scholarly journals Echo state network model for analyzing solar-wind effects on the AU and AL indices

2022 ◽  
Vol 40 (1) ◽  
pp. 11-22
Author(s):  
Shin'ya Nakano ◽  
Ryuho Kataoka
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Wind Speed ◽  
Solar Wind Speed ◽  
Synthetic Data ◽  
Nonlinear Effects ◽  
Echo State Network ◽  
Solar Wind Density ◽  
Machine Learning Model ◽  
Solar Wind Parameters

Abstract. The properties of the auroral electrojets are examined on the basis of a trained machine-learning model. The relationships between solar-wind parameters and the AU and AL indices are modeled with an echo state network (ESN), a kind of recurrent neural network. We can consider this trained ESN model to represent nonlinear effects of the solar-wind inputs on the auroral electrojets. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The analyses of various synthetic data show that the AU and AL indices are mainly controlled by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF) as suggested by the literature. The results also indicate that the solar-wind density effect is emphasized when solar-wind speed is high and when IMF Bz is near zero. This suggests some nonlinear effects of the solar-wind density.

scholarly journals Virtual sounding of solar-wind effects on the AU and AL indices based on an echo state network model

2021 ◽  
Author(s):  
Shin'ya Nakano ◽  
Ryuho Kataoka
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Wind Speed ◽  
Solar Wind Speed ◽  
Synthetic Data ◽  
Nonlinear Effects ◽  
Echo State Network ◽  
Solar Wind Density ◽  
Machine Learning Model ◽  
Solar Wind Parameters

Abstract. The properties of the auroral electrojets are examined on the basis of a trained machine learning model. The relationships between solar-wind parameters and the AU and AL indices are modeled with an echo state network (ESN), a kind of recurrent neural network. We can consider this trained ESN model to represent nonlinear effects of the solar-wind inputs on the auroral electrojets. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The analyses of various synthetic data show that the AU and AL indices are mainly controlled by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF) as suggested by the literature. The results also indicate that the solar-wind density effect is emphasized when solar-wind speed is high and when IMF Bz is near zero. This suggests some nonlinear effects of the solar-wind density.

scholarly journals Several special conditions in the solar wind fora supersubstorm appearance.

2020 ◽  
Author(s):  
I.V. Despirak ◽  
◽  
A.A. Lubchich ◽  
N.G. Kleimenova ◽  
◽  
...  
Keyword(s):  
Solar Wind ◽  
Wind Speed ◽  
Data Base ◽  
Space Weather ◽  
Large Scale ◽  
Dynamic Pressure ◽  
Solar Wind Speed ◽  
Weather Conditions ◽  
The Other ◽  
Solar Wind Parameters

Analysis of the space weather conditions associated with supersubstorms (SSS) was carried out. Two magnetic storms, on 11 April and on 18 April 2001 have been studied and compared. During the first storm, there were registered twoevents of the supersubstorms with intensity of the SML index ~2000-3000 nT, whereas during the second storm there were observed two intense substorms with SML ~ 1500 nT. Solar wind conditions before appearance of the SSSs and intense substorms were compared. For this purpose, the OMNI data base, the catalog of large-scale solar wind phenomena and the data from the magnetic ground-based stations of the SuperMAG network (http://supermag.jhuapl.edu/) were combined. It was shown that the onsets of the SSS event were preceded by strong jumps in the dynamic pressure and density of the solar wind, which were observed against the background of the high solar wind speed and high values of the southern ВZcomponent of the IMF. Comparison with the usual substorms showed thatsome solar wind parameters were higher before SSSs, then before usual substorms: the dynamic pressure, the speed and the magnitude of IMF. On the other hand, the PC index values was the same for these all substorms, that leads to the conclusion about the possible independence of SSS appearance on the level of solar energy penetrated to the magnetosphere.

scholarly journals Prediction of Solar Wind Speed at 1 AU Using an Artificial Neural Network

Space Weather ◽  
2018 ◽  
Vol 16 (9) ◽  
pp. 1227-1244 ◽  
Author(s):  
Yi Yang ◽  
Fang Shen ◽  
Zicai Yang ◽  
Xueshang Feng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Wind ◽  
Wind Speed ◽  
Solar Wind Speed ◽  
Artificial Neural

Solar wind propagation delay predictions between L1 and Earth based on machine learning

2021 ◽  
Author(s):  
Carsten Baumann ◽  
Aoife E. McCloskey
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
Wind Speed ◽  
Lead Time ◽  
Solar Wind Speed ◽  
Propagation Delay ◽  
Learning Model ◽  
Interplanetary Shocks ◽  
Feature Sets ◽  
Machine Learning Model

<p>GNSS positioning errors, spacecraft operations failures and power outages potentially originate from space weather in general and the solar wind interaction with the geomagnetic field in particular. Depending on the solar wind speed, information from L1 solar wind monitor spacecraft only give a lead time to take safety measures between 20 and 90 minutes.  This very short lead time requires end users to have the most reliable warnings when potential impacts will actually occur. In this study we present a machine learning algorithm that is suitable to predict the solar wind propagation delay between Lagrangian point L1 and the Earth.  This work introduces the proposed algorithm and investigates its operational applicability to a realtime scenario.</p><p>The propagation delay is measured from interplanetary shocks passing the Advanced Composition Explorer (ACE) first and their sudden commencements within the magnetosphere later, as recorded by ground-based magnetometers. Overall 380 interplanetary shocks with data ranging from 1998 to 2018 builds up the database that is used to train the machine learning model. We investigate two different feature sets. The training of one machine learning model DSCOVR real time solar wind (RTSW) like data which contains all three components of solar wind speed is used. For the other machine learning model ACE RTSW like data which only provide bulk solar wind speed will be used for training. Both feature sets also contain the position of the spacecrafts. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the different features have to be fed into the algorithms only and the evaluation of the propagation delay can be continuous.</p><p>Both machine learning models will be validated against a simple convective solar wind propagation delay model as it is also used in operational space weather centers. For this purpose time periods will be investigated where L1 spacecraft and Earth satellites just outside the magnetosphere probe the same features of the interplanetary magnetic field. This method allows a detailed validation of the solar wind propagation delay apart from the technique that relies on interplanetary shocks.</p>

scholarly journals Timing of the solar wind propagation delay between L1 and Earth based on machine learning

2021 ◽  
Author(s):  
Carsten Baumann ◽  
Aoife E. McCloskey
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
Wind Speed ◽  
Space Weather ◽  
Solar Wind Speed ◽  
Propagation Delay ◽  
Learning Approach ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Gb Model

Erroneous GNSS positioning, failures in spacecraft operations and power outages due to geomagnetically induced currents  are severe threats originating from space weather. Having knowledge of potential impacts on modern society in advance is key for many end-user applications. This covers not only the timing of severe geomagnetic storms but also predictions of substorm onsets at polar latitudes. In this study we aim at contributing to the timing problem of space weather impacts and propose a new method to predict the solar wind propagation delay between Lagrangian point L1 and the Earth based on machine learning, specifically decision tree models. The propagation delay is measured from the identification of interplanetary discontinuities detected by the Advanced Composition Explorer (ACE) and their subsequent sudden commencements in the magnetosphere recorded by ground-based magnetometers. A database of the propagation delay has been constructed on this principle including 380 interplanetary shocks with data ranging from 1998 to 2018. The feature set of the machine learning approach consists of six features, namely the three components of each the solar wind speed and position of ACE around L1. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The machine learning results are compared to physics-based models, i.e., the flat propagation delay and the more sophisticated method based on the normal vector of solar wind discontinuities (vector delay). After hyperparameter optimization, the trained gradient boosting (GB) model is the best machine learning model among the tested ones. The GB model achieves an RMSE of 4.5 min with respect to the measured solar wind propagation delay and also outperforms the physical flat and vector delay models by 50 % and 15 % respectively. To increase the confidence in the predictions of the trained GB model, we perform a operational validation, provide drop-column feature importance and analyse the feature impact on the model output with Shapley values. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the solar wind speed and spacecraft position from only one datapoint have to be fed into the algorithm for a good prediction.

scholarly journals A solar-wind-driven empirical model of Pc3 wave activity at a mid-latitude location

2015 ◽  
Vol 33 (2) ◽  
pp. 225-234 ◽  
Author(s):  
S. Lotz ◽  
B. Heilig ◽  
P. Sutcliffe
Keyword(s):  
Solar Wind ◽  
Wind Speed ◽  
Solar Wind Speed ◽  
Cone Angle ◽  
Wave Activity ◽  
Proton Density ◽  
Propagation Time ◽  
Field Orientation ◽  
Data Set ◽  
Solar Wind Parameters

Abstract. In this paper we describe the development of two empirical models of Pc3 wave activity observed at a ground station. The models are tasked to predict pulsation intensity at Tihany, Hungary, from the OMNI solar wind data set at 5 min time resolution. One model is based on artificial neural networks and the other on multiple linear regression. Input parameters to the models are iteratively selected from a larger set of candidate inputs. The optimal set of inputs are solar wind speed, interplanetary magnetic field orientation (via cone angle), proton density and solar zenith angle (representing local time). Solar wind measurements are shifted in time with respect to Pc3 data to account for the propagation time of ULF perturbations from upstream of the bow shock. Both models achieve correlation of about 70% between measured and predicted Pc3 wave intensity. The timescales at which the most important solar wind parameters influence pulsation intensity are calculated for the first time. We show that solar wind speed influences pulsation intensity at much longer timescales (about 2 days) than cone angle (about 1 h).

scholarly journals Extremely low geomagnetic activity during the recent deep solar cycle minimum

2011 ◽  
Vol 7 (S286) ◽  
pp. 200-209 ◽  
Author(s):  
E. Echer ◽  
B. T. Tsurutani ◽  
W. D. Gonzalez
Keyword(s):  
Solar Wind ◽  
Solar Cycle ◽  
Geomagnetic Activity ◽  
Solar Minimum ◽  
Solar Wind Speed ◽  
Sunspot Cycle ◽  
Cycle Minimum ◽  
Current Minimum ◽  
Solar Wind Parameters ◽  
Xix Century

AbstractThe recent solar minimum (2008-2009) was extreme in several aspects: the sunspot number, Rz, interplanetary magnetic field (IMF) magnitude Bo and solar wind speed Vsw were the lowest during the space era. Furthermore, the variance of the IMF southward Bz component was low. As a consequence of these exceedingly low solar wind parameters, there was a minimum in the energy transfer from solar wind to the magnetosphere, and the geomagnetic activity ap index reached extremely low levels. The minimum in geomagnetic activity was delayed in relation to sunspot cycle minimum. We compare the solar wind and geomagnetic activity observed in this recent minimum with previous solar cycle values during the space era (1964-2010). Moreover, the geomagnetic activity conditions during the current minimum are compared with long term variability during the period of available geomagnetic observations. The extremely low geomagnetic activity observed in this solar minimum was previously recorded only at the end of XIX century and at the beginning of the XX century, and this might be related to the Gleissberg (80-100 years) solar cycle.

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