Space Weather-The International Journal of Research and Applications最新文献_第3页

A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer 基于深度学习的电离层F2层峰值高度混合预测模型

2区地球科学

Space Weather-The International Journal of Research and Applications Pub Date : 2023-10-01 DOI: 10.1029/2023sw003581

Ya‐fei Shi, Cheng Yang, Jian Wang, Yu Zheng, Fan‐yi Meng, Leonid F. Chernogor

{"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}

引用次数: 1

New Space Companies Meet a “Normal” Solar Maximum 新的太空公司遇到了一个“正常的”太阳极大期

2区地球科学

Space Weather-The International Journal of Research and Applications Pub Date : 2023-09-01 DOI: 10.1029/2023sw003702

Noé Lugaz, Huixin Liu, Brett A. Carter, Jennifer Gannon, Shasha Zou, Steven K. Morley

引用次数: 0

The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model 利用基于神经网络的模型研究电离层电流对外部驱动的响应

2区地球科学

Space Weather-The International Journal of Research and Applications Pub Date : 2023-09-01 DOI: 10.1029/2023sw003506

Xin Cao, Xiangning Chu, Jacob Bortnik, James M. Weygand, Jinxing Li, Homayon Aryan, Donglai Ma

{"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}

引用次数: 0

Comparison of Empirical and Theoretical Models of the Thermospheric Density Enhancement During the 3–4 February 2022 Geomagnetic Storm 2022年2月3-4日地磁风暴期间热层密度增强的经验和理论模型比较

2区地球科学

Space Weather-The International Journal of Research and Applications Pub Date : 2023-09-01 DOI: 10.1029/2023sw003521

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

{"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}

引用次数: 0

Three‐Dimensional Modeling of the Ground Electric Field in Fennoscandia During the Halloween Geomagnetic Storm 万圣节地磁风暴期间芬诺斯坎迪亚地电场的三维模拟

2区地球科学

Space Weather-The International Journal of Research and Applications Pub Date : 2023-09-01 DOI: 10.1029/2022sw003370

Elena Marshalko, Mikhail Kruglyakov, Alexey Kuvshinov, Ari Viljanen

{"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}

引用次数: 1

Numerical Modeling and GNSS Observations of Ionospheric Depletions Due To a Small‐Lift Launch Vehicle 小升力运载火箭造成电离层耗损的数值模拟和GNSS观测

2区地球科学

Space Weather-The International Journal of Research and Applications Pub Date : 2023-09-01 DOI: 10.1029/2023sw003563

G. W. Bowden, M. Brown

引用次数: 0

Probabilistic Solar Proxy Forecasting With Neural Network Ensembles 基于神经网络集成的概率太阳代理预报

2区地球科学

Space Weather-The International Journal of Research and Applications Pub Date : 2023-09-01 DOI: 10.1029/2023sw003675

Joshua D. Daniell, Piyush M. Mehta

{"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}

引用次数: 2

Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction 环境太阳风预测的贝叶斯推理和全局敏感性分析

2区地球科学

Space Weather-The International Journal of Research and Applications Pub Date : 2023-09-01 DOI: 10.1029/2023sw003555

Opal Issan, Pete Riley, Enrico Camporeale, Boris Kramer

{"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}

引用次数: 1

Prediction of Proton Pressure in the Outer Part of the Inner Magnetosphere Using Machine Learning 利用机器学习预测内磁层外层的质子压力

2区地球科学

Space Weather-The International Journal of Research and Applications Pub Date : 2023-09-01 DOI: 10.1029/2022sw003387

S. Y. Li, E. A. Kronberg, C. G. Mouikis, H. Luo, Y. S. Ge, A. M. Du

{"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}

引用次数: 0

New Index to Characterize Ionospheric Irregularity Distribution 表征电离层不规则分布的新指标

2区地球科学

Space Weather-The International Journal of Research and Applications Pub Date : 2023-09-01 DOI: 10.1029/2023sw003469

Endawoke Yizengaw

引用次数: 0