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Space Weather
Institute of Electrical & Electronics Engineers Inc.
Space Weather

Institute of Electrical & Electronics Engineers Inc.

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Space Weather/Journal Space Weather
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    Space weather journal: Into the future

    Knipp, Delores J.
    1382-1383页
    查看更多>>摘要：The Editor in Chief discusses the vitality of Space Weather Journal.
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union

    Space radiation and plasma effects on satellites and aviation: Quantities and metrics for tracking performance of space weather environment models

    Zheng, YihuaGanushkina, Natalia YuJiggens, PierJun, Insoo...
    1384-1403页
    查看更多>>摘要：The Community Coordinated Modeling Center has been leading community-wide space science and space weather model validation projects for many years. These efforts have been broadened and extended via the newly launched International Forum for Space Weather Modeling Capabilities Assessment (). Its objective is to track space weather models' progress and performance over time, a capability that is critically needed in space weather operations and different user communities in general. The Space Radiation and Plasma Effects Working Team of the aforementioned International Forum works on one of the many focused evaluation topics and deals with five different subtopics () and varieties of particle populations: Surface Charging from tens of eV to 50-keV electrons and internal charging due to energetic electrons from hundreds keV to several MeVs. Single-event effects from solar energetic particles and galactic cosmic rays (several MeV to TeV), total dose due to accumulation of doses from electrons (>100 keV) and protons (>1 MeV) in a broad energy range, and radiation effects from solar energetic particles and galactic cosmic rays at aviation altitudes. A unique aspect of the Space Radiation and Plasma Effects focus area is that it bridges the space environments, engineering, and user communities. The intent of the paper is to provide an overview of the current status and to suggest a guide for how to best validate space environment models for operational/engineering use, which includes selection of essential space environment and effect quantities and appropriate metrics.
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union

    Identifying solar flare precursors using time series of SDO/HMI images and SHARP parameters

    Chen, YangManchester, Ward B.Hero, Alfred O.Toth, Gabor...
    1404-1426页
    查看更多>>摘要：In this paper we present several methods to identify precursors that show great promise for early predictions of solar flare events. A data preprocessing pipeline is built to extract useful data from multiple sources, Geostationary Operational Environmental Satellites and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare inputs for machine learning algorithms. Two classification models are presented: classification of flares from quiet times for active regions and classification of strong versus weak flare events. We adopt deep learning algorithms to capture both spatial and temporal information from HMI magnetogram data. Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space-Weather HMI-Active Region Patch (SHARP) data files. Case studies show a significant increase in the prediction score around 20 hr before strong solar flare events.
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union

    A 21st century view of the March 1989 magnetic storm

    Boteler, D. H.
    1427-1441页
    查看更多>>摘要：On 13 March 1989, the largest magnetic storm of the last century caused widespread effects on power systems including a blackout of the Hydro-Quebec system. Since then this event has become the archetypal disturbance for examining the geomagnetic hazard to power systems. However, even 30 years on from 1989, the story of exactly what happened in March 1989 is far from complete. This paper reexamines the information available about the March 1989 event and uses this to construct a timeline and description of the space weather phenomena and how they caused the power system effects. The evidence shows that the disturbance was caused by two coronal mass ejections (CMEs): the first associated with a X4.5 flare on 10 March and the second linked to a M7.3 flare on 12 March. The arrival of the interplanetary CME shock fronts caused storm sudden commencements at 01.27 and 07.43 UT on 13 March. The transit time and speed of the first (second) interplanetary CME shock are 54.5 hr (31.5 hr) and 760 km/s (1,320 km/s). Empirical relations are used to estimate solar wind speed and southward interplanetary magnetic field, Bs, and give values of v = 980 km/s, Bs = 40 to 60 nT at the peak of the storm. Key findings are that the second storm sudden commencement occurred at the same time as the substorm that impacted the Hydro-Quebec system and indicates that external triggering of the substorm may have contributed to a faster substorm onset than might otherwise have occurred. This caused the production of larger geomagnetically induced currents that caused the Hydro-Quebec blackout. The March 1989 storm had the largest recorded value of the Dst index representing the size of the magnetic storm main phase, but the Hydro-Quebec blackout occurred early in the storm when the Dst value was less disturbed. Only later in the storm did Dst reach its peak value. At this time an expansion of the auroral oval brought disturbances to lower latitudes where they caused power system problems in the United States, United Kingdom, and Sweden.
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union

    Error growth in the mesosphere and lower thermosphere based on hindcast experiments in a whole atmosphere model

    Pedatella, N. M.Liu, H-LMarsh, D. R.Raeder, K....
    1442-1460页
    查看更多>>摘要：The capability to forecast conditions in the mesosphere and lower thermosphere is investigated based on 30-day hindcast experiments that were initialized bimonthly during 2009 and 2010. The hindcasts were performed using the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCMX) with data assimilation provided by the Data Assimilation Research Testbed (DART) ensemble Kalman filter. Analysis of the WACCMX+DART hindcasts reveals several important features that are relevant to forecasting the middle atmosphere. The results show a clear dependence on spatial scale, with the slowest error growth occurring in the zonal mean and the fastest error growth occurring for small-scale waves. The error growth rate is also found to be significantly greater in the upper mesosphere and lower thermosphere compared to in the upper stratosphere to lower mesosphere, suggesting that the forecast skill decreases with increasing altitude. The results demonstrate that the errors in the lower thermosphere reach saturation, on average, in less than 5 days, at least with the current version of WACCMX+DART. A seasonal dependency to the error growth is found at high latitudes in the Northern and Southern Hemispheres but not in the tropics or global average. We additionally investigate the error growth rates for migrating and nonmigrating atmospheric tides and find that the errors saturate after similar to 5 days for tides in the lower thermosphere. The results provide an initial assessment of the error growth rates in the mesosphere and lower thermosphere and are relevant for understanding how whole atmosphere models can potentially improve space weather forecasting.
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union

    Systematic analysis of machine learning and feature selection techniques for prediction of the Kp index

    Zhelayskaya, I. S.Vasile, R.Shprits, Y. Y.Stolle, C....
    1461-1486页
    查看更多>>摘要：The Kp index is a measure of the midlatitude global geomagnetic activity and represents short-term magnetic variations driven by solar wind plasma and interplanetary magnetic field. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere and the radiation belts. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast Kp, based their inferences on the recent history of Kp and on solar wind measurements at L1. In this study, we systematically test how different machine learning techniques perform on the task of nowcasting and forecasting Kp for prediction horizons of up to 12 hr. Additionally, we investigate different methods of machine learning and information theory for selecting the optimal inputs to a predictive model. We illustrate how these methods can be applied to select the most important inputs to a predictive model of Kp and to significantly reduce input dimensionality. We compare our best performing models based on a reduced set of optimal inputs with the existing models of Kp, using different test intervals, and show how this selection can affect model performance.
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union

    Dst index forecast based on ground-level data aided by bio-inspired algorithms

    Lazzus, J. A.Vega-Jorquera, P.Palma-Chilla, L.Stepanova, M., V...
    1487-1506页
    查看更多>>摘要：In this study, different hybridized techniques that combine an artificial neural network (ANN) with bio-inspired optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), and a hybridized PSO+GA were applied to update the ANN connection weights and so forecast the disturbance storm time (Dst) index. The past values of Dst index time series were used as input parameters to forecast its variation from 1 to 6 hours ahead. The database collected considers 233,760 hourly data from 01 January 1990 to 31 August 2016, containing storms and quiet period, grouped into three data sets: learning set (116,880 hourly data points), validation set (58,440 data points), and testing set (58,440 data points). Several ANN configurations were studied and optimized during the training process by evaluating the root mean square error (RMSE) and the correlation coefficient (R). An analysis of the predictive capability of each method was made year per year, and according to the levels of the geomagnetic storm. Also, an additional test was applied to the proposed method using 17 intense geomagnetic storms reported during solar cycle 24, including the St. Patrick's Day storm of 2015. Results show that the hybridized ANN+PSO method can forecast the Dst index quite accurately from 1 to 3 h in advance (with RMSE <= 5 nT and R >= 0.9), while the ANN+PSO+GA method can forecast the Dst index quite accurately from 4 to 6 h ahead (RMSE <= 7 nT and R >= 0.8)
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union