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Showing papers in "Space Weather-the International Journal of Research and Applications in 2022"


Journal ArticleDOI
TL;DR: In this article , a prediction model of global IGS-TEC maps is established based on testing several different LSTM network (LSTM)-based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time.
Abstract: The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS-TEC maps are established based on testing several different long short-term memory (LSTM) network (LSTM)-based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. We find that a Multi-step auxiliary algorithm based prediction model performs best. It can effectively predict the global ionospheric IGS-TEC in the next 6 days (the mean absolute deviation (MAD) and root mean square error (RMSE) are 2.485 and 3.511 TECU, respectively) compared to the IRI (the MAD and RMSE are 4.248 and 5.593 TECU). The analyses of four geomagnetic storm events are completely separate from the time range of the training set, so as to further validate the performance of the model. The International Reference Ionosphere model is used as a reference for the performance of our predictive model, and a rotated persistence is estimated by time-shift algorithm of IGS-TEC. The result suggests that the Multi-step auxiliary prediction model has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm and quiet time.

16 citations


Journal ArticleDOI
TL;DR: In this paper , the authors analyzed the ionospheric response to the 25-26 August 2018 geomagnetic storm and revealed formation of intense ionosphere plasma irregularities over broad latitudinal ranges from equatorial toward middle latitudes in the American and Pacific sectors.
Abstract: Can geomagnetic storms during low solar activity trigger formation of extreme equatorial plasma bubbles (EPBs) that affect low and midlatitudes? We analyzed the ionospheric response to the 25–26 August 2018 geomagnetic storm and revealed formation of intense ionospheric plasma irregularities over broad latitudinal ranges from equatorial toward middle latitudes in the American and Pacific sectors. Storm-induced penetration electric fields created favorable conditions for strong fountain effect uplifted the equatorial ionosphere, enhancement of the equatorial ionization anomaly (EIA), and postsunset EPBs generation. We found two patterns of equatorial ionospheric irregularities expansion toward low and middle latitudes: (a) storm-induced EPBs developed over a large latitudinal extent between widely spread EIA crests, (b) narrow channel of the ionospheric irregularities stretched away from the EPB location toward the auroral zone in the northwestward direction. The EPBs latitudinal extent largely exceeded climatological-based expectations for solar minimum conditions; EPBs reached atypically high latitudes (20°–25° magnetic latitude [MLAT]) in the Pacific Ocean sector. The poleward-streaming plasma density depletions were registered along the western coast of North America. The ionospheric irregularities transported in the northwestward direction toward midlatitudes reaching as high as 40°–45° MLAT. The passage of these ionospheric irregularities coincided with the spread-F conditions recorded at a midlatitude ionosonde (42° MLAT)—rather atypical phenomenon for midlatitudes. It is suggested that enhanced westward drifts associated with prompt penetration and Sub Auroral Polarization Stream electric fields can support the northwestward plasma transportation.

13 citations


Journal ArticleDOI
TL;DR: In this article , the authors examined the space weather conditions related to the satellite loss, based on observations, forecasts, and numerical simulations from the National Oceanic and Atmospheric Administration Space Weather Prediction Center (SWPC).
Abstract: On 3 February 2022, SpaceX Starlink launched and subsequently lost 38 of 49 satellites due to enhanced neutral density associated with a geomagnetic storm. This study examines the space weather conditions related to the satellite loss, based on observations, forecasts, and numerical simulations from the National Oceanic and Atmospheric Administration Space Weather Prediction Center (SWPC). Working closely with the Starlink team, the thermospheric densities along the satellite orbits were estimated and the neutral density increase leading to the satellite loss was investigated. Simulation results suggest that during the geomagnetic storm, pre-launch Monte Carlo analyses performed by the Starlink team using empirical neutral density inputs from NRLMSISE-00 tended to underestimate the impact relative to predictions from the operational coupled Whole Atmosphere Model and Ionosphere Plasmasphere Electrodynamics physics-based model. The numerical simulation indicated this minor to moderate geomagnetic storm was sufficient to create 50%–125% density enhancement at altitudes ranging between 200 and 400 km. With the increasing solar activity of Solar Cycle 25, satellites in low-Earth orbit are expected to experience an increasing number of thermospheric expansion events. Currently, no alerts and warnings issued by SWPC are focused on satellite users concerned with atmospheric drag and related applications. Thus, during geomagnetic storms, it is crucial to establish suitable alerts and warnings based on neutral density predictions to provide users guidance for preventing satellite losses due to drag and to aid in collision avoidance calculations.

12 citations


Journal ArticleDOI
Wanke Liu1
TL;DR: In this article , an ionospheric long short-term memory network (Ion-LSTM) with multiple input parameters was developed to predict the global ionosphere total electron content (TEC).
Abstract: The application of deep learning technology to ionospheric prediction has become a new research hotspot. However, there are still some gaps, such as the prediction effect with different input solar and geomagnetic activity parameters, and the forecast accuracy with different prediction methods as well as the validation of long period data results, to be filled. We developed an ionospheric long short-term memory network (Ion-LSTM) with multiple input parameters to predict the global ionospheric total electron content (TEC). Two solutions with different ionospheric data based on Ion-LSTM were assessed, namely spherical harmonic coefficients (SHC) and vertical TEC (VTEC) prediction solution. The results show two solutions, both perform well in accuracy and stability. The input of the geomagnetic activity index improves the prediction effect of the model in the storm period. For the 1- and 2-day-predicted global ionospheric maps (GIMs) from 2015 to 2020, the root mean square error (RMSE) of SHC prediction solution is 1.69 TECU and 1.84 TECU while that of the VTEC prediction solution is 1.70 TECU and 1.84 TECU, respectively. Over 70% of the absolute residuals are within 3 TECU in high solar activity and over 96% in low solar activity. Further, by comparing the predicted results between Ion-LSTM and conventional methods (e.g., Center for Orbit Determination in Europe (CODE) predicted GIMs), the evaluation results show that the RMSE of Ion-LSTM is 0.7 TECU lower than that of CODE predicted GIMs under different solar and geomagnetic activities. Additionally, the accuracy of the Ion-LSTM prediction results decreases slightly as the input time span increases.

12 citations


Journal ArticleDOI
TL;DR: In this paper , the authors reveal the space weather process during 3-4 February 2022 geomagnetic disturbances, from the Sun all the way to the satellite orbiting atmosphere, which brought significant financial, aerospace and public influences.
Abstract: On 4 February 2022, 38 Starlink satellites were destroyed by the geomagnetic storm, which brought significant financial, aerospace and public influences. In this letter, we reveal the space weather process during 3-4 February 2022 geomagnetic disturbances, from the Sun all the way to the satellite orbiting atmosphere. Initiated by an M1.0 class flare and the following coronal mass ejection (CME), a moderate geomagnetic storm was stimulated on February 3rd by the CME arrival at Earth. Subsequently, another moderate storm was triggered on February 4th by the passage of another CME. Model simulations driven by solar wind show that the first geomagnetic storm induced around 20% atmospheric density perturbations at 210 km altitude on February 3rd. The unexpected subsequent storm on February 4th led to a density enhancement of around 20%-30% at around 210 km. The resulting atmospheric drag can be even larger, since the regional density enhancement was over 60% and the satellite orbits were continuously decaying. This event brings forth the urgent requirements of better understanding and accurate prediction of the space weather as well as collaborations between industry and space weather community.

12 citations


Journal ArticleDOI
TL;DR: In this article , the authors report on a number of studies carried out to establish some key points and the best metric used to evaluate their performance and how it depends on the application for which the coupling function is intended.
Abstract: Solar wind-magnetosphere coupling functions have now been in use for almost 50 years. In that time, a very large number of formulations have been proposed. As they become increasingly subsumed into systems analysis and machine-learning studies of the magnetosphere, it is timely to establish best practice in their derivation and study their limitations. This paper reports on a number of studies carried out to establish some key points. Particular attention is paid to the best metric used to evaluate their performance and how it depends on the application for which the coupling function is intended.

10 citations


Journal ArticleDOI
TL;DR: In this paper , a detailed study on the local time, solar cycle, and geomagnetic dependencies of the ILG10 events are presented on local time and solar cycle dependencies.
Abstract: Geomagnetically induced current (GIC) measurements at the Mäntsälä, Finland (57.9° magnetic latitude) gas pipeline from 1999 through 2019 are analyzed. It is found that the GIC events with peak intensity A are not individual peaks, but occur in clusters with duration from ∼ 5 to ∼ 38 hours when GIC values are almost continuously above ∼ 1.5 A. The intense, long-duration GIC A clusters (ILG10) are characterized by average (median) duration of ∼ 17 ± 9 hours ( ∼ 14 hours), peak intensity of ∼ 21 ± 10 A ( ∼ 19 A), and time-integrated current flows of ∼ 1.0 ± 0.7 A-d ( ∼ 0.9 A-d) for all events under study. An one-to-one correlation is observed between the ILG10 events and intense substorm clusters characterized by average (median) duration of ∼ 20 ± 10 hours ( ∼ 17 hours), peak westward auroral electrojet intensity (presented by SuperMAG AL or SML index) of ∼ − 2238 ± 843 nT ( ∼ − 2099 nT) for all events. About 10 to 60 minutes fluctuations in the ILG10 events are found to be induced by substorm (SML) activity, and geomagnetic pulsations. A detailed study is presented on the local time, solar cycle, and geomagnetic dependencies of the ILG10 events. This will hopefully augment the predictability of the intense GICs.

9 citations


Journal ArticleDOI
TL;DR: In this paper , a new ionospheric total electron content (TEC) model over China was developed using the bidirectional long short-term memory (bi-LSTM) method and observations from 257 ground-based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021.
Abstract: The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short-term memory (bi-LSTM) method and observations from 257 ground-based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi-LSTM-based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI-2016, artificial neural network and LSTM-based models, correspondingly. The bi-LSTM-based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi-LSTM-based model. Moreover, the TEC maps resulting from the bi-LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

9 citations


Journal ArticleDOI
TL;DR: In this article , a new TEC-based ionospheric data assimilation system (TIDAS) over the continental US and adjacent area (20°-60°N, 60°-130°W, and 100-600 km) has been developed through assimilating heterogeneous ionosphere data, including dense ground-based Global Navigation Satellite System (GNSS) Total Electron Content (TEC) from 2,000+ receivers, Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation data, JASON satellite altimeter TEC, and Millstone Hill incoherent scatter radar measurements.
Abstract: A new TEC-based ionospheric data assimilation system (TIDAS) over the continental US and adjacent area (20°–60°N, 60°–130°W, and 100–600 km) has been developed through assimilating heterogeneous ionospheric data, including dense ground-based Global Navigation Satellite System (GNSS) Total Electron Content (TEC) from 2,000+ receivers, Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation data, JASON satellite altimeter TEC, and Millstone Hill incoherent scatter radar measurements. A hybrid Ensemble-Variational scheme is utilized to reconstruct the regional 3-D electron density distribution: a more realistic and location-dependent background error covariance matrix is calculated from an ensemble of corrected NeQuick outputs, and a three-dimensional variational (3DVAR) method is adopted for measurement updates to obtain an optimal state estimation. The spatial-temporal resolution of the reanalyzed 3-D electron density product is as high as 1° × 1° in latitude and longitude, 20 km in altitude, and 5 min in universal time, which is sufficient to reproduce ionospheric fine structure and storm-time disturbances. The accuracy and reliability of data assimilation results are validated using ionosonde and other measurements. TIDAS reanalyzed electron density is able to successfully reconstruct the 3-D morphology and dynamic evolution of the storm-enhanced density (SED) plume observed during the St. Patrick's day geomagnetic storm on 17 March 2013 with high fidelity. Using TIDAS, we found that the 3-D SED plume manifests as a ridge-like high-density channel that predominantly occurred between 300 and 500 km during 19:00–21:00 UT for this event, with the F2 region peak height being raised by 40–60 km and peak density enhancement of 30%–50%.

8 citations


Journal ArticleDOI
TL;DR: In this paper , the influence of the magnetic field map input (synoptic/diachronic vs. synchronic magnetic maps) on the global modeling of the solar wind and the CME-driven shock in the 11 April 2013 solar energetic particle event was quantitatively assessed.
Abstract: In the past decade, significant efforts have been made in developing physics-based solar wind and coronal mass ejection (CME) models, which have been or are being transferred to national centers (e.g., SWPC, Community Coordinated Modeling Center) to enable space weather predictive capability. However, the input data coverage for space weather forecasting is extremely limited. One major limitation is the solar magnetic field measurements, which are used to specify the inner boundary conditions of the global magnetohydrodynamic (MHD) models. In this study, using the Alfvén wave solar model, we quantitatively assess the influence of the magnetic field map input (synoptic/diachronic vs. synchronic magnetic maps) on the global modeling of the solar wind and the CME-driven shock in the 11 April 2013 solar energetic particle event. Our study shows that due to the inhomogeneous background solar wind and dynamical evolution of the CME, the CME-driven shock parameters change significantly both spatially and temporally as the CME propagates through the heliosphere. The input magnetic map has a great impact on the shock connectivity and shock properties in the global MHD simulation. Therefore this study illustrates the importance of taking into account the model uncertainty due to the imperfect magnetic field measurements when using the model to provide space weather predictions.

8 citations


Journal ArticleDOI
TL;DR: In this paper , the authors used principal component analysis (PCA) for dimensionality reduction, which creates the coefficients upon which nonlinear machine-learned (ML) regression models are trained.
Abstract: A thermospheric neutral mass density model with robust and reliable uncertainty estimates is developed based on the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database. This database, created by SET, contains 20 years of outputs from the U.S. Space Force's HASDM, which currently represents the state of the art for density and drag modeling. We utilize principal component analysis for dimensionality reduction, which creates the coefficients upon which nonlinear machine-learned (ML) regression models are trained. These models use three unique loss functions: Mean square error (MSE), negative logarithm of predictive density (NLPD), and continuous ranked probability score. Three input sets are also tested, showing improved performance when introducing time histories for geomagnetic indices. These models leverage Monte Carlo dropout to provide uncertainty estimates, and the use of the NLPD loss function results in well-calibrated uncertainty estimates while only increasing error by 0.25% (<10% mean absolute error) relative to MSE. By comparing the best HASDM-ML model to the HASDM database along satellite orbits, we found that the model provides robust and reliable density uncertainties over diverse space weather conditions. A storm-time comparison shows that HASDM-ML also supplies meaningful uncertainty estimates during extreme geomagnetic events.

Journal ArticleDOI
TL;DR: In this article , the phase scintillation index was extracted from each GNSS carrier with 1s-sampling-interval, mainly based on the cycle slip detection, the geodetic detrending and the wavelet transform, in which the optimal symmetry parameter and the timebandwidth product were determined with trial calculation.
Abstract: The adverse effect of the ionospheric scintillation on Global Navigation Satellite System (GNSS) requires scintillation monitoring on a global scale. Ionospheric Scintillation Monitoring Receivers (ISMR) are usually adopted to monitor scintillation, while they are not suitable for global monitoring due to the 50 Hz data collecting rate, which restricts the distribution. This paper proposes a new method to extract the phase scintillation index from each GNSS carrier with 1s-sampling-interval, mainly based on the cycle slip detection, the geodetic detrending and the wavelet transform, in which the optimal symmetry parameter and the time-bandwidth product are determined with trial calculation. Taken the index provided by ISMR as the reference, 1-year observations are utilized to evaluate the scintillation monitoring performance of the extracted index regarding the correlation of the magnitude in each observation arc, the detected daily scintillation occurrence rate, the diurnal variation pattern of the ionospheric scintillation, the correlation between the scintillation occurrence rate and the space weather parameter, and the complementary cumulative distribution of the magnitudes. Compared to the performance of Rate of Total electron content Index, a higher consistency can be achieved between the extracted index and the index, indicating the rationality of applying the proposed method in monitoring scintillations. The extracted scintillation index can be expected to introduce geodetic receivers operating at 1s-sampling-interval into the field of ionospheric scintillation monitoring on a global scale.

Journal ArticleDOI
TL;DR: In this article , an encoder-decoder structure with a convolution long short-term memory (ED-ConvLSTM) network was proposed to forecast global total electron content (TEC) based on the International GNSS Service (IGS) TEC maps from 2005 to 2018 with 1-hr time cadence.
Abstract: In this paper, we proposed an innovative encoder-decoder structure with a convolution long short-term memory (ED-ConvLSTM) network to forecast global total electron content (TEC) based on the International GNSS Service (IGS) TEC maps from 2005 to 2018 with 1-hr time cadence. The ED-ConvLSTM model is used to forecast TEC maps 1–7 days in advance through iterations. To investigate the model's performance, we compared the model with International Reference Ionosphere (IRI2016) model in 2014 and 2018, and compared the model with 1-day Beijing University of Aeronautics and Astronautics (BUAA) model in 2018. The results show that our 7-day ED-ConvLSTM model (ED-ConvLSTM model that forecasts 7 days in advance) outperforms IRI2016 in 2014 and 2018, and our 5-day ED-ConvLSTM model (ED-ConvLSTM model that forecasts 5 days in advance) outperforms 1-day BUAA model. Furthermore, the root mean square error (RMSE) from the 1-day ED-ConvLSTM model with respect to the IGS TEC maps decreases by 51.5% and 43%, respectively, in 2014 and 2018 compared with that from IRI2016 model. The RMSE from the 1-day ED-ConvLSTM model is 20.3% lower than that from the 1-day BUAA model in 2018. In addition, our model has the highest RMSE in the Equatorial Ionospheric Anomaly (EIA) region, but can roughly predict the features and locations of EIA. However, the model fails to forecast localized TEC enhancement and the sudden ionospheric response to the geomagnetic storms. Overall, the model shows competitive performance in medium-term global TEC maps prediction during geomagnetic quiet periods.

Journal ArticleDOI
TL;DR: In this paper , a neural network model was proposed to reproduce the dynamics of electron fluxes in the range of 50 keV to 1 MeV in the outer radiation belt, using only solar wind conditions and geomagnetic indices as input.
Abstract: We present a set of neural network models that reproduce the dynamics of electron fluxes in the range of 50 keV $\sim$ 1 MeV in the outer radiation belt. The Outer Radiation belt Electron Neural net model for Medium energy electrons(ORIENT-M) uses only solar wind conditions and geomagnetic indices as input. The models are trained on electron flux data from the Magnetic Electron Ion Spectrometer (MagEIS) instrument onboard Van Allen Probes, and they can reproduce the dynamic variations of electron fluxes in different energy channels. The model results show high coefficient of determination $R^2 \sim $ 0.78-0.92 on the test dataset, an out-of-sample 30-day period from February 25 to March 25 in 2017, when a geomagnetic storm took place, as well as an out-of-sample one year period after March 2018. In addition, the models are able to capture electron dynamics such as intensifications, decays, dropouts, and the Magnetic Local Time (MLT) dependence of the lower energy ($\sim <$ 100 keV )electron fluxes during storms. The models have reliable prediction capability and can be used for a wide range of space weather applications. The general framework of building our model is not limited to radiation belt fluxes and could be used to build machine learning models for a variety of other plasma parameters in the Earth's magnetosphere.

Journal ArticleDOI
TL;DR: In this article , the convLSTM-based models were used to forecast global ionospheric total electron content (TEC) maps with up to 24 hours of lead time at a 1-hour interval.
Abstract: This paper applies the convolutional long short-term memory (convLSTM)-based machine learning (ML) models to forecast global ionospheric total electron content (TEC) maps with up to 24 hours of lead time at a 1-hour interval. Four convLSTM-based models were investigated, and the one that implements the L1 loss function and the residual prediction strategy demonstrates the best performance. The convLSTM models are trained and evaluated using Centre for Orbit Determination in Europe (CODE) global TEC maps over a period of nearly seven years from October 19, 2014 to July 21, 2021. Results show that the best convLSTM model outperforms the 1-day predicted global TEC products released by CODE analysis center (c1pg) and persistence models under various levels of solar and geomagnetic activities, except for a lead time beyond 8 hours during the storm time where the c1pg has slightly better performance. The convLSTM forecasting performance degrades as the lead time increases.

Journal ArticleDOI
TL;DR: In this paper , the authors use machine learning methods to predict whether an active region (AR) which produces flares will lead to a solar energetic particle (SEP) event using Space-Weather Michelson Doppler Imager (MDI) Active Region Patches (SMARPs).
Abstract: We use machine learning methods to predict whether an active region (AR) which produces flares will lead to a solar energetic particle (SEP) event using Space-Weather Michelson Doppler Imager (MDI) Active Region Patches (SMARPs). This new data product is derived from maps of the solar surface magnetic field taken by the MDI aboard the Solar and Heliospheric Observatory. We survey the SMARP active regions associated with flares that appear on the solar disk between 5 June 1996 and 14 August 2010, label those that produced SEPs as positive and the rest as negative. The AR SMARP features that correspond to each flare are used to train two different types of machine learning methods, the support vector machines (SVMs) and the regression models. The results show that the SMARP data can predict whether a flare will lead to an SEP with accuracy (ACC) ≤0.72 ± 0.12 while allowing for a competitive leading time of 55.3 ± 28.6 min for forecasting the SEP events.

Journal ArticleDOI
TL;DR: The Open Solar Physics Rapid Ensemble Information (OSPREI) as discussed by the authors is a tool that describes Sun-to-Earth CME behavior, including the location, orientation, size, shape, speed, arrival time, and internal thermal and magnetic properties, on the timescale needed for forecasts.
Abstract: Coronal Mass Ejections (CMEs) drive space weather activity at Earth and throughout the solar system. Current CME-related space weather predictions rely on information reconstructed from coronagraphs, sometimes from only a single viewpoint, to drive a simple interplanetary propagation model, which only gives the arrival time or limited additional information. We present the coupling of three established models into OSPREI (Open Solar Physics Rapid Ensemble Information), a new tool that describes Sun-to-Earth CME behavior, including the location, orientation, size, shape, speed, arrival time, and internal thermal and magnetic properties, on the timescale needed for forecasts. First, ForeCAT describes the trajectory that a CME takes through the solar corona. Second, ANTEATR simulates the propagation, including expansion and deformation, of a CME in interplanetary space and determines the evolution of internal properties via conservation laws. Finally, FIDO produces in situ profiles for a CME's interaction with a synthetic spacecraft. OSPREI includes ensemble modeling by varying each input parameter to probe any uncertainty in their values, yielding probabilities for all outputs. Standardized visualizations are automatically generated, providing easily-accessible, essential information for space weather forecasting. We show OSPREI results for CMEs observed in the corona on 2021 April 22 and 2021 May 09. We approach these CME as a forecasting proof-of-concept, using information analogous to what would be available in real time rather than fine-tuning input parameters to achieve a best fit for a detailed scientific study. The OSPREI prediction shows good agreement with the arrival time and in situ properties.

Journal ArticleDOI
TL;DR: In this article , the authors use a large number of Cone CME simulations with the HUXt solar wind model to quantify the scale of uncertainty introduced into geometric modeling and the ELEvoHI CME arrival times by solar wind structure.
Abstract: Geometric modeling of Coronal Mass Ejections (CMEs) is a widely used tool for assessing their kinematic evolution. Furthermore, techniques based on geometric modeling, such as ELEvoHI, are being developed into forecast tools for space weather prediction. These models assume that solar wind structure does not affect the evolution of the CME, which is an unquantified source of uncertainty. We use a large number of Cone CME simulations with the HUXt solar wind model to quantify the scale of uncertainty introduced into geometric modeling and the ELEvoHI CME arrival times by solar wind structure. We produce a database of simulations, representing an average, a fast, and an extreme CME scenario, each independently propagating through 100 different ambient solar wind environments. Synthetic heliospheric imager observations of these simulations are then used with a range of geometric models to estimate the CME kinematics. The errors of geometric modeling depend on the location of the observer, but do not seem to depend on the CME scenario. In general, geometric models are biased towards predicting CME apex distances that are larger than the true value. For these CME scenarios, geometric modeling errors are minimised for an observer in the L5 region. Furthermore, geometric modeling errors increase with the level of solar wind structure in the path of the CME. The ELEvoHI arrival time errors are minimised for an observer in the L5 region, with mean absolute arrival time errors of 8.2 ± 1.2 h, 8.3 ± 1.0 h, and 5.8 ± 0.9 h for the average, fast, and extreme CME scenarios.

Journal ArticleDOI
TL;DR: In this paper , the authors present an analysis of five years of continuous GIC measurements in transformer neutral points in Austria, and evaluate two geomagnetic storms from September 2017 and May 2021 to discuss the effects of GIC on the power transmission grid and its assets.
Abstract: Geomagnetically induced currents (GICs), a result of solar wind interaction with the Earth's magnetic field and the resistive ground, are known to flow in power transmission grids, where they can lead to transformer damage and grid operation problems. In this study we present an analysis of five years of continuous GIC measurements in transformer neutral points in Austria. Seven self-designed stand-alone measurement systems are currently installed in the Austrian 220 and 380 kV transmission levels, measuring currents up to 25 A. We identify recurrent geomagnetic activity in the measurements, and also find man-made sources of low frequency currents using frequency analysis. In order to support the transmission grid operators, two GIC simulation approaches are used to simulate GICs in the power grid. The first model uses measurements to derive the sensitivity of the location to northward and eastward geoelectric field components (which requires no detailed grid data), and the second model uses the detailed grid model to compute GICs from a geoelectric field. We evaluate two geomagnetic storms from September 2017 and May 2021 to discuss the effects of GICs on the power transmission grid and its assets.

Journal ArticleDOI
TL;DR: In this paper , the authors proposed a geometry-free cycle slip threshold model based on ionospheric disturbance index rate of total electron content index to reduce the false detection rate of cycle slip in GNSS precise point positioning during strong storm periods, thus improving the accuracy and reliability of GNSS PPP.
Abstract: Geomagnetic storm can affect the performance of Global Navigation Satellite System (GNSS) precise positioning services. To mitigate the adverse effects of strong geomagnetic storms, we propose to establish the geometry-free (GF) cycle slip threshold model based on ionospheric disturbance index rate of total electron content index to reduce the false detection rate of cycle slip in GNSS precise point positioning (PPP) during strong storm periods, thus improving the accuracy and reliability of GNSS PPP. The performance of our proposed model is validated by using 171 International GNSS Service (IGS) tracking stations data on 8 September 2017. The analysis indicates that compared with conventional PPP scheme, the proposed model can improve the positioning accuracy by approximately 14.0% (36.8%) and 23.1% (51.5%) in the horizonal and vertical components for global (high latitudes) stations. Furthermore, the availability of our proposed model is also validated by PPP experiments using 379 IGS tracking stations data during another strong storm occurred on 26 August 2018.

Journal ArticleDOI
TL;DR: In this article , an empirical model of the Es layer using the multivariable nonlinear least-squares-fitting method, based on the S4max from Constellation Observing System for Meteorology, Ionosphere, and Climate satellite radio occultation measurements in the period 2006-2014, was constructed.
Abstract: The intense plasma irregularities within the ionospheric sporadic E (Es) layers at 90–130 km altitude have a significant impact on radio communications and navigation systems. As a result, the modeling of the Es layer is very important for the accuracy, reliability, and further applications of modern real-time global navigation satellite system precise point positioning. In this study, we have constructed an empirical model of the Es layer using the multivariable nonlinear least-squares-fitting method, based on the S4max from Constellation Observing System for Meteorology, Ionosphere, and Climate satellite radio occultation measurements in the period 2006–2014. The model can describe the climatology of the intensity of Es layers as a function of altitude, latitude, longitude, universal time, and day of year. To validate the model, the outputs of the model were compared with ionosonde data. The correlation coefficients of the hourly foEs and the daily maximum foEs between the ground-based ionosonde observations and model outputs at Beijing are 0.52 and 0.68, respectively. The model can give a global climatology of the intensity of Es layers and the seasonal variations of Es layers, although the Es layers during the summer are highly variable and difficult to accurately predict. The outputs of the model can be implemented in comprehensive models for a description of the climatology of Es layers and provide relatively accurate information about the global variation of Es layers.

Journal ArticleDOI
TL;DR: In this article , a deep convolutional generative adversarial network and Poisson blending (DCGAN-PB) was used to reconstruct the total electron content (TEC) maps in the vicinity of the Korean Peninsula by employing a deep CNN and a poisson blending model.
Abstract: This study reconstructs total electron content (TEC) maps in the vicinity of the Korean Peninsula by employing a deep convolutional generative adversarial network and Poisson blending (DCGAN-PB). Our interest is to rebuild small-scale ionosphere structures on the TEC map in a local region where pronounced ionospheric structures, such as the equatorial ionization anomaly, are absent. The reconstructed regional TEC maps have a domain of 120°–135.5°E longitude and 25.5°–41°N latitude with 0.5° resolution. To achieve this, we first train a DCGAN model by using the International Reference Ionosphere-based TEC maps from 2002 to 2019 (except for 2010 and 2014) as a training data set. Next, the trained DCGAN model generates synthetic complete TEC maps from observation-based incomplete TEC maps. Final TEC maps are produced by blending of synthetic TEC maps with observed TEC data by PB. The performance of the DCGAN-PB model is evaluated by testing the regeneration of the masked TEC observations in 2010 (solar minimum) and 2014 (solar maximum). Our results show that a good correlation between the masked and model-generated TEC values is maintained even with a large percentage (∼80%) of masking. The performance of the DCGAN-PB model is not sensitive to local time, solar activity, and magnetic activity. Thus, the DCGAN-PB model can reconstruct fine ionospheric structures in regions where observations are sparse and distinguishing ionospheric structures are absent. This model can contribute to near real-time monitoring of the ionosphere by immediately providing complete TEC maps.

Journal ArticleDOI
TL;DR: In this paper , the authors focus on the ensemble modeling of CME arrival times and arrival velocities using a drag-based model, which is wellsuited for this purpose due to its simplicity and low computational cost.
Abstract: In recent years, ensemble modeling has been widely employed in space weather to estimate uncertainties in forecasts. We here focus on the ensemble modeling of CME arrival times and arrival velocities using a drag-based model, which is well-suited for this purpose due to its simplicity and low computational cost. Although ensemble techniques have previously been applied to the drag-based model, it is still not clear how to best determine distributions for its input parameters, namely the drag parameter and the solar wind speed. The aim of this work is to evaluate statistical distributions for these model parameters starting from a list of past CME-ICME events. We employ LASCO coronagraph observations to measure initial CME position and speed, and in situ data to associate them with an arrival date and arrival speed. For each event we ran a statistical procedure to invert the model equations, producing parameters distributions as output. Our results indicate that the distributions employed in previous works were appropriately selected, even though they were based on restricted samples and heuristic considerations. On the other hand, possible refinements to the current method are also identified, such as the dependence of the drag parameter distribution on the CME being accelerated or decelerated by the solar wind, which deserve further investigation.

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TL;DR: In this paper , the authors developed ML models on three datasets: the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally matched dataset of outputs from the Jacchia-Bowman 2008 Empirical Thermospheric Density Model (JB2008), and an accelerometer-derived density dataset from CHAllenging Minisatellite Payload (CHAMP).
Abstract: Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information related to the ground-truth dataset used for fitting. A benefit to ML over parametric models is that there are no predefined basis functions limiting the phenomena that can be modeled. In this work, we develop ML models on three datasets: the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally matched dataset of outputs from the Jacchia-Bowman 2008 Empirical Thermospheric Density Model (JB2008), and an accelerometer-derived density dataset from CHAllenging Minisatellite Payload (CHAMP). These ML models are compared to the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar (NRLMSIS 2.0) model to study the presence of post-storm cooling in the middle-thermosphere. We find that both NRLMSIS 2.0 and JB2008-ML do not account for post-storm cooling and consequently perform poorly in periods following strong geomagnetic storms (e.g. the 2003 Halloween storms). Conversely, HASDM-ML and CHAMP-ML do show evidence of post-storm cooling indicating that this phenomenon is present in the original datasets. Results show that density reductions up to 40% can occur 1--3 days post-storm depending on location and the strength of the storm.

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TL;DR: In this article , an available prediction model is established by Long Short-Term Memory (LSTM)-based ensemble learning algorithms, which provides a promising way to give reliable and stable predictions of thermospheric mass density.
Abstract: The accurate prediction of storm-time thermospheric mass density is always critically important and also a challenge. In this paper, an available prediction model is established by Long Short-Term Memory (LSTM)-based ensemble learning algorithms. However, the generalization ability of the deep learning model is often suspicious since training data and testing data are from the same data set in the conventional method. Therefore, in order to objectively validate the performance and generalization of the model, we utilize the GOCE data for training and the SWARM-C data for testing to verify its performance mainly during the geomagnetic storm period. The results show that the LSTM-based ensemble learning model (LELM) is robust under different geomagnetic activity levels and has good generalization ability for the different satellite data set. The prediction accuracy of the LELM is proved to be better than a common-used empirical model (NRLMSISE-00). Thus, our approach provides a promising way to give reliable and stable predictions of thermospheric mass density.

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TL;DR: In this paper , the authors analyse the inner heliospheric context between two eruptive flares that took place in late 2020, i.e. the M4.4 flare of November 29 and the C7.4 flame of December 7.
Abstract: Predictions of coronal mass ejections (CMEs) and solar energetic particles (SEPs) are a central issue in space weather forecasting. In recent years, interest in space weather predictions has expanded to include impacts at other planets beyond Earth as well as spacecraft scattered throughout the heliosphere. In this sense, the scope of space weather science now encompasses the whole heliospheric system, and multi-point measurements of solar transients can provide useful insights and validations for prediction models. In this work, we aim to analyse the whole inner heliospheric context between two eruptive flares that took place in late 2020, i.e. the M4.4 flare of November 29 and the C7.4 flare of December 7. This period is especially interesting because the STEREO-A spacecraft was located ~60{\deg} east of the Sun-Earth line, giving us the opportunity to test the capabilities of "predictions at 360{\deg}" using remote-sensing observations from the Lagrange L1 and L5 points as input. We simulate the CMEs that were ejected during our period of interest and the SEPs accelerated by their shocks using the WSA-Enlil-SEPMOD modelling chain and four sets of input parameters, forming a "mini-ensemble". We validate our results using in-situ observations at six locations, including Earth and Mars. We find that, despite some limitations arising from the models' architecture and assumptions, CMEs and shock-accelerated SEPs can be reasonably studied and forecast in real time at least out to several tens of degrees away from the eruption site using the prediction tools employed here.

Journal ArticleDOI
J. Rex1
TL;DR: In this paper , the authors investigate the quality and continuity of the data that are available in Near-Real-Time (NRT) from the Advanced Composition Explorer and Deep Space Climate Observatory (DSCOVR) spacecraft and find that short gaps are the most common, and are most frequently found in the plasma data.
Abstract: Space weather represents a severe threat to ground-based infrastructure, satellites and communications. Accurately forecasting when such threats are likely (e.g., when we may see large induced currents) will help to mitigate the societal and financial costs. In recent years computational models have been created that can forecast hazardous intervals, however they generally use post-processed “science” solar wind data from upstream of the Earth. In this work we investigate the quality and continuity of the data that are available in Near-Real-Time (NRT) from the Advanced Composition Explorer and Deep Space Climate Observatory (DSCOVR) spacecraft. In general, the data available in NRT corresponds well with post-processed data, however there are three main areas of concern: greater short-term variability in the NRT data, occasional anomalous values and frequent data gaps. Some space weather models are able to compensate for these issues if they are also present in the data used to fit (or train) the model, while others will require extra checks to be implemented in order to produce high quality forecasts. We find that the DSCOVR NRT data are generally more continuous, though they have been available for small fraction of a solar cycle and therefore DSCOVR has experienced a limited range of solar wind conditions. We find that short gaps are the most common, and are most frequently found in the plasma data. To maximize forecast availability we suggest the implementation of limited interpolation if possible, for example, for gaps of 5 min or less, which could increase the fraction of valid input data considerably.

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TL;DR: In this article , the authors proposed a new ensemble machine leaning model for differential electron flux from 30 keV to 4 MeV in the Earth's radiation belts based on the RBSP-A observation data from March 2013 to December 2017.
Abstract: High energy electrons in planetary radiation belts are a major threat to satellites and communications in deep space applications. In order to predict the variations of energetic electron fluxes for different energy channels, we proposed a new ensemble machine leaning model for differential electron flux from 30 keV to 4 MeV in the Earth's radiation belts based on the RBSP-A observation data from March 2013 to December 2017. The deep neural network (DNN), the convolutional neural network (CNN), the combination of CNN and DNN (CNN&DNN), the linear regression (LR), and the light gradient boosting machine (LightGBM) are among the machine learning models chosen. We carefully compared the electron flux predictions for 20 energy levels and all five models can present valid short-time flux forecasts. The DNN model has the poorest result. The LR model is good for short-term forecasting but not so good for long-term forecasting. The LightGBM ensemble model is highly stable, and it has always outperformed other independent models in terms of forecast accuracy. Then the comparison by adding AE and SYM-H indexes is given and the influence of geomagnetic activity conditions can be negligible for this short-time prediction. Furthermore, we applied these five models on Saturn and finally got very similar prediction results. Our results will be significantly useful in instrument designs and system control of future scientific satellites in deep space explorations.

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TL;DR: In this paper , the authors report the origin, evolution, and heliospheric impact of a series of solar transient events that took place during the second half of August 2018, i.e. in the midst of the late declining phase of Solar Cycle 24.
Abstract: The activity of the Sun alternates between a solar minimum and a solar maximum, the former corresponding to a period of "quieter" status of the heliosphere. During solar minimum, it is in principle more straightforward to follow eruptive events and solar wind structures from their birth at the Sun throughout their interplanetary journey. In this paper, we report analysis of the origin, evolution, and heliospheric impact of a series of solar transient events that took place during the second half of August 2018, i.e. in the midst of the late declining phase of Solar Cycle 24. In particular, we focus on two successive coronal mass ejections (CMEs) and a following high-speed stream (HSS) on their way towards Earth and Mars. We find that the first CME impacted both planets, whilst the second caused a strong magnetic storm at Earth and went on to miss Mars, which nevertheless experienced space weather effects from the stream interacting region (SIR) preceding the HSS. Analysis of remote-sensing and in-situ data supported by heliospheric modelling suggests that CME--HSS interaction resulted in the second CME rotating and deflecting in interplanetary space, highlighting that accurately reproducing the ambient solar wind is crucial even during "simpler" solar minimum periods. Lastly, we discuss the upstream solar wind conditions and transient structures responsible for driving space weather effects at Earth and Mars.

Journal ArticleDOI
Tianhe Xu1
TL;DR: In this paper , the authors focus on the kinematic precise point positioning (PPP) solutions at high-latitudes during the March 2015 great geomagnetic storm and discover the mechanism behind the positioning degradation from the perspective of the impacts of the storminduced ionospheric disturbance on the global navigation satellite system (GNSS) data processing.
Abstract: In this study, we focus on the kinematic precise point positioning (PPP) solutions at high-latitudes during the March 2015 great geomagnetic storm. We aim to discover the mechanism behind the positioning degradation from the perspective of the impacts of the storm-induced ionospheric disturbance on the global navigation satellite system (GNSS) data processing. We observed that the phase scintillation dominated the amplitude scintillation at high-latitudes and the variation pattern of the rate of total electron content index (ROTI) was consistent with that of the phase scintillation during the storm. The kinematic PPP errors at high-latitudes were almost three times larger than those at the middle- and low-latitude, which were accompanied by large ROTI variations. From the perspective of GNSS data processing, the large positioning errors were also found to be related to the large number of satellites experiencing cycle slips (CSs). Based on the lock time from the ionospheric scintillation monitoring receiver, we found that a large amount of the CSs was falsely detected under the conventional threshold of the CS detector. By increasing such threshold, the kinematic positioning accuracy at high-latitudes can be improved to obtain similar magnitude as at middle- and low-latitude. The improved positioning accuracy may suggest that the ionospheric disturbance induced by the geomagnetic storm at high-latitudes has minor effects on triggering the CSs. Therefore, precise positioning can be achieved at high-latitudes under geomagnetic storms, given that the CS problem is well addressed.