Xiaomin Luo, Zhuang Chen, Shengfeng Gu, Neng Yue, Tao Yue
Abstract Global Positioning System (GPS) Precise Point Positioning (PPP) with correct fixing ambiguity resolution (AR) can reach cm‐mm level positioning accuracy. However, this accuracy can be degraded by the geomagnetic storm effects. To comprehensively investigate the ambiguity resolved percentage (ARP) of GPS kinematic PPP, referred to as PPP‐ARP, under different intensities of geomagnetic storms, based on the Natural Resources Canada's Canadian Spatial Reference System (CSRS) PPP, this study for the first time gives the correlation between the PPP‐ARP and storm intensity using 67 storms occurred in the past 5 years of 2018–2022. Experimental results indicate that the PPP‐ARP decreases gradually as the increase of geomagnetic storm intensity. Under quiet and low geomagnetic conditions (Dst min > −50 nT), the PPP‐ARP of global GNSS stations can achieve more than 96%, while these during strong storms (Dst min ≤ −100 nT) are generally lower than 90.0%, especially for the PPP‐ARP of some stations located at low latitudes which are lower than 40.0%. The mechanism of PPP‐ARP decrease under geomagnetic storms is mainly due to the cycle slips and even loss of lock of GNSS signals caused by the storms induced ionospheric disturbances and scintillations. In addition, different from many previous studies, we found that the CSRS‐PPP with AR can achieve good positioning accuracy (3D RMS <0.2 m) even under strong geomagnetic storms.
{"title":"Studying the Fixing Rate of GPS PPP Ambiguity Resolution Under Different Geomagnetic Storm Intensities","authors":"Xiaomin Luo, Zhuang Chen, Shengfeng Gu, Neng Yue, Tao Yue","doi":"10.1029/2023sw003542","DOIUrl":"https://doi.org/10.1029/2023sw003542","url":null,"abstract":"Abstract Global Positioning System (GPS) Precise Point Positioning (PPP) with correct fixing ambiguity resolution (AR) can reach cm‐mm level positioning accuracy. However, this accuracy can be degraded by the geomagnetic storm effects. To comprehensively investigate the ambiguity resolved percentage (ARP) of GPS kinematic PPP, referred to as PPP‐ARP, under different intensities of geomagnetic storms, based on the Natural Resources Canada's Canadian Spatial Reference System (CSRS) PPP, this study for the first time gives the correlation between the PPP‐ARP and storm intensity using 67 storms occurred in the past 5 years of 2018–2022. Experimental results indicate that the PPP‐ARP decreases gradually as the increase of geomagnetic storm intensity. Under quiet and low geomagnetic conditions (Dst min > −50 nT), the PPP‐ARP of global GNSS stations can achieve more than 96%, while these during strong storms (Dst min ≤ −100 nT) are generally lower than 90.0%, especially for the PPP‐ARP of some stations located at low latitudes which are lower than 40.0%. The mechanism of PPP‐ARP decrease under geomagnetic storms is mainly due to the cycle slips and even loss of lock of GNSS signals caused by the storms induced ionospheric disturbances and scintillations. In addition, different from many previous studies, we found that the CSRS‐PPP with AR can achieve good positioning accuracy (3D RMS <0.2 m) even under strong geomagnetic storms.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"17 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":"136199371","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}
Homayon Aryan, Jacob Bortnik, W. Kent Tobiska, Piyush Mehta, Rashmi Siddalingappa
Abstract It is believed that galactic cosmic rays and solar energetic particles are the two major sources of ionizing radiation. However, the radiation source may also be due to relativistic electrons that are associated with precipitation from the Van Allen radiation belts. In this study, we use Automated Radiation Measurements for Aerospace Safety (ARMAS) measurements to investigate the precipitation mechanism of energetic radiation belt electrons. ARMAS instruments are flown on agency‐sponsored (NASA, National Oceanic and Atmospheric Administration, National Science Foundation, Federal Aviation Administration, DOE) flights, commercial space transportation companies and airliners (>9 km) in automated radiation collection mode. We identified magnetic conjunction events between ARMAS and NASA's Van Allen Probes to study the highly variable, dynamic mesoscale radiation events observed by ARMAS instruments at aviation altitudes and their relationship to various plasma waves in the inner magnetosphere measured by the Van Allen Probes. The results show that there is a strong correlation between dose rates observed by ARMAS and plasmaspheric hiss wave power measured by the Van Allen Probes, but no such relationship with electromagnetic ion cyclotron waves and only a modest correlation with whistler mode chorus waves. These results suggest that the space environment could have a potentially significant effect on passenger safety.
{"title":"Enhanced Radiation Levels at Aviation Altitudes and Their Relationship to Plasma Waves in the Inner Magnetosphere","authors":"Homayon Aryan, Jacob Bortnik, W. Kent Tobiska, Piyush Mehta, Rashmi Siddalingappa","doi":"10.1029/2023sw003477","DOIUrl":"https://doi.org/10.1029/2023sw003477","url":null,"abstract":"Abstract It is believed that galactic cosmic rays and solar energetic particles are the two major sources of ionizing radiation. However, the radiation source may also be due to relativistic electrons that are associated with precipitation from the Van Allen radiation belts. In this study, we use Automated Radiation Measurements for Aerospace Safety (ARMAS) measurements to investigate the precipitation mechanism of energetic radiation belt electrons. ARMAS instruments are flown on agency‐sponsored (NASA, National Oceanic and Atmospheric Administration, National Science Foundation, Federal Aviation Administration, DOE) flights, commercial space transportation companies and airliners (>9 km) in automated radiation collection mode. We identified magnetic conjunction events between ARMAS and NASA's Van Allen Probes to study the highly variable, dynamic mesoscale radiation events observed by ARMAS instruments at aviation altitudes and their relationship to various plasma waves in the inner magnetosphere measured by the Van Allen Probes. The results show that there is a strong correlation between dose rates observed by ARMAS and plasmaspheric hiss wave power measured by the Van Allen Probes, but no such relationship with electromagnetic ion cyclotron waves and only a modest correlation with whistler mode chorus waves. These results suggest that the space environment could have a potentially significant effect on passenger safety.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"25 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":"135963223","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}
Mikhail Kruglyakov, Elena Marshalko, Alexey Kuvshinov, Maxim Smirnov, Ari Viljanen
Abstract We propose a novel approach to model the ground electric field (GEF) induced by laterally‐nonuniform ionospheric sources in real time. The approach exploits the multi‐site transfer function concept, continuous magnetic field measurements at multiple sites in the region of interest, and spatial modes describing the ionospheric source. We compared the modeled GEFs with those measured at two locations in Fennoscandia and observed good agreement between modeled and measured GEF. Besides, we compared GEF‐based geomagnetically induced current (GIC) with that measured at the Mäntsälä natural gas pipeline recording point and again observed remarkable agreement between modeled and measured GIC.
{"title":"Multi‐Site Transfer Function Approach for Real‐Time Modeling of the Ground Electric Field Induced by Laterally‐Nonuniform Ionospheric Source","authors":"Mikhail Kruglyakov, Elena Marshalko, Alexey Kuvshinov, Maxim Smirnov, Ari Viljanen","doi":"10.1029/2023sw003621","DOIUrl":"https://doi.org/10.1029/2023sw003621","url":null,"abstract":"Abstract We propose a novel approach to model the ground electric field (GEF) induced by laterally‐nonuniform ionospheric sources in real time. The approach exploits the multi‐site transfer function concept, continuous magnetic field measurements at multiple sites in the region of interest, and spatial modes describing the ionospheric source. We compared the modeled GEFs with those measured at two locations in Fennoscandia and observed good agreement between modeled and measured GEF. Besides, we compared GEF‐based geomagnetically induced current (GIC) with that measured at the Mäntsälä natural gas pipeline recording point and again observed remarkable agreement between modeled and measured GIC.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":"33 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":"136009360","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}
Ya‐fei Shi, Cheng Yang, Jian Wang, Yu Zheng, Fan‐yi Meng, Leonid F. Chernogor
Abstract To achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory. The hybrid model decomposes the hmF2 time data into multiple subsequences through CEEMDAN and reconstructs the subsequences by sample entropy and correlation coefficient into high and low‐frequency sequences, which effectively shortens the calculation time of the model. Then, we determine the optimal hyperparameters of the LSTM models through ISOA, achieving high‐precision forecasting of the hmF2. In single‐step forecasting, the forecasting values of the hybrid model in diurnal and seasonal changes are highly consistent with the observation, which can better capture the severe changes in the hmF2. The model's RMSE, MAE, MAPE, and CC evaluation metrics are 15.86, 11.03 km, 4.76%, and 0.93 in the test set. Compared to IRI, GRU, and LSTM models, taking RMSE as an example, the forecasting accuracy of the models increased by 65.24%, 29.89%, and 29.60%, respectively. In multi‐step forecasting, the proposed model is better at forecasting the changing trend of hmF2, and the forecasting accuracies are significantly better than the IRI model. The data from multiple stations also verified the applicability of the proposed model for hmF2 forecasting. The above results indicate that the hybrid model has high accuracy in hmF2 short‐term forecasting and good applicability in multiple multi‐step forecasting, which can further improve the accurate forecasting of space weather.
{"title":"A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer","authors":"Ya‐fei Shi, Cheng Yang, Jian Wang, Yu Zheng, Fan‐yi Meng, Leonid F. Chernogor","doi":"10.1029/2023sw003581","DOIUrl":"https://doi.org/10.1029/2023sw003581","url":null,"abstract":"Abstract To achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory. The hybrid model decomposes the hmF2 time data into multiple subsequences through CEEMDAN and reconstructs the subsequences by sample entropy and correlation coefficient into high and low‐frequency sequences, which effectively shortens the calculation time of the model. Then, we determine the optimal hyperparameters of the LSTM models through ISOA, achieving high‐precision forecasting of the hmF2. In single‐step forecasting, the forecasting values of the hybrid model in diurnal and seasonal changes are highly consistent with the observation, which can better capture the severe changes in the hmF2. The model's RMSE, MAE, MAPE, and CC evaluation metrics are 15.86, 11.03 km, 4.76%, and 0.93 in the test set. Compared to IRI, GRU, and LSTM models, taking RMSE as an example, the forecasting accuracy of the models increased by 65.24%, 29.89%, and 29.60%, respectively. In multi‐step forecasting, the proposed model is better at forecasting the changing trend of hmF2, and the forecasting accuracies are significantly better than the IRI model. The data from multiple stations also verified the applicability of the proposed model for hmF2 forecasting. The above results indicate that the hybrid model has high accuracy in hmF2 short‐term forecasting and good applicability in multiple multi‐step forecasting, which can further improve the accurate forecasting of space weather.","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":"135849004","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}
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}
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