scholarly journals Predicting the occurrence of super-storms

2005 ◽  
Vol 23 (9) ◽  
pp. 2989-2995 ◽  
Author(s):  
N. Srivastava
Keyword(s):  
Solar Cycle ◽  
Geomagnetic Storm ◽  
High Speed ◽  
Geomagnetic Storms ◽  
Active Regions ◽  
Initial Speed ◽  
Reliable Prediction ◽  
Intense Storm ◽  
Ram Pressure ◽  
Class Flare

Abstract. A comparative study of five super-storms (Dst<-300 nT) of the current solar cycle after the launch of SoHO, to identify solar and interplanetary variables that influence the magnitude of resulting geomagnetic storms, is described. Amongst solar variables, the initial speed of a CME is considered the most reliable predictor of the strength of the associated geomagnetic storm because fast mass ejections are responsible for building up the ram pressure at the Earth's magnetosphere. However, although most of the super-storms studied were associated with high speed CMEs, the Dst index of the resulting geomagnetic storms varied between -300 to -472 nT. The most intense storm of 20 November 2003, (Dst ~ -472 nT) had its source in a comparatively smaller active region and was associated with a relatively weaker, M-class flare while all other super-storms had their origins in large active regions and were associated with strong X-class flares. However, this superstorm did not show any associated extraordinary solar and interplanetary characteristics. The study also reveals the challenge in the reliable prediction of the magnitude of a geomagnetic storm from solar and interplanetary variables.

scholarly journals Statistics of the largest geomagnetic storms per solar cycle (1844-1993)

1997 ◽  
Vol 15 (6) ◽  
pp. 719-728 ◽  
Author(s):  
D. M. Willis ◽  
P. R. Stevens ◽  
S. R. Crothers
Keyword(s):  
Statistical Analysis ◽  
Solar Cycle ◽  
Geomagnetic Storm ◽  
Extreme Values ◽  
Geomagnetic Storms ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Relative Dispersion ◽  
Solar Cycles ◽  
Aa Index

Abstract. A previous application of extreme-value statistics to the first, second and third largest geomagnetic storms per solar cycle for nine solar cycles is extended to fourteen solar cycles (1844–1993). The intensity of a geomagnetic storm is measured by the magnitude of the daily aa index, rather than the half-daily aa index used previously. Values of the conventional aa index (1868–1993), supplemented by the Helsinki Ak index (1844–1880), provide an almost continuous, and largely homogeneous, daily measure of geomagnetic activity over an interval of 150 years. As in the earlier investigation, analytic expressions giving the probabilities of the three greatest storms (extreme values) per solar cycle, as continuous functions of storm magnitude (aa), are obtained by least-squares fitting of the observations to the appropriate theoretical extreme-value probability functions. These expressions are used to obtain the statistical characteristics of the extreme values; namely, the mode, median, mean, standard deviation and relative dispersion. Since the Ak index may not provide an entirely homogeneous extension of the aa index, the statistical analysis is performed separately for twelve solar cycles (1868–1993), as well as nine solar cycles (1868–1967). The results are utilized to determine the expected ranges of the extreme values as a function of the number of solar cycles. For fourteen solar cycles, the expected ranges of the daily aa index for the first, second and third largest geomagnetic storms per solar cycle decrease monotonically in magnitude, contrary to the situation for the half-daily aa index over nine solar cycles. The observed range of the first extreme daily aa index for fourteen solar cycles is 159–352 nT and for twelve solar cycles is 215–352 nT. In a group of 100 solar cycles the expected ranges are expanded to 137–539 and 177–511 nT, which represent increases of 108% and 144% in the respective ranges. Thus there is at least a 99% probability that the daily aa index will satisfy the condition aa < 550 for the largest geomagnetic storm in the next 100 solar cycles. The statistical analysis is used to infer that remarkable conjugate auroral observations on the night of 16 September 1770, which were recorded during the first voyage of Captain Cook to Australia, occurred during an intense geomagnetic storm.

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

&lt;p&gt;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 &amp;#160;https://www.swpc.noaa.gov/sites/default/files/images/u30/Max%20Kp%20and%20GPRA.pdf).&lt;/p&gt;&lt;p&gt;&amp;#160;This fact suggests a necessity of looking for specific&amp;#160;processes&amp;#160;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 &amp; Yermolaev, 2007) and related&amp;#160;prognostic methods were recently developed (Kogai et al. 2019), while the problem of automatization of the prognosis remained unsolved.&lt;/p&gt;&lt;p&gt;&amp;#160;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&amp;#160;RNN&amp;#160;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.&amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;Adhikari, L., et al. 2019, The Role of Magnetic Reconnection&amp;#8211;associated Processes in Local Particle Acceleration in the Solar Wind, ApJ, 873, 1, 72,&amp;#160;https://doi.org/10.3847/1538-4357/ab05c6&lt;br&gt;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. &amp;#8220;GRINGAUZ 100: PLASMA IN THE SOLAR SYSTEM&amp;#8221;, IKI RAS, Moscow, June 13&amp;#8211;15, 2018, 140-143, ISBN 978-5-00015-043-6.&amp;#160;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&lt;br&gt;&amp;#160;Khabarova O. V., et al. 2018, &amp;#160;Re-acceleration of energetic particles in large-scale heliospheric magnetic cavities, Proceedings of the IAU, 76-82,&amp;#160;https://doi.org/10.1017/S1743921318000285&amp;#160;&lt;br&gt;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,&amp;#160;122,&amp;#160;http://iopscience.iop.org/article/10.3847/0004-637X/827/2/122&lt;br&gt;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,&amp;#160;https://doi.org/10.1088/0004-637X/808/2/181&lt;/p&gt;&lt;p&gt;Khabarova O.V., Current Problems of Magnetic Storm Prediction and Possible Ways of Their Solving. Sun&amp;Geosphere, &amp;#160;http://sg.shao.az/v2n1/SG_v2_No1_2007-pp-33-38.pdf&amp;#160;, 2(1), 33-38, 2007&lt;/p&gt;&lt;p&gt;Khabarova O.V. &amp; Yu.I.Yermolaev, Solar wind parameters' behavior before and after magnetic storms, JASTP, 70, 2-4, 2008, 384-390,&amp;#160;http://dx.doi.org/10.1016/j.jastp.2007.08.024&lt;/p&gt;

Statistical Study of Geomagnetic Storms during Year 1996-2007

2012 ◽  
Vol 433-440 ◽  
pp. 268-271
Author(s):  
Balveer S. Rathore ◽  
Subhash C. Kaushik ◽  
K.K. Parashar ◽  
Rammohan S. Bhadoria ◽  
Dinesh C. Gupta
Keyword(s):  
Magnetic Field ◽  
Solar Cycle ◽  
Geomagnetic Storm ◽  
Statistical Study ◽  
Geomagnetic Storms ◽  
Sunspot Cycle ◽  
Solar Wind Plasma ◽  
Strongly Correlated ◽  
Minimum Phase ◽  
Solar Cycle 23

A geomagnetic storm is a global disturbance in Earth’s magnetic field usually occurred due to abnormal conditions in the interplanetary magnetic field (IMF) and solar wind plasma emissions caused by various solar phenomenon. A study of 220 geomagnetic storms associated with disturbance storm time (Dst) decreases of more than -50 nT to -300 nT, observed during 1996-2007, the span of solar cycle 23. We have analyzed and studied them statistically. We find yearly occurrences of geomagnetic storm are strongly correlated with 11-year sunspot cycle, but no significant correlation between the maximum and minimum phase of solar cycle-23 have been found. It is also found that solar cycle-23 is remarkable for occurrence of Intense geomagnetic storm during its declining phase. The detailed results are discussed in this paper.

scholarly journals The CME link to geomagnetic storms

2009 ◽  
Vol 5 (S264) ◽  
pp. 326-335 ◽  
Author(s):  
Nat Gopalswamy
Keyword(s):  
Magnetic Field ◽  
Free Energy ◽  
Coronal Mass Ejection ◽  
High Speed ◽  
Geomagnetic Storms ◽  
Flux Rope ◽  
Active Regions ◽  
Angular Width ◽  
Central Meridian ◽  
Mass Ejection

AbstractThe coronal mass ejection (CME) link to geomagnetic storms stems from the southward component of the interplanetary magnetic field contained in the CME flux ropes and in the sheath between the flux rope and the CME-driven shock. A typical storm-causing CME is characterized by (i) high speed, (ii) large angular width (mostly halos and partial halos), and (iii) solar source location close to the central meridian. For CMEs originating at larger central meridian distances, the storms are mainly caused by the sheath field. Both the magnetic and energy contents of the storm-producing CMEs can be traced to the magnetic structure of active regions and the free energy stored in them.

scholarly journals Variability of Relativistic Electron Flux (E > 2 MeV) during Geo-Magnetically Quiet and Disturbed days: A Case Study

2020 ◽  
Author(s):  
Tulsi Thapa ◽  
Binod Adhikari ◽  
Prashrit Baruwal ◽  
Kiran Pudasainee
Keyword(s):  
Relativistic Electron ◽  
Geomagnetic Storm ◽  
Radiation Belt ◽  
Cross Correlation ◽  
Electron Flux ◽  
Geomagnetic Storms ◽  
Correlation Technique ◽  
Outer Radiation Belt ◽  
Cross Correlation Analysis ◽  
Intense Storm

Abstract. We analyzed the relativistic electron fluxes (E > 2 MeV) during three different geomagnetic storms: moderate, intense, and super-intense and one geo-magnetically quiet period. We have opted Continuous wavelet analysis and cross-correlation technique to extend current understanding and of the radiation-belt dynamics. We found that the fluctuation of relativistic electron fluxes dependent basically on prolonged southward interplanetary magnetic field IMF-Bz. Cross-correlation analysis depicted that SYM-H does not show a strong connection either with relativistic electron enhancement events or persistent depletion events. Our result supports the fact that geomagnetic storms are not a primary factor that pumps up the radiation belt. In fact they seem event specific; either depletion or enhancement or slight effect on the outer radiation belt might be observed depending on the event. Solar wind pressure and velocity were found to be highly and positively correlated with relativistic electron. We found that, the count of relativistic electron flux (> 2 MeV) decreases during the main phase of geomagnetic storm with the increase in – from quiet to super intense storm – geomagnetic storm conditions (Table 1). However, Psw was found to be weakly correlated in case of intense storms following an abrupt increase of electron flux for ~ 4 hrs, which is interesting and unique.

scholarly journals Carrington Longitudinal and Solar Cycle Distribution of the Super Active Regions From 1976 to 2018

2021 ◽  
Author(s):  
MIng-Xian Zhao ◽  
Guiming Le ◽  
Yonghua Liu ◽  
Tian Mao
Keyword(s):  
Solar Cycle ◽  
Space Weather ◽  
Geomagnetic Storms ◽  
Solar Maximum ◽  
Active Regions ◽  
Ground Level ◽  
Weather Events ◽  
Flare Index ◽  
Statistical Results ◽  
Extreme Space Weather Events

Abstract We studied the Carrington longitudinal and solar cycle distribution of the super active regions (SARs) from 1976to 2018. There were 51 SARs during this period. We divided the SARs into SARs1 and SARs2. SARs1 refers tothe SARs that produced extreme space weather events including ≥X5.0 flares, ground level events (GLEs) andsuper geomagnetic storms (SGSs: Dst≤ −250 nT), while SARs2 did not produce extreme space weather events.The total number of SARs1 and SARs2 are 32 and 19, respectively. The statistical results show that 34.4%, 65.6%and 78.1% of the SARs1 appeared in the ascending phase, descending phase and in the period from two yearsbefore to the three years after the solar maximum, respectively, while 52.6%, 47.4% and 100% of the SARs2appeared in the ascending phase, descending phase and in the period from two years before to the three years aftersolar maximum, respectively. The Carrington longitude distribution of the SARs1 shows that SARs1 in thelongitudinal scope of [0,150°] produced ≥X5.0 flares and GLEs, while only the SARs1 in the longitude range of[150°,360°] not only produced ≥X5.0 flares and GLEs, but also produced SGSs. The total number of SARsduring a SC has a good correlation with the SC size. However, the largest flare index of a SAR within a SC has apoor correlation with the SC size, implying that the number of SARs in a weak SC will be small. However, aweak SC may have a SAR that can produce very strong solar flare activities.

A century of geomagnetic storms, CMEs and HSS/SIRs

2021 ◽  
Author(s):  
Kalevi Mursula ◽  
Timo Qvick ◽  
Lauri Holappa
Keyword(s):  
Solar Wind ◽  
High Speed ◽  
Geomagnetic Storms ◽  
Coronal Holes ◽  
Active Regions ◽  
Time Interval ◽  
Dst Index ◽  
Intensity Category ◽  
Interaction Regions ◽  
Storm Index

&lt;p&gt;Geomagnetic storms are mainly driven by the two main solar wind transients: coronal mass ejections (CME) and high-speed solar wind streams with related (corotating) stream interaction regions (HSS/SIR). CMEs are produced by new magnetic flux emerging on solar surface as active regions, and their occurrence follows the occurrence of sunspots quite closely. HSSs are produced by coronal holes, whose occurrence at the ecliptic is maximized in the declining phase of the solar cycle.&lt;/p&gt;&lt;p&gt;Geomagnetic storms are defined and quantified by the Dst index that measures the intensity of the ring current and is available since 1957. We have corrected some early errors in the Dst index and extended its time interval from 1932 onwards using the same stations as the Dst index (CTO preceding HER). This extended storm index is called the Dxt index. We have also constructed Dxt3 and Dxt2 indices from three/two of the longest-operating Dst stations to extend the storm index back to 1903, covering more than a century of storms.&lt;/p&gt;&lt;p&gt;We divide the storms into four intensity categories (weak, moderate, intense and major), and use the classification of solar wind by Richardson et al. into CME, HSS/SIR and slow wind -related flows in order to study the drivers of storms of each intensity category since 1964. We also correct and use the list of sudden storm commencements (SSC) collected by Father P. Mayaud, and divide the storms of each category into SSC-related storms and non-SSC storms.&lt;/p&gt;&lt;p&gt;Studying geomagnetic storms of different intensity category and SSC relation allows us to study the occurrence of CMEs and HSS/SIR over the last century. We also use these results to derive new information on the centennial evolution of the structure of solar magnetic fields.&lt;/p&gt;

scholarly journals Variability of ionospheric TEC during solar and geomagnetic minima (2008 and 2009): external high speed stream drivers

2013 ◽  
Vol 31 (2) ◽  
pp. 263-276 ◽  
Author(s):  
O. P. Verkhoglyadova ◽  
B. T. Tsurutani ◽  
A. J. Mannucci ◽  
M. G. Mlynczak ◽  
L. A. Hunt ◽  
...  
Keyword(s):  
Solar Wind ◽  
Solar Cycle ◽  
High Speed ◽  
Geomagnetic Storms ◽  
Solar Wind Speed ◽  
Radiation Balance ◽  
Electron Content ◽  
Low Latitude ◽  
Emission Data ◽  
Total Electron

Abstract. We study solar wind–ionosphere coupling through the late declining phase/solar minimum and geomagnetic minimum phases during the last solar cycle (SC23) – 2008 and 2009. This interval was characterized by sequences of high-speed solar wind streams (HSSs). The concomitant geomagnetic response was moderate geomagnetic storms and high-intensity, long-duration continuous auroral activity (HILDCAA) events. The JPL Global Ionospheric Map (GIM) software and the GPS total electron content (TEC) database were used to calculate the vertical TEC (VTEC) and estimate daily averaged values in separate latitude and local time ranges. Our results show distinct low- and mid-latitude VTEC responses to HSSs during this interval, with the low-latitude daytime daily averaged values increasing by up to 33 TECU (annual average of ~20 TECU) near local noon (12:00 to 14:00 LT) in 2008. In 2009 during the minimum geomagnetic activity (MGA) interval, the response to HSSs was a maximum of ~30 TECU increases with a slightly lower average value than in 2008. There was a weak nighttime ionospheric response to the HSSs. A well-studied solar cycle declining phase interval, 10–22 October 2003, was analyzed for comparative purposes, with daytime low-latitude VTEC peak values of up to ~58 TECU (event average of ~55 TECU). The ionospheric VTEC changes during 2008–2009 were similar but ~60% less intense on average. There is an evidence of correlations of filtered daily averaged VTEC data with Ap index and solar wind speed. We use the infrared NO and CO2 emission data obtained with SABER on TIMED as a proxy for the radiation balance of the thermosphere. It is shown that infrared emissions increase during HSS events possibly due to increased energy input into the auroral region associated with HILDCAAs. The 2008–2009 HSS intervals were ~85% less intense than the 2003 early declining phase event, with annual averages of daily infrared NO emission power of ~ 3.3 × 1010 W and 2.7 × 1010 W in 2008 and 2009, respectively. The roles of disturbance dynamos caused by high-latitude winds (due to particle precipitation and Joule heating in the auroral zones) and of prompt penetrating electric fields (PPEFs) in the solar wind–ionosphere coupling during these intervals are discussed. A correlation between geoeffective interplanetary electric field components and HSS intervals is shown. Both PPEF and disturbance dynamo mechanisms could play important roles in solar wind–ionosphere coupling during prolonged (up to days) external driving within HILDCAA intervals.

The Hemispheric Sign Rule of Current Helicity during the Rising Phase of Cycle 23

2000 ◽  
Vol 179 ◽  
pp. 303-306
Author(s):  
S. D. Bao ◽  
G. X. Ai ◽  
H. Q. Zhang
Keyword(s):  
Solar Cycle ◽  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Active Regions ◽  
Hemispheric Preference ◽  
Current Helicity

AbstractWe compute the signs of two different current helicity parameters (i.e., αbestandHc) for 87 active regions during the rise of cycle 23. The results indicate that 59% of the active regions in the northern hemisphere have negative αbestand 65% in the southern hemisphere have positive. This is consistent with that of the cycle 22. However, the helicity parameterHcshows a weaker opposite hemispheric preference in the new solar cycle. Possible reasons are discussed.

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