scholarly journals Data derived NARMAX Dst model

2011 ◽  
Vol 29 (6) ◽  
pp. 965-971 ◽  
Author(s):  
R. J. Boynton ◽  
M. A. Balikhin ◽  
S. A. Billings ◽  
A. S. Sharma ◽  
O. A. Amariutei
Keyword(s):  
Mathematical Model ◽  
Solar Wind ◽  
System Identification ◽  
Nonlinear Systems ◽  
Wide Class ◽  
Geomagnetic Storms ◽  
Output Data ◽  
Dst Index ◽  
Geomagnetic Indices ◽  
Input And Output

Abstract. The NARMAX OLS-ERR methodology is applied to identify a mathematical model for the dynamics of the Dst index. The NARMAX OLS-ERR algorithm, which is widely used in the field of system identification, is able to identify a mathematical model for a wide class of nonlinear systems using input and output data. Solar wind-magnetosphere coupling functions, derived from analytical or data based methods, are employed as the inputs to such models and the outputs are geomagnetic indices. The newly deduced coupling function, p1/2V4/3BTsin6(θ/2), has been implemented as an input to model the Dst dynamics. It was shown that the identified model has a very good forecasting ability, especially with the geomagnetic storms.

scholarly journals The geomagnetic cutoff rigidities at high latitudes for different solar wind and geomagnetic conditions

2016 ◽  
Vol 34 (1) ◽  
pp. 45-53 ◽  
Author(s):  
W. Chu ◽  
G. Qin
Keyword(s):  
Solar Wind ◽  
Cosmic Rays ◽  
Strong Correlation ◽  
Geomagnetic Storms ◽  
Dynamic Pressure ◽  
Threshold Value ◽  
High Latitudes ◽  
Dst Index ◽  
Ae Index ◽  
Geomagnetic Cutoff

Abstract. Studying the access of the cosmic rays (CRs) into the magnetosphere is important to understand the coupling between the magnetosphere and the solar wind. In this paper we numerically studied CRs' magnetospheric access with vertical geomagnetic cutoff rigidities using the method proposed by Smart and Shea (1999). By the study of CRs' vertical geomagnetic cutoff rigidities at high latitudes we obtain the CRs' window (CRW) whose boundary is determined when the vertical geomagnetic cutoff rigidities drop to a value lower than a threshold value. Furthermore, we studied the area of CRWs and found out they are sensitive to different parameters, such as the z component of interplanetary magnetic field (IMF), the solar wind dynamic pressure, AE index, and Dst index. It was found that both the AE index and Dst index have a strong correlation with the area of CRWs during strong geomagnetic storms. However, during the medium storms, only AE index has a strong correlation with the area of CRWs, while Dst index has a much weaker correlation with the area of CRWs. This result on the CRW can be used for forecasting the variation of the cosmic rays during the geomagnetic storms.

Dynamic System Identification Using a Step Input

2005 ◽  
Vol 24 (2) ◽  
pp. 125-134
Author(s):  
Manabu Kosaka ◽  
Hiroshi Uda ◽  
Eiichi Bamba ◽  
Hiroshi Shibata
Keyword(s):  
Steady State ◽  
System Identification ◽  
Transfer Function ◽  
Step Response ◽  
Output Data ◽  
Mass System ◽  
Input And Output ◽  
Rational Transfer ◽  
Line Identification ◽  
The Moment

In this paper, we propose a deterministic off-line identification method performed by using input and output data with a constant steady state output response such as a step response that causes noise or vibration from a mechanical system at the moment when it is applied but they are attenuated asymptotically. The method can directly acquire any order of reduced model without knowing the real order of a plant, in such a way that the intermediate parameters are uniquely determined so as to be orthogonal with respect to 0 ∼ N-tuple integral values of output error and irrelevant to the unmodelled dynamics. From the intermediate parameters, the coefficients of a rational transfer function are calculated. In consequence, the method can be executed for any plant without knowing or estimating its order at the beginning. The effectiveness of the method is illustrated by numerical simulations and also by applying it to a 2-mass system.

System identification models for waves in the inner magnetosphere

2020 ◽  
Author(s):  
Richard Boynton ◽  
Homayon Aryan ◽  
Walker Simon ◽  
Michael Balikhin
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
System Identification ◽  
Moving Average ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Time Lags ◽  
Inner Magnetosphere ◽  
Geomagnetic Indices ◽  
The Waves

<p>This research develops forecast models of the spatiotemporal evolution of emissions throughout the inner magnetosphere between L=2-6 and at all MLT. The system identification, or machine learning, technique based on Nonlinear AutoRegressive Moving Average eXogenous (NARMAX) models is employed to deduce the forecasting models of the lower band chorus, Hiss, and magnetosonic waves using solar wind and geomagnetic indices as inputs. It is difficult to develop machine leaning based spatiotemporal models of the waves in the inner magnetosphere as the data is sparse and machine learning techniques require large amounts of data to deduce a model. To solve this problem, the spatial co-ordinates at the time of the measurements are included as inputs to the model along with time lags of the solar wind and geomagnetic indices, while the measurement of the waves by the Van Allen Probes are used as the output to train the models. The estimates of the resultant models are compared with separate data to the training data to assess the performance of the models. The models are then used to reconstruct the spatiotemporal waves over the inner magnetosphere, as the waves respond to changes in the solar wind and geomagnetic indices.  </p>

scholarly journals Validation of Simulation Models without Knowledge of Parameters Using Differential Algebra

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Björn Haffke ◽  
Riccardo Möller ◽  
Tobias Melz ◽  
Jens Strackeljan
Keyword(s):  
System Identification ◽  
Numerical Methods ◽  
Nonlinear Systems ◽  
Differential Algebra ◽  
External Validation ◽  
Simulation Models ◽  
Input And Output ◽  
Iterative Numerical Methods

This study deals with the external validation of simulation models using methods from differential algebra. Without any system identification or iterative numerical methods, this approach provides evidence that the equations of a model can represent measured and simulated sets of data. This is very useful to check if a model is, in general, suitable. In addition, the application of this approach to verification of the similarity between the identifiable parameters of two models with different sets of input and output measurements is demonstrated. We present a discussion on how the method can be used to find parameter deviations between any two models. The advantage of this method is its applicability to nonlinear systems as well as its algorithmic nature, which makes it easy to automate.

State-space Model Identification Using Input and Output Data With Steady State Values Zeroing Multiple Integrals of Output Error

2005 ◽  
Vol 128 (3) ◽  
pp. 746-749
Author(s):  
Manabu Kosaka ◽  
Hiroshi Uda ◽  
Eiichi Bamba ◽  
Hiroshi Shibata
Keyword(s):  
Steady State ◽  
System Identification ◽  
State Space ◽  
State Space Model ◽  
Hankel Matrix ◽  
Output Data ◽  
Single Input Single Output ◽  
Space Model ◽  
Output Error ◽  
Input And Output

This study proposes a new deterministic off-line identification method that obtains a state-space model using input and output data with steady state values. This method comprises of two methods: Zeroing the 0∼N-tuple integral values of the output error of single-input single-output transfer function model (Kosaka et al., 2004) and Ho-Kalman’s method (Zeiger and McEwen, 1974). Herein, we present a new method to derive a matrix similar to the Hankel matrix using multi-input and multi-output data with steady state values. State space matrices A, B, C, and D are derived from the matrix by the method shown in Zeiger and McEwen, 1974 and Longman and Juang, 1989. This method’s utility is that the derived state-space model is emphasized in the low frequency range under certain conditions. Its salient feature is that this method can identify use of step responses; consequently, it is suitable for linear mechanical system identification in which noise and vibration are unacceptable. Numerical simulations of multi-input multi-output system identification are illustrated.

scholarly journals A statistical analysis of the relationship between Pc4 and Pc5 ULF waves, solar winds and geomagnetic storms for predicting earthquake precursor signatures in low latitude regions

2021 ◽  
Vol 880 (1) ◽  
pp. 012010
Author(s):  
S N A Syed Zafar ◽  
Roslan Umar ◽  
N H Sabri ◽  
M H Jusoh ◽  
A Yoshikawa ◽  
...  
Keyword(s):  
Solar Wind ◽  
Geomagnetic Field ◽  
Geomagnetic Storms ◽  
Earthquake Precursor ◽  
Ulf Waves ◽  
Low Latitude ◽  
Geomagnetic Indices ◽  
Solar Winds ◽  
Solar Wind Parameters ◽  
The Relationship

Abstract Short-term earthquake forecasting is impossible due to the seismometer’s limited sensitivity in detecting the generation of micro-fractures prior to an earthquake. Therefore, there is a strong desire for a non-seismological approach, and one of the most established methods is geomagnetic disturbance observation. Previous research shows that disturbances in the ground geomagnetic field serves as a potential precursor for earthquake studies. It was discovered that electromagnetic waves (EM) in the Ultra-Low Frequency (ULF) range are a promising tool for studying the seismomagnetic effect of earthquake precursors. This study used a multiple regression approach to analyse the preliminary study on the relationship between Pc4 (6.7-22 mHz) and Pc5 (1.7-6.7 mHz) ULF magnetic pulsations, solar wind parameters and geomagnetic indices for predicting earthquake precursor signatures in low latitude regions. The ground geomagnetic field was collected from Davao station (7.00° N, 125.40° E), in the Philippines, which experiences nearby earthquake events (Magnitude <5.0, depth <100 km and epicentre distance from magnetometer station <100 km). The Pc5 ULF waves show the highest variance with four solar wind parameters, namely SWS, SWP, IMF-Bz, SIE and geomagnetic indices (SYM/H) prior to an earthquake event based on the regression model value of R2 = 0.1510. Furthermore, the IMF-Bz, SWS, SWP, SWE, and SYM/H were found to be significantly correlated with Pc5 ULF geomagnetic pulsation. This Pc5 ULF magnetic pulsation behaviour in solar winds and geomagnetic storms establishes the possibility of using Pc5 to predict earthquakes.

scholarly journals Relationship between geomagnetic storm development and the solar wind parameter ß

2021 ◽  
Vol 7 (4) ◽  
pp. 24-32
Author(s):  
Nadezhda Kurazhkovskaya ◽  
Oleg Zotov ◽  
Boris Klain
Keyword(s):  
Solar Wind ◽  
Geomagnetic Storms ◽  
Activity Cycle ◽  
Solar Activity Cycle ◽  
Solar Wind Plasma ◽  
Recovery Phase ◽  
Plasma Pressure ◽  
Superposition Method ◽  
Solar Wind Parameter ◽  
Dst Index

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.

scholarly journals A Statistical Study on DH CMEs and Its Geoeffectiveness

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
V. Vasanth ◽  
S. Umapathy
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Wind Speed ◽  
Field Component ◽  
Geomagnetic Storms ◽  
Solar Wind Speed ◽  
Type Ii ◽  
Dst Index ◽  
Long Duration ◽  
Type Ii Bursts

A detailed investigation on geoeffectiveness of CMEs associated with DH-type-II bursts observed during 1997–2008 is presented. The collected sample events are divided into two groups based on their association with CMEs related to geomagnetic storms Dst ≤−50 nT, namely, (i) geoeffective events and (ii) nongeoeffective events. We found that the geoeffective events have high starting frequency, low ending frequency, long duration, wider bandwidth, energetic flares, and CMEs than nongeoeffective events. The geoeffective events are found to have intense geomagnetic storm with mean Dst index (−150 nT). There exists good correlation between the properties of CMEs and flares for geoeffective events, while no clear correlation exists for nongeoeffective events. There exists a weak correlation for geoeffective events between (i) CME speed and Dst index (R=-0.51) and good correlation between (i) CME speed and solar wind speed (R=0.60), (ii) Dst index and solar wind speed (R=-0.64), and (iii) Dst index and southward magnetic field component (Bz) (R=0.80). From our study we conclude that the intense and long duration southward magnetic field component (Bz) and fast solar wind speed are responsible for geomagnetic storms, and the geomagnetic storms weakly depend on CME speed. About 22% (50/230) of the DH-type-II bursts are associated with geomagnetic storms. Therefore the DH-type-II bursts associated with energetic flares and CMEs are good indicator of geomagnetic storms.

Magnetospheric response to solar wind driving

2021 ◽  
Author(s):  
Tatiana Výbošťoková ◽  
Zdeněk Němeček ◽  
Jana Šafránková
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Electric Field ◽  
Geomagnetic Storms ◽  
Interplanetary Space ◽  
Time Lags ◽  
Interplanetary Shocks ◽  
Geomagnetic Indices ◽  
The Earth ◽  
The Magnetic Field

&lt;p&gt;Interaction of solar events propagating throughout the interplanetary space with the magnetic field of the Earth may result in disruption of the magnetosphere. Disruption of the magnetic field is followed by the formation of the time-varying electric field and thus electric current is induced in Earth-bound structures such as transmission networks, pipelines or railways. In that case, it is necessary to be able to predict a future state of the magnetosphere and magnetic field of the Earth. The most straightforward way would use geomagnetic indices. Several studies are investigating the relationship of the response of the magnetosphere to changes in the solar wind with motivation to give a more accurate prediction of geomagnetic indices during geomagnetic storms. To forecast these indices, different approaches have been attempted--from simple correlation studies to neural networks.&lt;/p&gt;&lt;p&gt;We study the effects of interplanetary shocks observed at L1 on the Earth's magnetosphere with a database of tens of shocks between 2009 and 2019. Driving the magnetosphere is described as integral of reconnection electric field for each shock. The response of the geomagnetic field is described with the SYM-H index. We created an algorithm in Python for prediction of the magnetosphere state based on the correlation of solar wind driving and magnetospheric response and found that typical time-lags range between tens of minutes to maximum 2 hours. The results are documented by a large statistical study.&lt;/p&gt;

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