Showing papers in "Space Weather-the International Journal of Research and Applications in 2020"

On the regional variability of dB/dt and its significance to GIC

Abstract: Faraday's law of induction is responsible for setting up a geoelectric field due to the variations in the geomagnetic field caused by ionospheric currents. This drives geomagnetically induced currents (GICs) which flow in large ground‐based technological infrastructure such as high‐voltage power lines. The geoelectric field is often a localized phenomenon exhibiting significant variations over spatial scales of only hundreds of kilometers. This is due to the complex spatiotemporal behavior of electrical currents flowing in the ionosphere and/or large gradients in the ground conductivity due to highly structured local geological properties. Over some regions, and during large storms, both of these effects become significant. In this study, we quantify the regional variability of dB/dt using closely placed IMAGE stations in northern Fennoscandia. The dependency between regional variability, solar wind conditions, and geomagnetic indices are also investigated. Finally, we assess the significance of spatial geomagnetic variations to modeling GICs across a transmission line. Key results from this study are as follows: (1) Regional geomagnetic disturbances are important in modeling GIC during strong storms; (2) dB/dt can vary by several times up to a factor of three compared to the spatial average; (3) dB/dt and its regional variation is coupled to the energy deposited into the magnetosphere; and (4) regional variability can be more accurately captured and predicted from a local index as opposed to a global one. These results demonstrate the need for denser magnetometer networks at high latitudes where transmission lines extending hundreds of kilometers are present.

The Flare Irradiance Spectral Model - Version 2 (FISM2)

Abstract: The Flare Irradiance Spectral Model (FISM) is an important tool for estimating solar variability for a myriad of space weather research studies and applications, and FISM Version 2 (FISM2) has rece...

Conductance Model for Extreme Events : Impact of Auroral Conductance on Space Weather Forecasts

Abstract: NASANational Aeronautics & Space Administration (NASA) [NNX17AB87G, 80NSSC18K1120, 80NSSC17K0015]; NSFNational Science Foundation (NSF) [1663770]; National Science FoundationNational Science Foundation (NSF)

Solar Wind Prediction Using Deep Learning

Abstract: Emanating from the base of the Sun's corona, the solar wind fills the interplanetary medium with a magnetized stream of charged particles whose interaction with the Earth's magnetosphere has space-weather consequences such as geomagnetic storms. Accurately predicting the solar wind through measurements of the spatio-temporally evolving conditions in the solar atmosphere is important but remains an unsolved problem in heliophysics and space-weather research. In this work, we use deep learning for prediction of solar wind (SW) properties. We use Extreme Ultraviolet images of the solar corona from space based observations to predict the SW speed from the NASA OMNIWEB dataset, measured at Lagragian point 1. We evaluate our model against autoregressive and naive models, and find that our model outperforms the benchmark models, obtaining a best-fit correlation of 0.55 $\pm$ 0.03 with the observed data. Upon visualization and investigation of how the model uses data to make predictions, we find higher activation at the coronal holes for fast wind prediction ($\approx$ 3 to 4 days prior to prediction), and at the active regions for slow wind prediction. These trends bear an uncanny similarity to the influence of regions potentially being the sources of fast and slow wind, as reported in literature. This suggests that our model was able to learn some of the salient associations between coronal and solar wind structure without built-in physics knowledge. Such an approach may help us discover hitherto unknown relationships in heliophysics datasets.

When Plasma Streams Tie up Equatorial Plasma Irregularities with Auroral Ones

Abstract: We present a new pattern of storm‐induced ionospheric irregularities behavior at midlatitudes —poleward‐streaming plasma density depletions. Under disturbed conditions, they appear at North America low latitudes as a part of extended postsunset equatorial plasma bubbles, and further, they are streaming from low latitudes in a northwestward, poleward direction toward the main ionospheric trough and auroral irregularities zone. The poleward‐streaming plasma depletions represent a new phenomenon with the similar northwestward transportation path across the continental United States as storm‐enhanced density (SED) plumes. The channels of poleward‐streaming plasma depletions were stretched from low‐latitude base toward higher latitudes—they are found to occur for geomagnetic storms under specific combination of steady southward interplanetary magnetic field, subauroral polarization streams (SAPS) electric fields, and enhanced westward drifts at midlatitudes, resulting in northwestward plasma transportation equatorward of the SAPS region. The poleward‐streaming plasma depletions form an illusion of traveling ionospheric disturbances (TIDs) moving in a poleward, northwestward direction—this propagation direction is opposite to typical equatorward propagation of storm‐induced large‐scale TIDs generated in the auroral zone and propagated toward the equator. This phenomenon is accompanied by strong ionospheric irregularities that occurred over both edges of plasma depletion channel at midlatitudes. For two comparable geomagnetic storms, these poleward‐streaming plasma depletions persisted for several hours, posing a localized threat for GPS‐based positioning applications. Even moderate‐to‐intense storms (Dst minimum−145 nT) can promote such effects at midlatitudes.

Forecasting Megaelectron-Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms

Abstract: Here we present the recent progress in upgrading a predictive model for Megaelectron-Volt (MeV) electrons inside the Earth's outer Van Allen belt. This updated model, called PreMevE 2.0, is demonstrated to make much improved forecasts, particularly at outer Lshells, by including upstream solar wind speeds to the model's input parameter list. Furthermore, based on several kinds of linear and artificial machine learning algorithms, a list of models were constructed, trained, validated and tested with 42-month MeV electron observations from Van Allen Probes. Out-of-sample test results from these models show that, with optimized model hyperparameters and input parameter combinations, the top performer from each category of models has the similar capability of making reliable 1-day (2-day) forecasts with Lshell-averaged performance efficiency values ~ 0.87 (~0.82). Interestingly, the linear regression model is often the most successful one when compared to other models, which indicates the relationship between 1 MeV electron dynamics and precipitating electrons is dominated by linear components. It is also shown that PreMevE 2.0 can reasonably predict the onsets of MeV electron events in 2-day forecasts. This improved PreMevE model is driven by observations from longstanding space infrastructure (a NOAA satellite in low-Earth-orbit, the solar wind monitor at the L1 point, and one LANL satellite in geosynchronous orbit) to make high-fidelity forecasts for MeV electrons, and thus can be an invaluable space weather forecasting tool for the future.

Solar Flare Intensity Prediction With Machine Learning Models

Abstract: We develop a mixed Long Short Term Memory (LSTM) regression model to predict the maximum solar flare intensity within a 24-hour time window 0$\sim$24, 6$\sim$30, 12$\sim$36 and 24$\sim$48 hours ahead of time using 6, 12, 24 and 48 hours of data (predictors) for each Helioseismic and Magnetic Imager (HMI) Active Region Patch (HARP). The model makes use of (1) the Space-weather HMI Active Region Patch (SHARP) parameters as predictors and (2) the exact flare intensities instead of class labels recorded in the Geostationary Operational Environmental Satellites (GOES) data set, which serves as the source of the response variables. Compared to solar flare classification, the model offers us more detailed information about the exact maximum flux level, i.e. intensity, for each occurrence of a flare. We also consider classification models built on top of the regression model and obtain better results in solar flare classifications. Our results suggest that the most efficient time period for predicting the solar activity is within 24 hours before the prediction time using the SHARP parameters and the LSTM model.

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

Abstract: The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to a variety of satellite systems. While various models of the...

Benchmarking Forecasting Models for Space Weather Drivers

Abstract: Space weather indices are commonly used to drive operational forecasts of various geospace systems, including the thermosphere for mass density and satellite drag. The drivers serve as proxies for various processes that cause energy flow and deposition in the geospace system. Forecasts of neutral mass density is a major uncertainty in operational orbit prediction and collision avoidance for objects in low earth orbit (LEO). For the strongly driven system, accuracy of space weather driver forecasts is crucial for operations. The High Accuracy Satellite Drag Model (HASDM) currently employed by the United States Air Force in an operational environment is driven by four (4) solar and two (2) geomagnetic proxies. Space Environment Technologies (SET) is contracted by the space command to provide forecasts for the drivers. This work performs a comprehensive assessment for the performance of the driver forecast models. The goal is to provide a benchmark for future improvements of the forecast models. Using an archived data set spanning six (6) years and 15,000 forecasts across solar cycle 24, we quantify the temporal statistics of the model performance.

Real‐Time Thermospheric Density Estimation Via Two‐Line‐Element Data Assimilation

Abstract: Inaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction. To improve orbit prediction, real-time density estimation is required. In this work, we develop a reduced-order dynamic model for the thermospheric density by computing the main spatial modes of the atmosphere and deriving a linear model for the dynamics. The model is then used to estimate the density using two-line element (TLE) data by simultaneously estimating the reduced-order modes and the orbits and ballistic coefficients of several objects using an unscented Kalman filter. Accurate density estimation using the TLEs of 17 objects is demonstrated and validated against CHAMP and GRACE accelerometer-derived densities. Finally, the use of the model for density forecasting is shown.

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Abstract: We acknowledge and thank the Wind and ACE teams for the solar wind data and NASA GSFC's Space Physics Data Facility's CDAWeb service for data availability (https://cdaweb.gsfc.nasa.gov/index.html/). The results presented in the paper also rely on the SC list made available by the International Service on Rapid Magnetic Variations (https://www.obsebre.es/en/rapid) and published by the Observatorio de l'Ebre in association with the International Association of Geomagnetism and Aeronomy (IAGA) and the International Service of Geomagnetic Indices (ISGI). We thank the involved national institutes, the INTERMAGNET network and the ISGI. The authors would like to thank A. A. Samsonov for helpful discussions. This work has also used the interplanetary shock catalog compiled by Oliveira, Arel, et al. (2018), including those intervals identified Wang et al. (2010), and Dr. J. C. Kasper for the Wind (https://www.cfa.harvard.edu/shocks/wi_data/) and ACE data (https://www.cfa.harvard.edu/shocks/ac_master_data/), and also by the ACE team (https://www‐ssg.sr.unh.edu/mag/ace/ACElists/obs_list.html#shocks). It may be found in the supporting information of Oliveira, Arel, et al. (2018). A. W. S. and I. J. R. were supported by STFC Consolidated Grant ST/S000240/1 and NERC grants NE/P017150/1 and NE/V002724/1. C. F. was supported by the NERC Independent Research Fellowship NE/N014480/1 and STFC Consolidated Grant ST/S000240/1. D. M. O. was supported by NASA through grant HISFM18‐HIF (Heliophysics Innovation Fund). The analysis in this paper was performed using python, including the pandas (McKinney, 2010), numpy (van der Walt et al., 2011), scikit‐learn (Pedregosa et al., 2011), scipy (Virtanen et al., 2020) and matplotlib (Hunter, 2007) libraries. Detailed documentation for the models can be found at https://scikit‐learn.org/, while the specific implementations of the models used in this work are: sklearn.linear_model.LogisticRegression, sklearn.naive_bayes.GaussianNB, sklearn.gaussian_process.GaussianProcessClassifier, sklearn.ensemble.RandomForestClassifier. Funding Information: National Aeronautics and Space Administration (NASA). Grant Number: HISFM18‐HIF Natural Environment Research Council (NERC). Grant Numbers: NE/P017150/1, NE/V002724/1, NE/N014480/1 RCUK | Science and Technology Facilities Council (STFC). Grant Number: ST/S000240/1

Lyman-alpha Variability During Solar Flares Over Solar Cycle 24 Using GOES-15/EUVS-E

Abstract: The chromospheric Lyman‐alpha line of neutral hydrogen (Lyα; 1216 A) is the strongest emission line in the solar spectrum. Fluctuations in Lyα are known to drive changes in planetary atmospheres, although few instruments have had the ability to capture rapid Lyα enhancements during solar flares. In this paper, we describe flare‐associated emissions via a statistical study of 477 M‐ and X‐class flares as observed by the Extreme UltraViolet Sensor on board the 15th Geostationary Operational Environmental Satellite, which has been monitoring the full‐disk solar Lyα irradiance on 10‐s timescales over the course of Solar Cycle 24. The vast majority (95%) of these flares produced Lyα enhancements of 10% or less above background levels, with a maximum increase of ∼30%. The irradiance in Lyα was found to exceed that of the 1–8 A X‐ray irradiance by as much as two orders of magnitude in some cases, although flares that occurred closer to the solar limb were found to exhibit less of a Lyα enhancement. This center‐to‐limb variation was verified through a joint, stereoscopic observation of an X‐class flare that appeared near the limb as viewed from Earth, but close to disk center as viewed by the MAVEN spacecraft in orbit around Mars. The frequency distribution of peak Lyα was found to have a power‐law slope of 2.8±0.27. We also show that increased Lyα flux is closely correlated with induced currents in the ionospheric E‐layer through the detection of the solar flare effect as observed by the Kakioka magnetometer.

Improving Neutral Density Predictions Using Exospheric Temperatures Calculated on a Geodesic, Polyhedral Grid

Abstract: NASANational Aeronautics & Space Administration (NASA) [NNX17AC04G, NNX15AE05G]; Office of Naval ResearchOffice of Naval Research; NASA Heliophysics Division Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics Project