thank her for her service and contributions to the journal. We are pleased to be joined by a new editor: Jiuhou Lei.</p>\u0000<p>Four hundred eight-six researchers performed 947 reviews, often for multiple revisions of the same manuscript and for multiple manuscripts. Their names are given below, with those who have reviewed three or more manuscripts italicized.</p>\u0000<p>A. Kouloumvakos</p>\u0000<p>Aaron Ridley</p>\u0000<p>Agri Faturahman</p>\u0000<p>Alessio Pignalberi</p>\u0000<p><i>Alex Chartier</i></p>\u0000<p>Alex Hands</p>\u0000<p>Alex Marcuello</p>\u0000<p>Alexa Halford</p>\u0000<p>Alexander Boyd</p>\u0000<p>Alexander Engell</p>\u0000<p>Alexey Danilov</p>\u0000<p><i>Alison Moraes</i></p>\u0000<p>Alisson Lago</p>\u0000<p>Allison Jaynes</p>\u0000<p>Ambelu Tebabal</p>\u0000<p>Amy Keesee</p>\u0000<p><i>Andong Hu</i></p>\u0000<p>Andres Aibar</p>\u0000<p>Andres Calabia</p>\u0000<p>Andrew Dimmock</p>\u0000<p>Andrew Smith</p>\u0000<p>Andrew Sun</p>\u0000<p","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"21 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140312874","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}

{"title":"Geomagnetic Disturbances Due To Neutral-Wind-Driven Ionospheric Currents","authors":"Cheng Sheng, Yue Deng, Daniel T. Welling, Steven K. Morley","doi":"10.1029/2023sw003750","DOIUrl":"https://doi.org/10.1029/2023sw003750","url":null,"abstract":"Previous simulation efforts on geomagnetic disturbances (GMDs) and geomagnetically induced currents (GICs) mostly rely on global magnetohydrodynamics models, which explicitly calculate the magnetospheric currents and carry certain assumptions about the ionosphere currents. Therefore, the role of ionospheric and thermospheric processes to GMDs has not been fully evaluated. In this study, Global Ionosphere Thermosphere Model simulations for an idealized storm event have been conducted. Simply, the high-latitude electrodynamic forcing (potential pattern and particle precipitation) has been specified by empirical models. GMDs due to neutral-wind driven currents have been compared to those caused by magnetospheric convection driven currents during both the main and recovery phases. At locations where the high-latitude electric potential is dominant, neutral-wind driven currents are found to contribute to about 10%–30% of the total GMDs. During the recovery phase when the ion-convection pattern retreats to high latitudes, neutral-wind driven currents become the primary sources for GMDs at middle latitudes on the dayside due to the “flywheel” effect and the large dayside conductance. Our result strongly suggests that ionospheric and thermospheric processes should not be neglected when estimating GMDs and therefore GICs.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140202865","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}

{"title":"A Framework for Evaluating Geomagnetic Indices Forecasting Models","authors":"Armando Collado-Villaverde, Pablo Muñoz, Consuelo Cid","doi":"10.1029/2024sw003868","DOIUrl":"https://doi.org/10.1029/2024sw003868","url":null,"abstract":"The use of Deep Learning models to forecast geomagnetic storms is achieving great results. However, the evaluation of these models is mainly supported on generic regression metrics (such as the Root Mean Squared Error or the Coefficient of Determination), which are not able to properly capture the specific particularities of geomagnetic storms forecasting. Particularly, they do not provide insights during the high activity periods. To overcome this issue, we introduce the Binned Forecasting Error to provide a more accurate assessment of models' performance across the different intensity levels of a geomagnetic storm. This metric facilitates a robust comparison of different forecasting models, presenting a true representation of a model's predictive capabilities while being resilient to different storms duration. In this direction, for enabling fair comparison among models, it is important to standardize the sets of geomagnetic storms for model training, validation and testing. To do this, we have started from the current sets used in the literature for forecasting the SYM-H, enriching them with newer storms not considered previously, focusing not only on disturbances caused by Coronal Mass Ejections but also addressing High-Speed Streams. To operationalize the evaluation framework, a comparative study is conducted between a baseline neural network model and a persistence model, showcasing the effectiveness of the new metric in evaluating forecasting performance during intense geomagnetic storms. Finally, we propose the use of preliminary measurements from ACE to evaluate the model performance in settings closer to an operational real-time scenario, where the forecasting models are expected to operate.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"18 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140181985","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}

{"title":"Ionospheric TEC Prediction Based on Ensemble Learning Models","authors":"Yang Zhou, Jing Liu, Shuhan Li, Qiaoling Li","doi":"10.1029/2023sw003790","DOIUrl":"https://doi.org/10.1029/2023sw003790","url":null,"abstract":"In this paper, we propose the usage of an ensemble learning approach for predicting total electron content (TEC). The training data set spans from 2007 to 2016, while the testing data set is set to the year 2017. The model inputs in our study included Solar radio flux (F107), Solar Wind plasma speed, By, Bz, Dst, Ap, AE, day of year, universal time, 30-day and 90-day TEC averages. Specifically, eXtreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree, and Decision Tree were utilized for 1-hr TEC prediction at high- (80°W, 80°N), mid- (80°W, 40°N), and low- latitudes (80°W, 10°N). Results indicate that all three models performed well in predicting TEC, with a mean error of only approximately 0.6 TECU at high- and mid- latitudes and 1.13 TECU at low latitudes. At the same time, we compared the model with 1-day Beijing University of Aeronautics and Astronautics model during the period of magnetic storm from 25 August 2018 to 27 August 2018 and a quiet period from 13 December 2018 to 15 December 2018. In the magnetic storm period, Our model showed an average reduction of 1.83 TECU compared to BUAA model. During the quiet period, XGBoost exhibit an average error that is 1.14 TECU lower than that of BUAA model. Moreover, TEC prediction over the region between the 20°N–45°N and 70°E−120°E during geomagnetic storm has an error of 2.74 TECU, showing the stability and superiority of XGBoost. Overall, the ensemble learning approach exhibits its advantage in predicting TEC.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"15 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140182028","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}

{"title":"Modeling Ionospheric TEC Using Gradient Boosting Based and Stacking Machine Learning Techniques","authors":"Ayanew Nigusie, Ambelu Tebabal, Roman Galas","doi":"10.1029/2023sw003821","DOIUrl":"https://doi.org/10.1029/2023sw003821","url":null,"abstract":"Accurately predicting and modeling the ionospheric total electron content (TEC) can greatly improve the accuracy of satellite navigation and positioning and help to correct ionospheric delay. This study assesses the effectiveness of four different machine learning (ML) models in predicting hourly vertical TEC (VTEC) data for a single-station study over Ethiopia. The models employed include gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) algorithms, and a stacked combination of these algorithms with a linear regression algorithm. The models relied on input variables that represent solar activity, geomagnetic activity, season, time of the day, interplanetary magnetic field, and solar wind. The models were trained using the VTEC data from January 2011 to December 2018, excluding the testing data. The testing data comprised the data for the year 2015 and the initial 6 months of 2017. The RandomizedSearchCV algorithm was used to determine the optimal hyperparameters of the models. The predicted VTEC values of the four ML models were strongly correlated with the GPS VTEC, with a correlation coefficient of ∼0.96, which is significantly higher than the corresponding value of the International Reference Ionosphere (IRI 2020) model, which is 0.87. Comparing the GPS VTEC values with the predicted VTEC values based on diurnal and seasonal characteristics showed that the predictions of the developed models were generally in good agreement and outperformed the IRI 2020 model. Overall, the ML models used in this study demonstrated promising potential for accurate single-station VTEC prediction over Ethiopia.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"108 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140148955","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}

{"title":"Using ICON Satellite Data to Forecast Equatorial Ionospheric Instability Throughout 2022","authors":"D. L. Hysell, A. Kirchman, B. J. Harding, R. A. Heelis, S. L. England, H. U. Frey, S. B. Mende","doi":"10.1029/2023sw003817","DOIUrl":"https://doi.org/10.1029/2023sw003817","url":null,"abstract":"Numerical forecasts of plasma convective instability in the postsunset equatorial ionosphere are made based on data from the Ionospheric Connections Explorer satellite (ICON) following the method outlined in a previous study. Data are selected from pairs of successive orbits. Data from the first orbit in the pair are used to initialize and force a numerical forecast simulation, and data from the second orbit are used to validate the results 104 min later. Data from the IVM plasma density and drifts instrument and the MIGHTI red-line thermospheric winds instrument are used to force the forecast model. Thirteen (16) data set pairs from August (October), 2022, are considered. Forecasts produced one false negative in August and another false negative in October. Possible causes of forecast discrepancies are evaluated including the failure to initialize the numerical simulations with electron density profiles measured concurrently. Volume emission 135.6-nm OI profiles from the Far Ultraviolet (FUV) instrument on ICON are considered in the evaluation.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"18 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140128322","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}

{"title":"On Generalized Additive Models for Representation of Solar EUV Irradiance","authors":"Daniel A. Brandt, Erick F. Vega, Aaron J. Ridley","doi":"10.1029/2023sw003680","DOIUrl":"https://doi.org/10.1029/2023sw003680","url":null,"abstract":"In the context of space weather forecasting, solar EUV irradiance specification is needed on multiple time scales, with associated uncertainty quantification for determining the accuracy of downstream parameters. Empirical models of irradiance often rely on parametric fits between irradiance in several bands and various solar indices. We build upon these empirical models by using Generalized Additive Models (GAMs) to represent solar irradiance. We apply the GAM approach in two steps: (a) A GAM is fitted between FISM2 irradiance and solar indices F10.7, Revised Sunspot Number, and the Lyman-<i>α</i> solar index. (b) A second GAM is fit to model the residuals of the first GAM with respect to FISM2 irradiance. We evaluate the performance of this approach during Solar Cycle 24 using GAMs driven by known solar indices as well as those forecasted 3 days ahead with an autoregressive modeling approach. We demonstrate negligible dependence of performance on solar cycle and season, and we assess the efficacy of the GAM approach across different wavelengths.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"109 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140128146","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}