scholarly journals Probabilistic Geomagnetic Storm Forecasting via Deep Learning

2021 ◽  
Author(s):  
Adrian Tasistro-Hart ◽  
Alexander Grayver ◽  
Alexey Kuvshinov
Keyword(s):  
Neural Networks ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Solar Disk ◽  
Geomagnetic Storms ◽  
Probabilistic Forecasts ◽  
Uncertainty Estimates ◽  
External Component ◽  
Almost All ◽  
Communication Lines

<p>By causing time variation in Earth's external magnetic field, geomagnetic storms can induce damaging currents in ground-based conducting infrastructure, such as power and communication lines.  The physical link between solar activity and Earth's magnetosphere, while complicated, provides the basis for attempts to forecast geomagnetic storms. Fortunately, we have abundant observational data of both the solar disk and solar wind, which are ameable to the application of data-hungry neural networks to the forecasting problem. To date, almost all neural networks trained for geomagnetic storm forecasting have utilized solar wind observations from the Earth-Sun first Lagrangian point (L1) or closer and have generated deterministic output without uncertainty estimates. Furthermore, existing models generate forecasts for indices that are also sensitive to induced internal magnetic fields, complicating the forecasting problem with another layer of non-linearity. In this work, we present neural networks trained on observations from both the solar disk and the L1 point. Our architecture generates reliable probabilistic forecasts over Est, the external component of the disturbance storm time index, showing that neural networks can learn measures of confidence in their output. </p>

scholarly journals Probabilistic prediction of geomagnetic storms and the Kp index

2020 ◽  
Vol 10 ◽  
pp. 36
Author(s):  
Shibaji Chakraborty ◽  
Steven Karl Morley
Keyword(s):  
Geomagnetic Storm ◽  
Space Weather ◽  
Geomagnetic Storms ◽  
Propagation Time ◽  
Probabilistic Prediction ◽  
Storm Activity ◽  
Layered Architecture ◽  
Probabilistic Forecasts ◽  
Uncertainty Estimates ◽  
Forecast Models

Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index K p in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic K p predictions using a variety of methods – including empirically-derived functions, physics-based models, and neural networks – but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-ahead K p prediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (K p  ≥ 5−) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.

scholarly journals Total electron content responses to HILDCAAs and geomagnetic storms over South America

2017 ◽  
Vol 35 (6) ◽  
pp. 1309-1326 ◽  
Author(s):  
Patricia Mara de Siqueira Negreti ◽  
Eurico Rodrigues de Paula ◽  
Claudia Maria Nicoli Candido
Keyword(s):  
Solar Wind ◽  
South America ◽  
Total Electron Content ◽  
Geomagnetic Storm ◽  
Electric Fields ◽  
Geomagnetic Storms ◽  
Time Variability ◽  
Electron Content ◽  
Quiet Time ◽  
Total Electron

Abstract. Total electron content (TEC) is extensively used to monitor the ionospheric behavior under geomagnetically quiet and disturbed conditions. This subject is of greatest importance for space weather applications. Under disturbed conditions the two main sources of electric fields, which are responsible for changes in the plasma drifts and for current perturbations, are the short-lived prompt penetration electric fields (PPEFs) and the longer-lasting ionospheric disturbance dynamo (DD) electric fields. Both mechanisms modulate the TEC around the globe and the equatorial ionization anomaly (EIA) at low latitudes. In this work we computed vertical absolute TEC over the low latitude of South America. The analysis was performed considering HILDCAA (high-intensity, long-duration, continuous auroral electrojet (AE) activity) events and geomagnetic storms. The characteristics of storm-time TEC and HILDCAA-associated TEC will be presented and discussed. For both case studies presented in this work (March and August 2013) the HILDCAA event follows a geomagnetic storm, and then a global scenario of geomagnetic disturbances will be discussed. Solar wind parameters, geomagnetic indices, O ∕ N2 ratios retrieved by GUVI instrument onboard the TIMED satellite and TEC observations will be analyzed and discussed. Data from the RBMC/IBGE (Brazil) and IGS GNSS networks were used to calculate TEC over South America. We show that a HILDCAA event may generate larger TEC differences compared to the TEC observed during the main phase of the precedent geomagnetic storm; thus, a HILDCAA event may be more effective for ionospheric response in comparison to moderate geomagnetic storms, considering the seasonal conditions. During the August HILDCAA event, TEC enhancements from  ∼  25 to 80 % (compared to quiet time) were observed. These enhancements are much higher than the quiet-time variability observed in the ionosphere. We show that ionosphere is quite sensitive to solar wind forcing and considering the events studied here, this was the most important source of ionospheric responses. Furthermore, the most important source of TEC changes were the long-lasting PPEFs observed on August 2013, during the HILDCAA event. The importance of this study relies on the peculiarity of the region analyzed characterized by high declination angle and ionospheric gradients which are responsible for creating a complex response during disturbed periods.

Neural network applications in geomagnetic storm prognosis based on the pre-storm occurrence of magnetic islands in the solar wind

2020 ◽  
Author(s):  
Mikhail Fridman
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Particle Acceleration ◽  
Geomagnetic Storm ◽  
High Speed ◽  
Geomagnetic Storms ◽  
Pattern Search ◽  
Small Scale ◽  
Magnetic Islands ◽  
Solar Wind Density

<p>So far, the problem of a short-term forecast of geomagnetic storms can be considered as solved. Meanwhile, mid-term prognoses of geomagnetic storms with an advance time from 3 hours to 3 days are still unsuccessful (see  https://www.swpc.noaa.gov/sites/default/files/images/u30/Max%20Kp%20and%20GPRA.pdf).</p><p> This fact suggests a necessity of looking for specific processes in the solar wind preceding geomagnetic storms. Knowing that magnetic cavities filled with magnetic islands and current sheets are formed in front of high-speed streams of any type (Khabarova et al., 2015, 2016, 2018; Adhikari et al., 2019), we have performed an analysis of the corresponding ULF variations in the solar wind density observed at the Earth's orbit from hours to days before the arrival of a geoeffective stream or flow. The fact of the occurrence of ULF-precursors of geomagnetic storms was noticed a long time ago (Khabarova 2007; Khabarova & Yermolaev, 2007) and related prognostic methods were recently developed (Kogai et al. 2019), while the problem of automatization of the prognosis remained unsolved.</p><p> A new geomagnetic storm forecast method, which employs a Recurrent Neural Network (RNN) for an automatic pattern search, is proposed. An ability of self-teaching and extracting deeply hidden non-linear patterns is the main advantage of Deep Neural Networks (DNNs) with multiple layers over traditional Machine Learning methods. We show a success of the RNN method, using either the unprocessed solar wind density data or Wavelet analysis coefficients as the input parameter for a DNN to perform an automatic mid-term prognosis of geomagnetic storms.  </p><p>Adhikari, L., et al. 2019, The Role of Magnetic Reconnection–associated Processes in Local Particle Acceleration in the Solar Wind, ApJ, 873, 1, 72, https://doi.org/10.3847/1538-4357/ab05c6<br>Kogai T.G. et al., Pre-storm ULF variations in the solar wind density and interplanetary magnetic field as key parameters to build a mid-term prognosis of geomagnetic storms. “GRINGAUZ 100: PLASMA IN THE SOLAR SYSTEM”, IKI RAS, Moscow, June 13–15, 2018, 140-143, ISBN 978-5-00015-043-6. https://www.researchgate.net/publication/327781146_Pre-storm_ULF_variations_in_the_solar_wind_density_and_interplanetary_magnetic_field_as_key_parameters_to_build_a_mid-term_prognosis_of_geomagnetic_storms<br> Khabarova O. V., et al. 2018,  Re-acceleration of energetic particles in large-scale heliospheric magnetic cavities, Proceedings of the IAU, 76-82, https://doi.org/10.1017/S1743921318000285 <br>Khabarova O.V., et al. Small-scale magnetic islands in the solar wind and their role in particle acceleration. II. Particle energization inside magnetically confined cavities. 2016, ApJ, 827, 122, http://iopscience.iop.org/article/10.3847/0004-637X/827/2/122<br>Khabarova O., et al. Small-scale magnetic islands in the solar wind and their role in particle acceleration. 1. Dynamics of magnetic islands near the heliospheric current sheet. 2015, ApJ, 808, 181, https://doi.org/10.1088/0004-637X/808/2/181</p><p>Khabarova O.V., Current Problems of Magnetic Storm Prediction and Possible Ways of Their Solving. Sun&Geosphere,  http://sg.shao.az/v2n1/SG_v2_No1_2007-pp-33-38.pdf , 2(1), 33-38, 2007</p><p>Khabarova O.V. & Yu.I.Yermolaev, Solar wind parameters' behavior before and after magnetic storms, JASTP, 70, 2-4, 2008, 384-390, http://dx.doi.org/10.1016/j.jastp.2007.08.024</p>

scholarly journals Magnetospheric chaos and dynamical complexity response during storm time disturbance

2020 ◽  
Author(s):  
Irewola Aaron Oludehinwa ◽  
Olasunkanmi Isaac Olusola ◽  
Olawale Segun Bolaji ◽  
Olumide Olayinka Odeyemi ◽  
Abdullahi Ndzi Njah
Keyword(s):  
Time Series ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Time Series Data ◽  
Geomagnetic Storms ◽  
Approximate Entropy ◽  
Series Data ◽  
Dynamical Complexity ◽  
Significant Difference ◽  
Dynamical Features

Abstract. In this study, we examine the magnetospheric chaos and dynamical complexity response in the disturbance storm time (Dst) and solar wind electric field (VBs) during different categories of geomagnetic storm (minor, moderate and major geomagnetic storm). The time series data of the Dst and VBs are analyzed for the period of nine years using nonlinear dynamics tools (Maximal Lyapunov Exponent, MLE, Approximate Entropy, ApEn and Delay Vector Variance, DVV). We found a significant trend between each nonlinear parameter and the categories of geomagnetic storm. The MLE and ApEn values of the Dst indicate that chaotic and dynamical complexity response are high during minor geomagnetic storms, reduce at moderate geomagnetic storms and declined further during major geomagnetic storms. However, the MLE and ApEn values obtained in VBs indicate that chaotic and dynamical complexity response are high with no significant difference between the periods that are associate with minor, moderate and major geomagnetic storms. The test for nonlinearity in the Dst time series during major geomagnetic storm reveals the strongest nonlinearity features. Based on these findings, the dynamical features obtained in the VBs as input and Dst as output of the magnetospheric system suggest that the magnetospheric dynamics is nonlinear and the solar wind dynamics is consistently stochastic in nature.

scholarly journals ESTIMASI BADAI GEOMAGNET BERDASARKAN PERILAKU PARAMETER ANGIN SURYA DAN MEDAN MAGNET ANTARPLANET SEBELUM BADAI GEOMAGNET (THE ESTIMATION OF GEOMAGNETIC STORM BASED ON SOLAR WIND PARAMETERS AND INTERPLANETARY MAGNETIC FIELD BEHAVIOR BEFORE GEOMAGNETIC STOR

2017 ◽  
Vol 14 (2) ◽  
pp. 17
Author(s):  
Anwar Santoso ◽  
Mamat Rahimat ◽  
Rasdewita Kesumaningrum ◽  
Siska Filawati
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Space Weather ◽  
Geomagnetic Storms ◽  
Storm Events ◽  
Science Center ◽  
Solar Wind Parameters ◽  
The Impact ◽  
The Imf

Space weather research is the principal activity at the Space Science Center, Lapan to learn characteristics and generator source of the space weather so that can mitigate its the impact on the Earth's environment as mandated in Law No. 21 Year 2013. One of them is the phenomenon of geomagnetic storms. Geomagnetic storms caused by the entry of solar wind together with the IMF Bz that leads to the south. The behavior of the solar wind parameters together with the IMF Bz before geomagnetic storms can determine the formation of geomagnetic storms that caused it. In spite that, by the solar wind parameters and IMF Bz behavior before geomagnetic storm can be estimated its intensity through the equation Dst * = 1.599 * Ptotal - 34.48. The result of this equation is obtained that the Dst minimum deviation between the raw data and the output of this equation to the geomagnetic storm events on March 17, 2013 is about of -2.51 nT or 1.9% and on the geomagnetic storm events on February 19, 2014 is about of 2.77 nT or 2, 5%. Thus, the equation Dst * = 1.599 * Ptotal - 34.48 is very good for the estimation of geomagnetic storms.

scholarly journals Geomagnetic storms and their impacts on Ethiopian power grid

2020 ◽  
Vol 10 (2) ◽  
pp. 55-64
Author(s):  
Gebregiorgis Abraha ◽  
Tesfay Yemane ◽  
Tsegaye Kassa
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Geomagnetic Storms ◽  
Solar Wind Speed ◽  
Recovery Period ◽  
Power Grids ◽  
Storm Events ◽  
Earth Magnetic Field ◽  
Speed Solar Wind

In present work we analysed eight geomagnetic storm events in 2015/2016 and studied the possible influence of these events on Ethiopian power grids. The results showed that the majority of the forced power outages occurred in the period of the main phase of events and the recovery period of the geomagnetic storms. The geomagnetic storms are characterised by different indices and parameters such as the disturbance storm time (Dst) values, coronal mass ejection (CME) speed, solar wind speed (V sw) and interplanetary magnetic field (IMF-Bz) on the selected dates. In most cases the observed geomagnetic storms were produced by the CME-driven storms as they show a storm sudden commencement (SSCs) before the main storms, and also have the short recovery periods. The sudden jumps of the solar wind velocities and IMF-Bz are also consistent with occurrence of the CMEs. Moreover, this effect can be traced in changes of Earth magnetic field during geomagnetic storm and quiet days. The observed CME-driven storms can produce highly variable magnetic fields on the transformers and provide forced outages, however the studied outages have not been recognised as those one driven by a geomagnetic storm.

scholarly journals Solar cycle effect on geomagnetic storms caused by interplanetary magnetic clouds

2006 ◽  
Vol 24 (12) ◽  
pp. 3383-3389 ◽  
Author(s):  
C.-C. Wu ◽  
R. P. Lepping
Keyword(s):  
Solar Wind ◽  
Geomagnetic Storm ◽  
Solar Minimum ◽  
Geomagnetic Storms ◽  
Solar Maximum ◽  
Solar Wind Speed ◽  
Magnetic Clouds ◽  
Active Period ◽  
Quiet Period ◽  
Solar Wind Parameters

Abstract. We investigated geomagnetic activity which was induced by interplanetary magnetic clouds during the past four solar cycles, 1965–1998. We have found that the intensity of such geomagnetic storms is more severe in solar maximum than in solar minimum. In addition, we affirm that the average solar wind speed of magnetic clouds is faster in solar maximum than in solar minimum. In this study, we find that solar activity level plays a major role on the intensity of geomagnetic storms. In particular, some new statistical results are found and listed as follows. (1) The intensity of a geomagnetic storm in a solar active period is stronger than in a solar quiet period. (2) The magnitude of negative Bzmin is larger in a solar active period than in a quiet period. (3) Solar wind speed in an active period is faster than in a quiet period. (4) VBsmax in an active period is much larger than in a quiet period. (5) Solar wind parameters, Bzmin, Vmax and VBsmax are correlated well with geomagnetic storm intensity, Dstmin during a solar active period. (6) Solar wind parameters, Bzmin, and VBsmax are not correlated well (very poorly for Vmax) with geomagnetic storm intensity during a solar quiet period. (7) The speed of the solar wind plays a key role in the correlation of solar wind parameters vs. the intensity of a geomagnetic storm. (8) More severe storms with Dstmin≤−100 nT caused by MCs occurred in the solar active period than in the solar quiet period.

scholarly journals Predicting geomagnetic storms from solar-wind data using time-delay neural networks

1996 ◽  
Vol 14 (7) ◽  
pp. 679-686 ◽  
Author(s):  
H. Gleisner ◽  
H. Lundstedt ◽  
P. Wintoft
Keyword(s):  
Neural Networks ◽  
Solar Wind ◽  
Time Delay ◽  
Input Data ◽  
Ring Current ◽  
Geomagnetic Storms ◽  
Time History ◽  
Recovery Phase ◽  
Wind Data ◽  
Data Sequence

Abstract. We have used time-delay feed-forward neural networks to compute the geomagnetic-activity index Dst one hour ahead from a temporal sequence of solar-wind data. The input data include solar-wind density n, velocity V and the southward component Bz of the interplanetary magnetic field. Dst is not included in the input data. The networks implement an explicit functional relationship between the solar wind and the geomagnetic disturbance, including both direct and time-delayed non-linear relations. In this study we especially consider the influence of varying the temporal size of the input-data sequence. The networks are trained on data covering 6600 h, and tested on data covering 2100 h. It is found that the initial and main phases of geomagnetic storms are well predicted, almost independent of the length of the input-data sequence. However, to predict the recovery phase, we have to use up to 20 h of solar-wind input data. The recovery phase is mainly governed by the ring-current loss processes, and is very much dependent on the ring-current history, and thus also the solar-wind history. With due consideration of the time history when optimizing the networks, we can reproduce 84% of the Dst variance.

scholarly journals An investigation into the correlation of geomagnetic storms with tropospheric parameters over the South Pole

2003 ◽  
Vol 21 (5) ◽  
pp. 1095-1100 ◽  
Author(s):  
M. M. Lam ◽  
A. S. Rodger
Keyword(s):  
Solar Wind ◽  
Geomagnetic Storm ◽  
Lower Stratosphere ◽  
Geomagnetic Storms ◽  
Cosmic Ray ◽  
Magnetic Activity ◽  
Atmospheric Composition ◽  
South Pole ◽  
The South ◽  
Interplanetary Physics

Abstract. We test the proposal that the Sun’s magnetic activity, communicated via the solar wind, provides a link between solar variability and the Earth’s climate in the Antarctic troposphere. The strength of a geomagnetic storm is one indicator of the state of the solar wind; therefore, we use the dates of 51 moderate to strong winter geomagnetic storms from the period 1961–1990 to conduct a series of superposed epoch analyses of the winter South Pole isobaric height and temperature, at pressures of between 100–500 mbar. Using Student’s t -test to compare the mean value of the pre- and post-storm data sets, we find no evidence to support the hypothesis that there is a statistically-significant correlation between the onset of a geomagnetic storm and changes in the isobaric temperature or height of the troposphere and lower stratosphere over the South Pole during winter months. This concurs with a similar study of the variability of the troposphere and lower stratosphere over the South Pole (Lam and Rodger, 2002) which uses drops in the level of observed galactic cosmic ray intensity, known as Forbush decreases, as a proxy for solar magnetic activity instead of geomagnetic storms.Key words. Interplanetary physics (solar wind plasma; cosmic rays) – Atmospheric composition and structure (pressure, density and temperature)

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