Predicting storm-time thermospheric mass density variations at CHAMP and GRACE altitudes

Abstract. Orbit-averaged mass density measurements derived from the satellites CHAMP and GRACE are used to investigate the storm-time prediction model developed by Liu et al. (2010) at different altitudes. This model uses as input only the solar wind merging electric field. From 2002 to 2005 in total 31 major geomagnetic storms with minimum Dst

Download Full-text

Magnetospheric response to solar wind driving

10.5194/egusphere-egu21-8951 ◽

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

<p>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.</p><p>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.</p>

Download Full-text

Thermospheric mass density variations during geomagnetic storms and a prediction model based on the merging electric field

Annales Geophysicae ◽

10.5194/angeo-28-1633-2010 ◽

2010 ◽

Vol 28 (9) ◽

pp. 1633-1645 ◽

Cited By ~ 22

Author(s):

R. Liu ◽

H. Lühr ◽

E. Doornbos ◽

S.-Y. Ma

Keyword(s):

Electric Field ◽

Geomagnetic Storms ◽

Mass Density ◽

Simple Relation ◽

Magnetic Storms ◽

Activity Levels ◽

Accelerometer Data ◽

Storm Events ◽

Controlling Parameter ◽

Wind Input

Abstract. With the help of four years (2002–2005) of CHAMP accelerometer data we have investigated the dependence of low and mid latitude thermospheric density on the merging electric field, Em, during major magnetic storms. Altogether 30 intensive storm events (Dstmin<−100 nT) are chosen for a statistical study. In order to achieve a good correlation Em is preconditioned. Contrary to general opinion, Em has to be applied without saturation effect in order to obtain good results for magnetic storms of all activity levels. The memory effect of the thermosphere is accounted for by a weighted integration of Em over the past 3 h. In addition, a lag time of the mass density response to solar wind input of 0 to 4.5 h depending on latitude and local time is considered. A linear model using the preconditioned Em as main controlling parameter for predicting mass density changes during magnetic storms is developed: ρ=0.5 Em + ρamb, where ρamb is based on the mean density during the quiet day before the storm. We show that this simple relation predicts all storm-induced mass density variations at CHAMP altitude fairly well especially if orbital averages are considered.

Download Full-text

Midday reversal of equatorial ionospheric electric field

Annales Geophysicae ◽

10.1007/s00585-997-1309-2 ◽

1997 ◽

Vol 15 (10) ◽

pp. 1309-1315 ◽

Cited By ~ 16

Author(s):

R. G. Rastogi

Keyword(s):

Magnetic Field ◽

Solar Wind ◽

Electric Field ◽

Electric Fields ◽

Ring Current ◽

Geomagnetic Storms ◽

Low Latitude ◽

E Region ◽

Sporadic E ◽

Ionospheric Electric Field

Abstract. A comparative study of the geomagnetic and ionospheric data at equatorial and low-latitude stations in India over the 20 year period 1956–1975 is described. The reversal of the electric field in the ionosphere over the magnetic equator during the midday hours indicated by the disappearance of the equatorial sporadic E region echoes on the ionograms is a rare phenomenon occurring on about 1% of time. Most of these events are associated with geomagnetically active periods. By comparing the simultaneous geomagnetic H field at Kodaikanal and at Alibag during the geomagnetic storms it is shown that ring current decreases are observed at both stations. However, an additional westward electric field is superimposed in the ionosphere during the main phase of the storm which can be strong enough to temporarily reverse the normally eastward electric field in the dayside ionosphere. It is suggested that these electric fields associated with the V×Bz electric fields originate at the magnetopause due to the interaction of the solar wind and the interplanetary magnetic field.

Download Full-text

Forecasting of SYMH and ASYH indices for geomagnetic storms of solar cycle 24 including St. Patrick’s day, 2015 storm using NARX neural network

Journal of Space Weather and Space Climate ◽

10.1051/swsc/2019007 ◽

2019 ◽

Vol 9 ◽

pp. A12

Author(s):

Ankush Bhaskar ◽

Geeta Vichare

Keyword(s):

Neural Network ◽

Solar Wind ◽

Solar Cycle ◽

Prediction Model ◽

Ring Current ◽

Geomagnetic Storms ◽

Time Profile ◽

Asymmetric Component ◽

Solar Cycle 24 ◽

Cycle 24

Artificial Neural Network (ANN) has proven to be very successful in forecasting a variety of irregular magnetospheric/ionospheric processes like geomagnetic storms and substorms. SYMH and ASYH indices represent longitudinal symmetric and the asymmetric component of the ring current. Here, an attempt is made to develop a prediction model for these indices using ANN. The ring current state depends on its past conditions therefore, it is necessary to consider its history for prediction. To account for this effect Nonlinear Autoregressive Network with exogenous inputs (NARX) is implemented. This network considers input history of 30 min and output feedback of 120 min. Solar wind parameters mainly velocity, density, and interplanetary magnetic field are used as inputs. SYMH and ASYH indices during geomagnetic storms of 1998–2013, having minimum SYMH < −85 nT are used as the target for training two independent networks. We present the prediction of SYMH and ASYH indices during nine geomagnetic storms of solar cycle 24 including the recent largest storm occurred on St. Patrick’s day, 2015. The present prediction model reproduces the entire time profile of SYMH and ASYH indices along with small variations of ∼10–30 min to the good extent within noise level, indicating a significant contribution of interplanetary sources and past state of the magnetosphere. Therefore, the developed networks can predict SYMH and ASYH indices about an hour before, provided, real-time upstream solar wind data are available. However, during the main phase of major storms, residuals (observed-modeled) are found to be large, suggesting the influence of internal factors such as magnetospheric processes.

Download Full-text