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.
{"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}
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.
{"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}
D. L. Hysell, A. Kirchman, B. J. Harding, R. A. Heelis, S. L. England, H. U. Frey, S. B. Mende
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.
{"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}
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-α 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.
{"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}
Ercha Aa, Shun-Rong Zhang, Shasha Zou, Wenbin Wang, Zihan Wang, Xuguang Cai, Philip J. Erickson, Anthea J. Coster
This paper investigates the midlatitude ionospheric disturbances over the American/Atlantic longitude sector during an intense geomagnetic storm on 23 April 2023. The study utilized a combination of ground-based observations (Global Navigation Satellite System total electron content and ionosonde) along with measurements from multiple satellite missions (GOLD, Swarm, Defense Meteorological Satellite Program, and TIMED/GUVI) to analyze storm-time electrodynamics and neutral dynamics. We found that the storm main phase was characterized by distinct midlatitude ionospheric density gradient structures as follows: (a) In the European-Atlantic longitude sector, a significant midlatitude bubble-like ionospheric super-depletion structure (BLISS) was observed after sunset. This BLISS appeared as a low-density channel extending poleward/westward and reached ∼40° geomagnetic latitude, corresponding to an APEX height of ∼5,000 km. (b) Coincident with the BLISS, a dynamic storm-enhanced density plume rapidly formed and decayed at local afternoon in the North American sector, with the plume intensity being doubled and halved in just a few hours. (c) The simultaneous occurrence of these strong yet opposite midlatitude gradient structures could be mainly attributed to common key drivers of prompt penetration electric fields and subauroral polarization stream electric fields. This shed light on the important role of storm-time electrodynamic processes in shaping global ionospheric disturbances.
{"title":"Significant Midlatitude Bubble-Like Ionospheric Super-Depletion Structure (BLISS) and Dynamic Variation of Storm-Enhanced Density Plume During the 23 April 2023 Geomagnetic Storm","authors":"Ercha Aa, Shun-Rong Zhang, Shasha Zou, Wenbin Wang, Zihan Wang, Xuguang Cai, Philip J. Erickson, Anthea J. Coster","doi":"10.1029/2023sw003704","DOIUrl":"https://doi.org/10.1029/2023sw003704","url":null,"abstract":"This paper investigates the midlatitude ionospheric disturbances over the American/Atlantic longitude sector during an intense geomagnetic storm on 23 April 2023. The study utilized a combination of ground-based observations (Global Navigation Satellite System total electron content and ionosonde) along with measurements from multiple satellite missions (GOLD, Swarm, Defense Meteorological Satellite Program, and TIMED/GUVI) to analyze storm-time electrodynamics and neutral dynamics. We found that the storm main phase was characterized by distinct midlatitude ionospheric density gradient structures as follows: (a) In the European-Atlantic longitude sector, a significant midlatitude bubble-like ionospheric super-depletion structure (BLISS) was observed after sunset. This BLISS appeared as a low-density channel extending poleward/westward and reached ∼40° geomagnetic latitude, corresponding to an APEX height of ∼5,000 km. (b) Coincident with the BLISS, a dynamic storm-enhanced density plume rapidly formed and decayed at local afternoon in the North American sector, with the plume intensity being doubled and halved in just a few hours. (c) The simultaneous occurrence of these strong yet opposite midlatitude gradient structures could be mainly attributed to common key drivers of prompt penetration electric fields and subauroral polarization stream electric fields. This shed light on the important role of storm-time electrodynamic processes in shaping global ionospheric disturbances.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"31 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072599","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}
We studied the response of ionospheric total electron content (TEC) and equatorial electrojet (EEJ) to the ultra-fast Kelvin wave (UFKW) at the equator in the mesosphere using zonal wind data obtained from TIMED Doppler Interferometer (TIDI), EEJ data over the monitoring station Jicamarca (12°S, 77°W) and global TEC maps. The least squares fitting method is utilized to perform a spectral analysis of zonal wind, EEJ and TEC. Our analysis results demonstrate that UFKW events can be divided into four categories: (a) UFKW events with both TEC and EEJ response; (b) UFKW events with TEC response but without EEJ response; (c) UFKW events with EEJ response but without TEC response; (d) UFKW events without neither TEC response nor EEJ response. The first type of UFKW events occur the most often and is generally thought to generate a response in EEJ at approximately 105–110 km through the dynamo effect. The polarization electric field associated with EEJ then produces a response in the ionospheric TEC through the fountain effect. The lack of EEJ response in the second type of UFKWs may be due to the influence of eastward background winds. We found that all UFKW events with EEJ response have a response in TEC. The fourth type of UFKWs have smaller amplitudes, shorter vertical wavelengths and longer periods, which make them more likely to dissipate and cannot propagate to higher altitudes. These UFKWs cannot propagate to the altitude of EEJ and produce a response in EEJ, much less in TEC.
{"title":"Different Response of the Ionospheric TEC and EEJ to Ultra-Fast Kelvin Waves in the Mesosphere and Lower Thermosphere","authors":"Ruidi Sun, Sheng-Yang Gu, Xiankang Dou, Yusong Qin, Yafei Wei","doi":"10.1029/2023sw003699","DOIUrl":"https://doi.org/10.1029/2023sw003699","url":null,"abstract":"We studied the response of ionospheric total electron content (TEC) and equatorial electrojet (EEJ) to the ultra-fast Kelvin wave (UFKW) at the equator in the mesosphere using zonal wind data obtained from TIMED Doppler Interferometer (TIDI), EEJ data over the monitoring station Jicamarca (12°S, 77°W) and global TEC maps. The least squares fitting method is utilized to perform a spectral analysis of zonal wind, EEJ and TEC. Our analysis results demonstrate that UFKW events can be divided into four categories: (a) UFKW events with both TEC and EEJ response; (b) UFKW events with TEC response but without EEJ response; (c) UFKW events with EEJ response but without TEC response; (d) UFKW events without neither TEC response nor EEJ response. The first type of UFKW events occur the most often and is generally thought to generate a response in EEJ at approximately 105–110 km through the dynamo effect. The polarization electric field associated with EEJ then produces a response in the ionospheric TEC through the fountain effect. The lack of EEJ response in the second type of UFKWs may be due to the influence of eastward background winds. We found that all UFKW events with EEJ response have a response in TEC. The fourth type of UFKWs have smaller amplitudes, shorter vertical wavelengths and longer periods, which make them more likely to dissipate and cannot propagate to higher altitudes. These UFKWs cannot propagate to the altitude of EEJ and produce a response in EEJ, much less in TEC.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"21 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072501","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}
Liangchao Li, Haijun Liu, Huijun Le, Jing Yuan, Haoran Wang, Yi Chen, Weifeng Shan, Li Ma, Chunjie Cui
In this paper, we propose a novel Total Electron Content (TEC) map prediction model, named ED-AttConvLSTM, using a Convolutional Long Short-Term Memory (ConvLSTM) network and attention mechanism based on encoder-decoder structure. The inclusion of the attention mechanism enhances the efficient utilization of spatiotemporal features extracted by the ConvLSTM, emphasizing the significance of crucial spatiotemporal features in the prediction process and, as a result, leading to an enhancement in predictive performance. We conducted experiments in East Asia (10°N–45°N, 90°E−130°E). The ED-AttConvLSTM was trained and evaluated using the International GNSS Service TEC maps over a period of six years from 2013 to 2015 (high solar activity years) and 2017 to 2019 (low solar activity years). We compared our ED-AttConvLSTM with IRI-2016, COPG, LSTM, GRU, ED-ConvLSTM and ED-ConvGRU. The results indicate that our model surpasses the comparison models in forecasting both high and low solar activity years, across most months and UT moments in a day. Moreover, our model exhibits notably superior prediction performance during the most severe phases of a magnetic storm when compared to the comparison models. Subsequently, we then also discuss how the prediction performance of our model is affected by latitude. Finally, we discuss the diminishing performance of our model in multi-day predictions, demonstrating that its reliability for forecasts ranging from one to 4 days in advance. Beyond the fifth day, there is a pronounced decline in the model's performance.
{"title":"ED-AttConvLSTM: An Ionospheric TEC Map Prediction Model Using Adaptive Weighted Spatiotemporal Features","authors":"Liangchao Li, Haijun Liu, Huijun Le, Jing Yuan, Haoran Wang, Yi Chen, Weifeng Shan, Li Ma, Chunjie Cui","doi":"10.1029/2023sw003740","DOIUrl":"https://doi.org/10.1029/2023sw003740","url":null,"abstract":"In this paper, we propose a novel Total Electron Content (TEC) map prediction model, named ED-AttConvLSTM, using a Convolutional Long Short-Term Memory (ConvLSTM) network and attention mechanism based on encoder-decoder structure. The inclusion of the attention mechanism enhances the efficient utilization of spatiotemporal features extracted by the ConvLSTM, emphasizing the significance of crucial spatiotemporal features in the prediction process and, as a result, leading to an enhancement in predictive performance. We conducted experiments in East Asia (10°N–45°N, 90°E−130°E). The ED-AttConvLSTM was trained and evaluated using the International GNSS Service TEC maps over a period of six years from 2013 to 2015 (high solar activity years) and 2017 to 2019 (low solar activity years). We compared our ED-AttConvLSTM with IRI-2016, COPG, LSTM, GRU, ED-ConvLSTM and ED-ConvGRU. The results indicate that our model surpasses the comparison models in forecasting both high and low solar activity years, across most months and UT moments in a day. Moreover, our model exhibits notably superior prediction performance during the most severe phases of a magnetic storm when compared to the comparison models. Subsequently, we then also discuss how the prediction performance of our model is affected by latitude. Finally, we discuss the diminishing performance of our model in multi-day predictions, demonstrating that its reliability for forecasts ranging from one to 4 days in advance. Beyond the fifth day, there is a pronounced decline in the model's performance.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"8 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140055059","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}
Data assimilation is one of the most important approaches to monitoring the variations of ionospheric electron densities. The construction of the background error covariance matrix is an important component of ionospheric data assimilations. To construct the background error covariance matrix, the information about the spatial ionospheric correlations is required. We present a statistical analysis on the ionospheric vertical error correlation length (VCL) based on a global network of ionosondes and the Neustrelitz Electron Density Model. We show that the locally derived VCL is well-defined and the VCL does not show a considerable dependency on the geographical seasons while local time dependencies of the VCL are shown to be present. A novel VCL model is also established based on the ionospheric scale heights. We show that the ionospheric VCL can be characterized by the variance ratio between the ionosphere model and ionospheric measurements. The altitudinal variations of VCLs are controlled by the interactions between the inherent VCLs of the ionosphere model and the measurements. Two experiments are conducted at two different latitudes based on the proposed model. The results show that the proposed model is stable and well-correlated with the observed VCLs, which implies a potential to be generalized for a global correlation model. The proposed model can be used in the temporal evolution of error covariance matrices in the ionospheric 4D-Variational (4D-Var) assimilations, which may overcome the main drawbacks of the static error covariance specifications in the ionospheric 4D-Var assimilations.
{"title":"Characterization of the Ionospheric Vertical Error Correlation Lengths Based on Global Ionosonde Observations","authors":"L. Yuan, Timothy Kodikara, M. M. Hoque","doi":"10.1029/2023sw003743","DOIUrl":"https://doi.org/10.1029/2023sw003743","url":null,"abstract":"Data assimilation is one of the most important approaches to monitoring the variations of ionospheric electron densities. The construction of the background error covariance matrix is an important component of ionospheric data assimilations. To construct the background error covariance matrix, the information about the spatial ionospheric correlations is required. We present a statistical analysis on the ionospheric vertical error correlation length (VCL) based on a global network of ionosondes and the Neustrelitz Electron Density Model. We show that the locally derived VCL is well-defined and the VCL does not show a considerable dependency on the geographical seasons while local time dependencies of the VCL are shown to be present. A novel VCL model is also established based on the ionospheric scale heights. We show that the ionospheric VCL can be characterized by the variance ratio between the ionosphere model and ionospheric measurements. The altitudinal variations of VCLs are controlled by the interactions between the inherent VCLs of the ionosphere model and the measurements. Two experiments are conducted at two different latitudes based on the proposed model. The results show that the proposed model is stable and well-correlated with the observed VCLs, which implies a potential to be generalized for a global correlation model. The proposed model can be used in the temporal evolution of error covariance matrices in the ionospheric 4D-Variational (4D-Var) assimilations, which may overcome the main drawbacks of the static error covariance specifications in the ionospheric 4D-Var assimilations.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"32 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140045237","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}
Space weather indices are used to drive forecasts of thermosphere density, which directly affects objects in low-Earth orbit (LEO) through atmospheric drag force. A set of proxies and indices (drivers), F10.7, S10.7, M10.7, and Y10.7 are used as inputs by the JB2008, (https://doi.org/10.2514/6.2008-6438) thermosphere density model. The United States Air Force (USAF) operational High Accuracy Satellite Drag Model (HASDM), relies on JB2008, (https://doi.org/10.2514/6.2008-6438), and forecasts of solar drivers from a linear algorithm. We introduce methods using long short-term memory (LSTM) model ensembles to improve over the current prediction method as well as a previous univariate approach. We investigate the usage of principal component analysis (PCA) to enhance multivariate forecasting. A novel method, referred to as striped sampling, is created to produce statistically consistent machine learning data sets. We also investigate forecasting performance and uncertainty estimation by varying the training loss function and by investigating novel weighting methods. Results show that stacked neural network model ensembles make multivariate driver forecasts which outperform the operational linear method. When using MV-MLE (multivariate multi-lookback ensemble), we see an improvement of RMSE for F10.7, S10.7, M10.7, and Y10.7 of 17.7%, 12.3%, 13.8%, 13.7% respectively, over the operational method. We provide the first probabilistic forecasting method for S10.7, M10.7, and Y10.7. Ensemble approaches are leveraged to provide a distribution of predicted values, allowing an investigation into robustness and reliability (R&R) of uncertainty estimates. Uncertainty was also investigated through the use of calibration error score (CES), with the MV-MLE providing an average CES of 5.63%, across all drivers.
{"title":"Probabilistic Short-Term Solar Driver Forecasting With Neural Network Ensembles","authors":"Joshua D. Daniell, Piyush M. Mehta","doi":"10.1029/2023sw003785","DOIUrl":"https://doi.org/10.1029/2023sw003785","url":null,"abstract":"Space weather indices are used to drive forecasts of thermosphere density, which directly affects objects in low-Earth orbit (LEO) through atmospheric drag force. A set of proxies and indices (drivers), <i>F</i><sub>10.7</sub>, <i>S</i><sub>10.7</sub>, <i>M</i><sub>10.7</sub>, and <i>Y</i><sub>10.7</sub> are used as inputs by the JB2008, (https://doi.org/10.2514/6.2008-6438) thermosphere density model. The United States Air Force (USAF) operational High Accuracy Satellite Drag Model (HASDM), relies on JB2008, (https://doi.org/10.2514/6.2008-6438), and forecasts of solar drivers from a linear algorithm. We introduce methods using long short-term memory (LSTM) model ensembles to improve over the current prediction method as well as a previous univariate approach. We investigate the usage of principal component analysis (PCA) to enhance multivariate forecasting. A novel method, referred to as striped sampling, is created to produce statistically consistent machine learning data sets. We also investigate forecasting performance and uncertainty estimation by varying the training loss function and by investigating novel weighting methods. Results show that stacked neural network model ensembles make multivariate driver forecasts which outperform the operational linear method. When using MV-MLE (multivariate multi-lookback ensemble), we see an improvement of RMSE for <i>F</i><sub>10.7</sub>, <i>S</i><sub>10.7</sub>, <i>M</i><sub>10.7</sub>, and <i>Y</i><sub>10.7</sub> of 17.7%, 12.3%, 13.8%, 13.7% respectively, over the operational method. We provide the first probabilistic forecasting method for <i>S</i><sub>10.7</sub>, <i>M</i><sub>10.7</sub>, and <i>Y</i><sub>10.7</sub>. Ensemble approaches are leveraged to provide a distribution of predicted values, allowing an investigation into robustness and reliability (R&R) of uncertainty estimates. Uncertainty was also investigated through the use of calibration error score (CES), with the MV-MLE providing an average CES of 5.63%, across all drivers.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"12 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140055058","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}
Very-low-frequency (VLF) signals emitted from ground-based transmitters for submarine communication can penetrate the ionosphere and leak into the magnetosphere, leading to electron precipitation via wave-particle interaction and thereby providing a potential means for radiation belt remediation. In this study, we systematically analyze the dependence of quasi-trapped electron fluxes scattered by signals from the North West Cape (NWC) transmitter on electron energy, L-shell, and geomagnetic activity (i.e., the Dst index) using long-term measurements from the DEMETER satellite. Considering potentially changed theoretical cyclotron resonant condition, we find that the variations of wave normal angle (WNA) of NWC transmitter signals or of the background electron density can explain the variated “wisp” positions in energy versus L plane. The long-term data analyzation suggests that the energy-dependences increases can help to distinguish the different source mechanisms of quasi-trapped electrons. The enhancement of quasi-trapped electron fluxes induced by NWC transmitter signals is more obvious at L = 1.8 than L = 1.6 due to higher trapped flux levels and strong pitch angle diffusion induced by transmitter signals.
用于海底通信的地面发射机发射的甚低频（VLF）信号可以穿透电离层并泄漏到磁层中，通过波粒相互作用导致电子沉淀，从而为辐射带修复提供一种潜在的手段。在这项研究中，我们利用 DEMETER 卫星的长期测量数据，系统分析了西北角（NWC）发射机信号散射的准俘获电子通量与电子能量、L 壳和地磁活动（即 Dst 指数）的关系。考虑到理论上回旋共振条件的潜在变化，我们发现 NWC 发射机信号的波法线角（WNA）或背景电子密度的变化可以解释能量与 L 平面上 "缕 "的位置变化。长期数据分析表明，能量依赖性的增加有助于区分准俘获电子的不同来源机制。在 L = 1.8 时，NWC 发射信号诱导的准俘获电子通量的增强比 L = 1.6 时更为明显，这是因为发射信号诱导了更高的俘获通量水平和更强的俯仰角扩散。
{"title":"Long-Term Variations of Energetic Electrons Scattered by Signals From the North West Cape Transmitter","authors":"Jingle Hu, Zheng Xiang, Xin Ma, Yangxizi Liu, Junhu Dong, Deyu Guo, Binbin Ni","doi":"10.1029/2023sw003827","DOIUrl":"https://doi.org/10.1029/2023sw003827","url":null,"abstract":"Very-low-frequency (VLF) signals emitted from ground-based transmitters for submarine communication can penetrate the ionosphere and leak into the magnetosphere, leading to electron precipitation via wave-particle interaction and thereby providing a potential means for radiation belt remediation. In this study, we systematically analyze the dependence of quasi-trapped electron fluxes scattered by signals from the North West Cape (NWC) transmitter on electron energy, L-shell, and geomagnetic activity (i.e., the Dst index) using long-term measurements from the DEMETER satellite. Considering potentially changed theoretical cyclotron resonant condition, we find that the variations of wave normal angle (WNA) of NWC transmitter signals or of the background electron density can explain the variated “wisp” positions in energy versus <i>L</i> plane. The long-term data analyzation suggests that the energy-dependences increases can help to distinguish the different source mechanisms of quasi-trapped electrons. The enhancement of quasi-trapped electron fluxes induced by NWC transmitter signals is more obvious at <i>L</i> = 1.8 than <i>L</i> = 1.6 due to higher trapped flux levels and strong pitch angle diffusion induced by transmitter signals.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":"15 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140026193","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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