Noé Lugaz, Huixin Liu, Brett A. Carter, Jennifer Gannon, Shasha Zou, Steven K. Morley
Abstract The monthly mean sunspot number has been larger in June–July 2023 than the double peak of solar cycle 24 (146 in February 2014 and 139 in November 2011) and brings us back to the sunspot level of solar cycle 23. However, the number of rocket launches, satellites in orbit and private space companies has increased dramatically in the past 20 years. Additionally, there is a growing interest for space exploration beyond Earth's orbit, to the Moon and beyond, which comes with higher risk of being affected by space weather. Here, we discuss some of these trends and the role of the journal to improve awareness of space weather impacts.
{"title":"New Space Companies Meet a “Normal” Solar Maximum","authors":"Noé Lugaz, Huixin Liu, Brett A. Carter, Jennifer Gannon, Shasha Zou, Steven K. Morley","doi":"10.1029/2023sw003702","DOIUrl":"https://doi.org/10.1029/2023sw003702","url":null,"abstract":"Abstract The monthly mean sunspot number has been larger in June–July 2023 than the double peak of solar cycle 24 (146 in February 2014 and 139 in November 2011) and brings us back to the sunspot level of solar cycle 23. However, the number of rocket launches, satellites in orbit and private space companies has increased dramatically in the past 20 years. Additionally, there is a growing interest for space exploration beyond Earth's orbit, to the Moon and beyond, which comes with higher risk of being affected by space weather. Here, we discuss some of these trends and the role of the journal to improve awareness of space weather impacts.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135388038","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}
Xin Cao, Xiangning Chu, Jacob Bortnik, James M. Weygand, Jinxing Li, Homayon Aryan, Donglai Ma
Abstract A predictive model for the variation of ionospheric currents is of great scientific and practical importance to our modern industrial society. To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neural network model trained on the Equivalent Ionospheric Current (EIC) data from 1st January 2007 to 31st December 2019. Due to the highly imbalanced nature of the ionospheric currents data, which means that the data of extreme events are much less than those of quiet times, we utilized different loss functions to improve the model performance. Our model demonstrates the potential to predict the active events of ionospheric currents reasonably well (e.g., EICs during substorms) within a timescale of a few minutes. Although the data used for training are measurements over the North American and Greenland sectors, our model is not only able to predict EICs within this region, but is also able to provide a promising out‐of‐sample prediction on a global scale.
{"title":"The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model","authors":"Xin Cao, Xiangning Chu, Jacob Bortnik, James M. Weygand, Jinxing Li, Homayon Aryan, Donglai Ma","doi":"10.1029/2023sw003506","DOIUrl":"https://doi.org/10.1029/2023sw003506","url":null,"abstract":"Abstract A predictive model for the variation of ionospheric currents is of great scientific and practical importance to our modern industrial society. To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neural network model trained on the Equivalent Ionospheric Current (EIC) data from 1st January 2007 to 31st December 2019. Due to the highly imbalanced nature of the ionospheric currents data, which means that the data of extreme events are much less than those of quiet times, we utilized different loss functions to improve the model performance. Our model demonstrates the potential to predict the active events of ionospheric currents reasonably well (e.g., EICs during substorms) within a timescale of a few minutes. Although the data used for training are measurements over the North American and Greenland sectors, our model is not only able to predict EICs within this region, but is also able to provide a promising out‐of‐sample prediction on a global scale.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135394140","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}
Jianhui He, Elvira Astafyeva, Xinan Yue, Nicholas M. Pedatella, Dong Lin, Timothy J. Fuller‐Rowell, Mariangel Fedrizzi, Mihail Codrescu, Eelco Doornbos, Christian Siemes, Sean Bruinsma, Frederic Pitout, Adam Kubaryk
Abstract On 3 February 2022, at 18:13 UTC, SpaceX launched and a short time later deployed 49 Starlink satellites at an orbit altitude between 210 and 320 km. The satellites were meant to be further raised to 550 km. However, the deployment took place during the main phase of a moderate geomagnetic storm, and another moderate storm occurred on the next day. The resulting increase in atmospheric drag led to 38 out of the 49 satellites reentering the atmosphere in the following days. In this work, we use both observations and simulations to perform a detailed investigation of the thermospheric conditions during this storm. Observations at higher altitudes, by Swarm‐A (∼438 km, 09/21 Local Time [LT]) and the Gravity Recovery and Climate Experiment Follow‐On (∼505 km, 06/18 LT) missions show that during the main phase of the storms the neutral mass density increased by 110% and 120%, respectively. The storm‐time enhancement extended to middle and low latitudes and was stronger in the northern hemisphere. To further investigate the thermospheric variations, we used six empirical and first‐principle numerical models. We found the models captured the upper and lower thermosphere changes, however, their simulated density enhancements differ by up to 70%. Further, the models showed that at the low orbital altitudes of the Starlink satellites (i.e., 200–300 km) the global averaged storm‐time density enhancement reached up to ∼35%–60%. Although such storm effects are far from the largest, they seem to be responsible for the reentry of the 38 satellites.
{"title":"Comparison of Empirical and Theoretical Models of the Thermospheric Density Enhancement During the 3–4 February 2022 Geomagnetic Storm","authors":"Jianhui He, Elvira Astafyeva, Xinan Yue, Nicholas M. Pedatella, Dong Lin, Timothy J. Fuller‐Rowell, Mariangel Fedrizzi, Mihail Codrescu, Eelco Doornbos, Christian Siemes, Sean Bruinsma, Frederic Pitout, Adam Kubaryk","doi":"10.1029/2023sw003521","DOIUrl":"https://doi.org/10.1029/2023sw003521","url":null,"abstract":"Abstract On 3 February 2022, at 18:13 UTC, SpaceX launched and a short time later deployed 49 Starlink satellites at an orbit altitude between 210 and 320 km. The satellites were meant to be further raised to 550 km. However, the deployment took place during the main phase of a moderate geomagnetic storm, and another moderate storm occurred on the next day. The resulting increase in atmospheric drag led to 38 out of the 49 satellites reentering the atmosphere in the following days. In this work, we use both observations and simulations to perform a detailed investigation of the thermospheric conditions during this storm. Observations at higher altitudes, by Swarm‐A (∼438 km, 09/21 Local Time [LT]) and the Gravity Recovery and Climate Experiment Follow‐On (∼505 km, 06/18 LT) missions show that during the main phase of the storms the neutral mass density increased by 110% and 120%, respectively. The storm‐time enhancement extended to middle and low latitudes and was stronger in the northern hemisphere. To further investigate the thermospheric variations, we used six empirical and first‐principle numerical models. We found the models captured the upper and lower thermosphere changes, however, their simulated density enhancements differ by up to 70%. Further, the models showed that at the low orbital altitudes of the Starlink satellites (i.e., 200–300 km) the global averaged storm‐time density enhancement reached up to ∼35%–60%. Although such storm effects are far from the largest, they seem to be responsible for the reentry of the 38 satellites.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135434340","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}
Elena Marshalko, Mikhail Kruglyakov, Alexey Kuvshinov, Ari Viljanen
Abstract In this study, we perform three‐dimensional (3‐D) ground electric field (GEF) modeling in Fennoscandia for three days of the Halloween geomagnetic storm (29–31 October 2003) using magnetic field data from the International Monitor for Auroral Geomagnetic Effects (IMAGE) magnetometer network and a 3‐D conductivity model of the region. To explore the influence of the inducing source model on 3‐D GEF simulations, we consider three different approaches to source approximation. Within the first two approaches, the source varies laterally, whereas in the third method, the GEF is calculated by implementing the time‐domain realization of the magnetotelluric intersite impedance method. We then compare GEF‐based geomagnetically induced current (GIC) with observations at the Mäntsälä natural gas pipeline recording point. We conclude that a high correlation between modeled and recorded GIC is observed for all considered approaches. The highest correlation is achieved when performing a 3‐D GEF simulation using a “conductivity‐based” laterally nonuniform inducing source. Our results also highlight the strong dependence of the GEF on the earth's conductivity distribution.
{"title":"Three‐Dimensional Modeling of the Ground Electric Field in Fennoscandia During the Halloween Geomagnetic Storm","authors":"Elena Marshalko, Mikhail Kruglyakov, Alexey Kuvshinov, Ari Viljanen","doi":"10.1029/2022sw003370","DOIUrl":"https://doi.org/10.1029/2022sw003370","url":null,"abstract":"Abstract In this study, we perform three‐dimensional (3‐D) ground electric field (GEF) modeling in Fennoscandia for three days of the Halloween geomagnetic storm (29–31 October 2003) using magnetic field data from the International Monitor for Auroral Geomagnetic Effects (IMAGE) magnetometer network and a 3‐D conductivity model of the region. To explore the influence of the inducing source model on 3‐D GEF simulations, we consider three different approaches to source approximation. Within the first two approaches, the source varies laterally, whereas in the third method, the GEF is calculated by implementing the time‐domain realization of the magnetotelluric intersite impedance method. We then compare GEF‐based geomagnetically induced current (GIC) with observations at the Mäntsälä natural gas pipeline recording point. We conclude that a high correlation between modeled and recorded GIC is observed for all considered approaches. The highest correlation is achieved when performing a 3‐D GEF simulation using a “conductivity‐based” laterally nonuniform inducing source. Our results also highlight the strong dependence of the GEF on the earth's conductivity distribution.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135889102","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 Space launches produce ionospheric disturbances which can be observed through measurements such as Global Navigation Satellite System signal delays. Here we report observations and numerical simulations of the ionospheric depletion due to a Small‐Lift Launch Vehicle. The case examined was the launch of a Rocket Lab Electron at 22:30 UTC on 22 March 2021. Despite the very small launch vehicle, ground stations in the Chatham Islands measured decreases in slant total electron content for navigation satellite signals following the launch. Global Ionosphere Thermosphere Model results indicated ionospheric depletions which were comparable with these measurements. Measurements indicated a maximum decrease of 2.7 TECU in vertical total electron content, compared with a simulated decrease of 2.6 TECU. Advection of the exhaust plume due to its initial velocity and subsequent effects of neutral winds are identified as some remaining challenges for this form of modeling.
{"title":"Numerical Modeling and GNSS Observations of Ionospheric Depletions Due To a Small‐Lift Launch Vehicle","authors":"G. W. Bowden, M. Brown","doi":"10.1029/2023sw003563","DOIUrl":"https://doi.org/10.1029/2023sw003563","url":null,"abstract":"Abstract Space launches produce ionospheric disturbances which can be observed through measurements such as Global Navigation Satellite System signal delays. Here we report observations and numerical simulations of the ionospheric depletion due to a Small‐Lift Launch Vehicle. The case examined was the launch of a Rocket Lab Electron at 22:30 UTC on 22 March 2021. Despite the very small launch vehicle, ground stations in the Chatham Islands measured decreases in slant total electron content for navigation satellite signals following the launch. Global Ionosphere Thermosphere Model results indicated ionospheric depletions which were comparable with these measurements. Measurements indicated a maximum decrease of 2.7 TECU in vertical total electron content, compared with a simulated decrease of 2.6 TECU. Advection of the exhaust plume due to its initial velocity and subsequent effects of neutral winds are identified as some remaining challenges for this form of modeling.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135299035","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 Space weather indices are used commonly to drive forecasts of thermosphere density, which affects objects in low‐Earth orbit (LEO) through atmospheric drag. One commonly used space weather proxy, F 10.7cm , correlates well with solar extreme ultra‐violet (EUV) energy deposition into the thermosphere. Currently, the USAF contracts Space Environment Technologies (SET), which uses a linear algorithm to forecast F 10.7cm . In this work, we introduce methods using neural network ensembles with multi‐layer perceptrons (MLPs) and long‐short term memory (LSTMs) to improve on the SET predictions. We make predictions only from historical F 10.7cm values. We investigate data manipulation methods (backwards averaging and lookback) as well as multi step and dynamic forecasting. This work shows an improvement over the popular persistence and the operational SET model when using ensemble methods. The best models found in this work are ensemble approaches using multi step or a combination of multi step and dynamic predictions. Nearly all approaches offer an improvement, with the best models improving between 48% and 59% on relative MSE with respect to persistence. Other relative error metrics were shown to improve greatly when ensembles methods were used. We were also able to leverage the ensemble approach to provide a distribution of predicted values; allowing an investigation into forecast uncertainty. Our work found models that produced less biased predictions at elevated and high solar activity levels. Uncertainty was also investigated through the use of a calibration error score metric (CES), our best ensemble reached similar CES as other work.
{"title":"Probabilistic Solar Proxy Forecasting With Neural Network Ensembles","authors":"Joshua D. Daniell, Piyush M. Mehta","doi":"10.1029/2023sw003675","DOIUrl":"https://doi.org/10.1029/2023sw003675","url":null,"abstract":"Abstract Space weather indices are used commonly to drive forecasts of thermosphere density, which affects objects in low‐Earth orbit (LEO) through atmospheric drag. One commonly used space weather proxy, F 10.7cm , correlates well with solar extreme ultra‐violet (EUV) energy deposition into the thermosphere. Currently, the USAF contracts Space Environment Technologies (SET), which uses a linear algorithm to forecast F 10.7cm . In this work, we introduce methods using neural network ensembles with multi‐layer perceptrons (MLPs) and long‐short term memory (LSTMs) to improve on the SET predictions. We make predictions only from historical F 10.7cm values. We investigate data manipulation methods (backwards averaging and lookback) as well as multi step and dynamic forecasting. This work shows an improvement over the popular persistence and the operational SET model when using ensemble methods. The best models found in this work are ensemble approaches using multi step or a combination of multi step and dynamic predictions. Nearly all approaches offer an improvement, with the best models improving between 48% and 59% on relative MSE with respect to persistence. Other relative error metrics were shown to improve greatly when ensembles methods were used. We were also able to leverage the ensemble approach to provide a distribution of predicted values; allowing an investigation into forecast uncertainty. Our work found models that produced less biased predictions at elevated and high solar activity levels. Uncertainty was also investigated through the use of a calibration error score metric (CES), our best ensemble reached similar CES as other work.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135149912","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}
Opal Issan, Pete Riley, Enrico Camporeale, Boris Kramer
Abstract The ambient solar wind plays a significant role in propagating interplanetary coronal mass ejections and is an important driver of space weather geomagnetic storms. A computationally efficient and widely used method to predict the ambient solar wind radial velocity near Earth involves coupling three models: Potential Field Source Surface, Wang‐Sheeley‐Arge (WSA), and Heliospheric Upwind eXtrapolation. However, the model chain has 11 uncertain parameters that are mainly non‐physical due to empirical relations and simplified physics assumptions. We, therefore, propose a comprehensive uncertainty quantification (UQ) framework that is able to successfully quantify and reduce parametric uncertainties in the model chain. The UQ framework utilizes variance‐based global sensitivity analysis followed by Bayesian inference via Markov chain Monte Carlo to learn the posterior densities of the most influential parameters. The sensitivity analysis results indicate that the five most influential parameters are all WSA parameters. Additionally, we show that the posterior densities of such influential parameters vary greatly from one Carrington rotation to the next. The influential parameters are trying to overcompensate for the missing physics in the model chain, highlighting the need to enhance the robustness of the model chain to the choice of WSA parameters. The ensemble predictions generated from the learned posterior densities significantly reduce the uncertainty in solar wind velocity predictions near Earth.
{"title":"Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction","authors":"Opal Issan, Pete Riley, Enrico Camporeale, Boris Kramer","doi":"10.1029/2023sw003555","DOIUrl":"https://doi.org/10.1029/2023sw003555","url":null,"abstract":"Abstract The ambient solar wind plays a significant role in propagating interplanetary coronal mass ejections and is an important driver of space weather geomagnetic storms. A computationally efficient and widely used method to predict the ambient solar wind radial velocity near Earth involves coupling three models: Potential Field Source Surface, Wang‐Sheeley‐Arge (WSA), and Heliospheric Upwind eXtrapolation. However, the model chain has 11 uncertain parameters that are mainly non‐physical due to empirical relations and simplified physics assumptions. We, therefore, propose a comprehensive uncertainty quantification (UQ) framework that is able to successfully quantify and reduce parametric uncertainties in the model chain. The UQ framework utilizes variance‐based global sensitivity analysis followed by Bayesian inference via Markov chain Monte Carlo to learn the posterior densities of the most influential parameters. The sensitivity analysis results indicate that the five most influential parameters are all WSA parameters. Additionally, we show that the posterior densities of such influential parameters vary greatly from one Carrington rotation to the next. The influential parameters are trying to overcompensate for the missing physics in the model chain, highlighting the need to enhance the robustness of the model chain to the choice of WSA parameters. The ensemble predictions generated from the learned posterior densities significantly reduce the uncertainty in solar wind velocity predictions near Earth.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135248443","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}
S. Y. Li, E. A. Kronberg, C. G. Mouikis, H. Luo, Y. S. Ge, A. M. Du
Abstract The information on plasma pressure in the outer part of the inner magnetosphere is important for simulations of the inner magnetosphere and a better understanding of its dynamics. Based on 17‐year observations from both Cluster Ion Spectrometry and Research with Adaptive Particle Imaging Detector instruments onboard the Cluster mission, we used machine‐learning‐based models to predict proton plasma pressure at energies from ∼40 eV to 4 MeV in the outer part of the inner magnetosphere ( = 5–9). Proton pressure distributions are assumed to be isotropic. The location in the magnetosphere, the property of stably trapped particles, and parameters of solar, solar wind, and geomagnetic activity from the OMNI database are used as predictors. We trained several different machine‐learning‐based models and compared their performances with observations. The results demonstrate that the Extra‐Trees Regressor has the best predicting performance. The Spearman correlation between the observations and predictions by the model is about 70%. The most important parameter for predicting proton pressure in our model is the value, which relates to the property of stably trapped particles. The most important predictor of solar and geomagnetic activity is F 10.7 index. Based on the observations and predictions by our model, we find that no matter under quiet or disturbed geomagnetic conditions, both the dusk‐dawn asymmetry at the dayside with higher pressure at the duskside and the day‐night asymmetry with higher pressure at the nightside occur. Our results have direct practical applications, for instance, inputs for simulations of the inner magnetosphere or the reconstruction of the 3‐D magnetospheric electric current system based on the magnetostatic equilibrium.
{"title":"Prediction of Proton Pressure in the Outer Part of the Inner Magnetosphere Using Machine Learning","authors":"S. Y. Li, E. A. Kronberg, C. G. Mouikis, H. Luo, Y. S. Ge, A. M. Du","doi":"10.1029/2022sw003387","DOIUrl":"https://doi.org/10.1029/2022sw003387","url":null,"abstract":"Abstract The information on plasma pressure in the outer part of the inner magnetosphere is important for simulations of the inner magnetosphere and a better understanding of its dynamics. Based on 17‐year observations from both Cluster Ion Spectrometry and Research with Adaptive Particle Imaging Detector instruments onboard the Cluster mission, we used machine‐learning‐based models to predict proton plasma pressure at energies from ∼40 eV to 4 MeV in the outer part of the inner magnetosphere ( = 5–9). Proton pressure distributions are assumed to be isotropic. The location in the magnetosphere, the property of stably trapped particles, and parameters of solar, solar wind, and geomagnetic activity from the OMNI database are used as predictors. We trained several different machine‐learning‐based models and compared their performances with observations. The results demonstrate that the Extra‐Trees Regressor has the best predicting performance. The Spearman correlation between the observations and predictions by the model is about 70%. The most important parameter for predicting proton pressure in our model is the value, which relates to the property of stably trapped particles. The most important predictor of solar and geomagnetic activity is F 10.7 index. Based on the observations and predictions by our model, we find that no matter under quiet or disturbed geomagnetic conditions, both the dusk‐dawn asymmetry at the dayside with higher pressure at the duskside and the day‐night asymmetry with higher pressure at the nightside occur. Our results have direct practical applications, for instance, inputs for simulations of the inner magnetosphere or the reconstruction of the 3‐D magnetospheric electric current system based on the magnetostatic equilibrium.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135349548","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 Characterization of the global ionospheric irregularities as a function of local time, longitude, altitude, and magnetic activities is still a challenge for radio frequency operations, especially at the low‐latitude region. One of the main reasons is lack of observations due to the unevenly distributed instruments. To overcome this constraint, we developed a new spatial density gradient index (DGRI) at two different scale sizes: small scale and medium/large scale. The DGRI is derived from in situ density measurements onboard recently launched constellation of low‐Earth‐orbiting satellites (COSMIC‐2 and ICON) at the rate of 1 Hz. Hence, the DGRI appeared to be suitable parameter that can be used as a proxy to describe the essential features of ionospheric disturbances that may critically affect our radio wave application as well as to identify the “ all clear ” zone as a function of longitude, latitude, and local time—at a refreshment rate of 30 min or less.
{"title":"New Index to Characterize Ionospheric Irregularity Distribution","authors":"Endawoke Yizengaw","doi":"10.1029/2023sw003469","DOIUrl":"https://doi.org/10.1029/2023sw003469","url":null,"abstract":"Abstract Characterization of the global ionospheric irregularities as a function of local time, longitude, altitude, and magnetic activities is still a challenge for radio frequency operations, especially at the low‐latitude region. One of the main reasons is lack of observations due to the unevenly distributed instruments. To overcome this constraint, we developed a new spatial density gradient index (DGRI) at two different scale sizes: small scale and medium/large scale. The DGRI is derived from in situ density measurements onboard recently launched constellation of low‐Earth‐orbiting satellites (COSMIC‐2 and ICON) at the rate of 1 Hz. Hence, the DGRI appeared to be suitable parameter that can be used as a proxy to describe the essential features of ionospheric disturbances that may critically affect our radio wave application as well as to identify the “ all clear ” zone as a function of longitude, latitude, and local time—at a refreshment rate of 30 min or less.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"31 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134993669","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}
Martin G Mlynczak, Delores J Knipp, Linda A Hunt, John Gaebler, Tomoko Matsuo, Liam M Kilcommons, Cindy L Young
Infrared radiative cooling by nitric oxide (NO) and carbon dioxide (CO2) modulates the thermosphere's density and thermal response to geomagnetic storms. Satellite tracking and collision avoidance planning require accurate density forecasts during these events. Over the past several years, failed density forecasts have been tied to the onset of rapid and significant cooling due to production of NO and its associated radiative cooling via emission of infrared radiation at 5.3 μm. These results have been diagnosed, after the fact, through analyses of measurements of infrared cooling made by the Sounding of the Atmosphere using Broadband Emission Radiometry instrument now in orbit over 16 years on the National Aeronautics and Space Administration Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics satellite. Radiative cooling rates for NO and CO2 have been further shown to be directly correlated with composition and exospheric temperature changes during geomagnetic storms. These results strongly suggest that a network of smallsats observing the infrared radiative cooling of the thermosphere could serve as space weather sentinels. These sentinels would observe and provide radiative cooling rate data in real time to generate nowcasts of density and aerodynamic drag on space vehicles. Currently, radiative cooling is not directly considered in operational space weather forecast models. In addition, recent research has shown that different geomagnetic storm types generate substantially different infrared radiative response, and hence, substantially different thermospheric density response. The ability to identify these storms, and to measure and predict the Earth's response to them, should enable substantial improvement in thermospheric density forecasts.
{"title":"Space-Based Sentinels for Measurement of Infrared Cooling in the Thermosphere for Space Weather Nowcasting and Forecasting.","authors":"Martin G Mlynczak, Delores J Knipp, Linda A Hunt, John Gaebler, Tomoko Matsuo, Liam M Kilcommons, Cindy L Young","doi":"10.1002/2017SW001757","DOIUrl":"https://doi.org/10.1002/2017SW001757","url":null,"abstract":"<p><p>Infrared radiative cooling by nitric oxide (NO) and carbon dioxide (CO<sub>2</sub>) modulates the thermosphere's density and thermal response to geomagnetic storms. Satellite tracking and collision avoidance planning require accurate density forecasts during these events. Over the past several years, failed density forecasts have been tied to the onset of rapid and significant cooling due to production of NO and its associated radiative cooling via emission of infrared radiation at 5.3 μm. These results have been diagnosed, after the fact, through analyses of measurements of infrared cooling made by the Sounding of the Atmosphere using Broadband Emission Radiometry instrument now in orbit over 16 years on the National Aeronautics and Space Administration Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics satellite. Radiative cooling rates for NO and CO<sub>2</sub> have been further shown to be directly correlated with composition and exospheric temperature changes during geomagnetic storms. These results strongly suggest that a network of smallsats observing the infrared radiative cooling of the thermosphere could serve as space weather sentinels. These sentinels would observe and provide radiative cooling rate data in real time to generate nowcasts of density and aerodynamic drag on space vehicles. Currently, radiative cooling is not directly considered in operational space weather forecast models. In addition, recent research has shown that different geomagnetic storm types generate substantially different infrared radiative response, and hence, substantially different thermospheric density response. The ability to identify these storms, and to measure and predict the Earth's response to them, should enable substantial improvement in thermospheric density forecasts.</p>","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"16 4","pages":"363-375"},"PeriodicalIF":3.7,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/2017SW001757","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41217786","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：