A cellular automata-based model of Earth's magnetosphere in relation with<i>Dst</i>index

Banerjee, Adrija; Bej, Amaresh; Chatterjee, Tanmoy

doi:10.1002/2014sw001138

Cited by 6 publications

(5 citation statements)

References 57 publications

(70 reference statements)

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“…Predictions of geomagnetic indices thus serve two purposes: to provide alerts and warnings of coming events and to serve as inputs to models. Many different approaches have been applied to predict indices from upstream solar wind beginning with the early work of Burton et al (1975) and many others, for example, some more recent: Ayala Solares et al (2016), Bala and Reiff (2012), Banerjee et al (2015), Billings (2013), and Boynton et al (2011).…”

Section: Introductionmentioning

confidence: 99%

Evaluation ofKpandDstPredictions Using ACE and DSCOVR Solar Wind Data

Wintoft

Wik

2018

Space Weather

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Models that predict the Kp and Dst indices are evaluated using solar wind data at L1. The models consist of ensembles of neural networks that have been developed using ACE Level 2 data from the period 1998–2015. The use of ensembles is motivated by the difficulty of generating functions that generalize well in regions of the input‐output space that are poorly sampled, which typically occurs during stronger events. Since the launch of the DSCOVR spacecraft, providing measurements from about August 2016, new and independent data have become available to test the models. ACE Level 2 data for almost the same period are also available, representing another independent set collected after 2015. We evaluate the models using plots and various statistical measures. We also study the performance of the predictions for lead times up to 3 hr. The results show that the models perform better when using ACE or cleaned DSCOVR data compared to the real‐time DSCOVR data and that lead times of L1‐Earth travel time plus a maximum of 1 hr are possible. As the models use only solar wind for their inputs, and the temporal dynamics of Kp and Dst are very different, we see significant differences in the error distributions that we believe are related to long‐term changes in Dst that are not captured by the Dst prediction model.

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Section: Introductionmentioning

confidence: 99%

Evaluation ofKpandDstPredictions Using ACE and DSCOVR Solar Wind Data

Wintoft

Wik

2018

Space Weather

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“…In the present paper, we continued our analysis based on the sandpile‐like cellular automata model of the terrestrial magnetosphere, presented in our 2015 paper (Banerjee et al, ). We proposed a definite solar wind‐magnetosphere energy coupling function in terms of IMF B Z and a threshold value, B Th .…”

Section: Introductionmentioning

confidence: 80%

“…The cellular automata-based sandpile model presented here is a refinement of the model proposed in our previous paper (Banerjee et al, 2015). In summary, the model, a numerical representation of the Earth's magnetosphere, is a finite matrix of n × n elements, characterized by energy E, which is a function of time and space.…”

Section: Methodsmentioning

confidence: 99%

“…In our previous work, we came to realize that geomagnetic disturbances on the Earth is an essentially complex natural phenomenon having a number of underlying dynamics and quite impossible to predict accurately, whereas Dst index, the measurement of geomagnetic activity, is a positively correlated fractional Brownian motion having long‐range correlation (Banerjee et al, ). Enlightened by this outcome, we aimed to develop a sandpile‐like cellular automata model, analogous to the Earth's magnetosphere to investigate the dynamical characteristics of the geomagnetic storm (Banerjee et al, ). Now sandpile model was selected as the first example displaying the concept of self‐organized criticality, introduced by Bak et al in their , pioneering papers.…”

Section: Introductionmentioning

confidence: 99%

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An SOC Approach to Study the Solar Wind‐Magnetosphere Energy Coupling

Banerjee

Bej

Chatterjee³

et al. 2019

Earth and Space Science

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Solar wind‐magnetosphere interaction and the injection of large quantity of plasma particles into the Earth's magnetosphere are the primary reasons behind geomagnetic storm, auroral effects, and, in general, all the fluctuations observed in the terrestrial magnetic field. In this paper, we analyzed the perturbed magnetosphere as a sandpile‐like cellular automata model based on the concept of self‐organized criticality and many‐body interactive system and proposed a solar wind‐magnetosphere energy coupling function in terms of interplanetary magnetic field BZ, the zth component of interplanetary magnetic field. The function determines the cusp width W depending on the intensity of (−BZ − BTh) where BTh is the threshold value. The model generates two output series, which are the numerical representation of the real‐time Dst index and AE index series, respectively. For our study, the range of years 1997–2007 of the 23rd solar cycle had been considered here. The threshold value BTh plays a significant role in the analysis and exhibits a proportional relationship with the yearly mean total number of sunspots for each year of the range 1997–2007 of the 23rd solar cycle. For each year, the two resultant output time series of the model display high‐correlation coefficients with the real‐time Dst and AE indexes, respectively, which denotes the acceptability of the proposed energy coupling function and its relation with the solar activities.

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“…Tobiska et al [10] proposed a data-driven deterministic algorithm, the Anemomilos algorithm, which can predict the Dst up to 6 days based on the arrival time of large, medium, or small magnetic storms to the Earth. Banerjee et al [11] used the direction and magnitude of the B Z component of the real-time solar wind and real-time interplanetary magnetic field (IMF) as input parameters to model the magnetosphere based on a metric automaton, and the simulation of the Dst met expectations. Chandorkar et al [12] developed Gaussian Process Autoregressive (GP-AR) and Gaussian Process Autoregressive with external inputs (GP-ARX) models, whose Root Mean Square Errors (RMSEs) were only 14.04 nT and 11.88 nT and CCs (correlation coefficients) were 0.963 and 0.972, respectively.…”

Section: Related Studiesmentioning

confidence: 99%

The Short Time Prediction of the Dst Index Based on the Long-Short Time Memory and Empirical Mode Decomposition–Long-Short Time Memory Models

Zhang,

Feng,

Zhang

et al. 2023

Applied Sciences

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The Dst index is the geomagnetic storm index used to measure the energy level of geomagnetic storms, and the prediction of this index is of great significance for geomagnetic storm studies and solar activities. In contrast to traditional numerical modeling techniques, machine learning, which emerged decades ago based on rapidly developing computer hardware and software and artificial intelligence methods, has been unprecedentedly developed in geophysics, especially solar–terrestrial space physics. This study uses two machine learning models, the LSTM (Long-Short Time Memory, LSTM) and EMD-LSTM models (Empirical Mode Decomposition, EMD), to model and predict the Dst index. By building the Dst index data series from 2018 to 2023, two models were built to fit and predict the data. Firstly, we evaluated the influences of the learning rate and the amount of training data on the prediction accuracy of the LSTM model, and finally, 10−3 was thought to be the optimal learning rate. Secondly, the two models were used to predict the Dst index in the solar active and quiet periods, respectively, and the RMSE (Root Mean Square Error) of the LSTM model in the active period was 7.34 nT and the CC (correlation coefficient) was 0.96, and those of the quiet period were 2.64 nT and 0.97. The RMSE and CC of the EMD-LSTM model were 8.87 nT and 0.93 in the active period and 3.29 nT and 0.95 in the quiet period. Finally, the prediction accuracy of the LSTM model in the short time period was slightly better than the EMD-LSTM model. However, there will be a problem of prediction lag, which the EMD-LSTM model can solve and better predict the geomagnetic storm.

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A cellular automata-based model of Earth's magnetosphere in relation withDstindex

Cited by 6 publications

References 57 publications

Evaluation ofKpandDstPredictions Using ACE and DSCOVR Solar Wind Data

Evaluation ofKpandDstPredictions Using ACE and DSCOVR Solar Wind Data

An SOC Approach to Study the Solar Wind‐Magnetosphere Energy Coupling

The Short Time Prediction of the Dst Index Based on the Long-Short Time Memory and Empirical Mode Decomposition–Long-Short Time Memory Models

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