Relationship between geomagnetic storm development and the solar wind parameter ß

We have analyzed the dynamics of solar wind and interplanetary magnetic field (IMF) parameters during the development of 933 isolated geomagnetic storms, observed over the period from 1964 to 2010. The analysis was carried out using the epoch superposition method at intervals of 48 hrs before and 168 hrs after the moment of Dst minimum. The geomagnetic storms were selected by the type of storm commencement (sudden or gradual) and by intensity (weak, moderate, and strong). The dynamics of the solar wind and IMF parameters was compared with that of the Dst index, which is an indicator of the development of geomagnetic storms. The largest number of storms in the solar activity cycle is shown to occur in the years of minimum average values (close in magnitude to 1) of the solar wind parameter β (β is the ratio of plasma pressure to magnetic pressure). We have revealed that the dynamics of the Dst index is similar to that of the β parameter. The duration of the storm recovery phase follows the characteristic recovery time of the β parameter. We have found out that during the storm main phase the β parameter is close to 1, which reflects the maximum turbulence of solar wind plasma fluctuations. In the recovery phase, β returns to background values β~2‒3.5. We assume that the solar wind plasma turbulence, characterized by the β parameter, can play a significant role in the development of geomagnetic storms.

Relationship between geomagnetic storm development and the solar wind parameter β

We have analyzed the dynamics of solar wind and interplanetary magnetic field (IMF) parameters during the development of 933 isolated geomagnetic storms, observed over the period from 1964 to 2010. The analysis was carried out using the epoch superposition method at intervals of 48 hrs before and 168 hrs after the moment of Dst minimum. The geomagnetic storms were selected by the type of storm commencement (sudden or gradual) and by intensity (weak, moderate, and strong). The dynamics of the solar wind and IMF parameters was compared with that of the Dst index, which is an indicator of the development of geomagnetic storms. The largest number of storms in the solar activity cycle is shown to occur in the years of minimum average values (close in magnitude to 1) of the solar wind parameter β (β is the ratio of plasma pressure to magnetic pressure). We have revealed that the dynamics of the Dst index is similar to that of the β parameter. The duration of the storm recovery phase follows the characteristic recovery time of the β parameter. We have found out that during the storm main phase the β parameter is close to 1, which reflects the maximum turbulence of solar wind plasma fluctuations. In the recovery phase, β returns to background values β~2‒3.5. We assume that the solar wind plasma turbulence, characterized by the β parameter, can play a significant role in the development of geomagnetic storms.

Geomagnetic Storm Related to Disturbance Storm Time Indices

The magnitude of the Disturbance Storm Time (Dst) index varied in relation to the extremely small negative integer that indicated a large geomagnetic storm. The large sharpened variants of negative Dst indices could describe the detailed features of a geomagnetic storm. the Dst index was estimated using an algorithm through time and frequency-domain band-stop filtering to remove the solar-quiet variation and the mutual coupling effects between the Earth’s rotation, the Moon’s orbit, and the Earth’s orbit around the Sun. A good geomagnetic model that could describe the true variations in the geomagnetic field when undergoing diverse space weather, and one that could even predict variations in the geomagnetic field with a high accuracy. A suitable temporal resolution for the Dst index was per hour.

Real-time Magnetic Observatory Network: A Review

The International Real-time Magnetic Observatory Network (INTERMAGNET) was based on the Observatory Instruments in Ottawa, Canada in August 1986. After coordination between the United States and British Geological Surveys, this network could use to record Earth’s magnetic field e.g., Disturbance storm time (Dst) index that monitored a large geomagnetic storm. The INTERMAGNET has been used in to access the observed communicating. The production of geomagnetic products could be obtained in real-time. Overseeing the operations of INTERMAGNET, the first geomagnetic Information Node (GIN) was established in 1991, the first CD-ROM/DVD was published in 1991.

Magnetic Field Measurements by the Satellite Systems: A Review

Recently, in real-time the Disturbance storm time (Dst) indices observing by Geostationary Operational Environmental Satellite (GOES) was performable using so-called Goes-Magnetometer. Dst index is a geomagnetic index, which is the L1 data with the lead time, to detect geomagnetic storms with the lead time. Geomagnetic storms affected human activity and caused economic losses. Therefore, Dst index is a very important index. The past recorded contributions of corresponding Satellites were introduced. Now, in real-time Dst indices observing by Geostationary Operational Environmental Satellite (GOES-16) (Goes-Magnetometer) was performed. However, the Dst index was not the issue in this study.

Operational Dst index prediction model based on combination of artificial neural network and empirical model

<p class="Abstract" style="margin: 6pt 0cm 0.0001pt; font-size: 12pt; font-family: 굴림, sans-serif; color: rgb(0, 0, 0); text-align: justify; text-indent: 36pt;"><span lang="EN-US" style="font-family: &quot;Times New Roman&quot;, serif;">In this paper, an operational Dst index prediction model is developed by combining empirical and artificial neural network models. Artificial neural network algorithms are widely used to predict space weather conditions. While they require a large amount of data for machine learning, large-scale geomagnetic storms have not occurred sufficiently for the last 20 years, ACE and DSCOVR mission operation period. Conversely, the empirical models are based on numerical equations derived from human intuition and are therefore applicable to extrapolate for large storms. In this study, we distinguish between Coronal Mass Ejection (CME) driven and Corotating Interaction Region (CIR) driven storms, estimate the minimum Dst values, and derive an equation for describing the recovery phase. The combined Korea Astronomy and Space Science Institute (KASI) Dst Prediction (KDP) model achieved better performance contrasted to Artificial Neural Network (ANN) model only. This model could be used practically for space weather operation by extending prediction time to 24 hours and updating the model output every hour.<o:p></o:p></span></p>

Exploring Three Recurrent Neural Network Architectures for Geomagnetic Predictions

Three different recurrent neural network (RNN) architectures are studied for the prediction of geomagnetic activity. The RNNs studied are the Elman, gated recurrent unit (GRU), and long short-term memory (LSTM). The RNNs take solar wind data as inputs to predict the Dst index. The Dst index summarizes complex geomagnetic processes into a single time series. The models are trained and tested using five-fold cross-validation based on the hourly resolution OMNI dataset using data from the years 1995–2015. The inputs are solar wind plasma (particle density and speed), vector magnetic fields, time of year, and time of day. The RNNs are regularized using early stopping and dropout. We find that both the gated recurrent unit and long short-term memory models perform better than the Elman model; however, we see no significant difference in performance between GRU and LSTM. RNNs with dropout require more weights to reach the same validation error as networks without dropout. However, the gap between training error and validation error becomes smaller when dropout is applied, reducing over-fitting and improving generalization. Another advantage in using dropout is that it can be applied during prediction to provide confidence limits on the predictions. The confidence limits increase with increasing Dst magnitude: a consequence of the less populated input-target space for events with large Dst values, thereby increasing the uncertainty in the estimates. The best RNNs have test set RMSE of 8.8 nT, bias close to zero, and linear correlation of 0.90.

Analysis of the possibility of using Russian geomagnetic stations to calculate the analogue of the Dst-index

В работе анализируется возможность построения аналога Dst-индекса по данным с российских геомагнитных обсерваторий. Проводится сравнительный анализ значений Dst-индекса, вычисленного классическим способом (по данным с 4 приэкваториальных станций: Hermanus, Kakioka, Honolulu и San Juan), со значениями Dst-индекса, посчитанного по данным исключительно с Российских станций. The paper analyzes the possibility of constructing an analogue of the Dst index using data from Russian geomagnetic observatories. A comparative analysis of the values of the Dstindex calculated by the classical method (according to data from 4 near-equatorial stations: Hermanus, Kakioka, Honolulu and San Juan) with the values of the Dst-index calculated according to data exclusively from Russian stations is carried out.

A preliminary investigation of dynamical complexity in Swarm Dst-like time series using information theory techniques

&lt;p&gt;For 7 years now, the European Space Agency&amp;#8217;s Swarm fleet of satellites surveys the Earth&amp;#8217;s magnetic field, measuring magnetic and electric fields at low-Earth orbit (LEO) with unprecedented detail. We have recently demonstrated the feasibility of Swarm measurements to derive a Swarm Dst-like index for the intense magnetic storms of solar cycle 24. We have shown that the newly proposed Swarm Dst-like index monitors magnetic storm activity at least as good as the standard Dst index. The Swarm derived Dst index can be used to (1) supplement the standard Dst index in near-real-time geomagnetic applications and (2) replace the &amp;#8216;prompt&amp;#8217; Dst index during periods of unavailability. Herein, we employ a series of information theory methods, namely Hurst exponent and various entropy measures, for analyzing Swarm Dst-like time series. The results show that information theory techniques can effectively detect the dissimilarity of complexity between the pre-storm activity and intense magnetic storms (Dst &lt; 150 nT), which is convenient for space weather applications.&lt;/p&gt;

A century of geomagnetic storms, CMEs and HSS/SIRs

&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;