Study of<i>Dst</i>/ring current recovery times using the WINDMI model

Patra, S.; Spencer, E. A.; Horton, W.; Sojka, J. J.

doi:10.1029/2010ja015824

Cited by 20 publications

(26 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quiet time values for each current system is included in the WINDMI model calculations. Following Patra et al [2011] we have lumped together the effects of the region one and two currents, I 1 , I 2 , and the geotail current and proceed to use αI of the geotail current to represent both. The contributions from the magnetopause and tail current systems are given by:

D s t_{italicmp} = a * \sqrt{P_{dyn}}

D s t_{t} = α I (t)

where Dst mp is the perturbation due to the magnetopause currents and Dst t is the magnetic field contribution from the tail current I ( t ) which is modeled by WINDMI as I .…”

Section: Description Of the Windmi Modelmentioning

confidence: 99%

“…We used the value of a = 15.5 as suggested by Burton et al [1975]. For a look at the results obtained by using the value ( a = 7.25 ), as estimated by O'Brien and McPherron [2000], see Patra et al [2011]. P dyn is the dynamic pressure exerted by the solar wind on the Earth's magnetopause.…”

Section: Description Of the Windmi Modelmentioning

confidence: 99%

“…The 6 events chosen for this study from the previous group of 13 are (1) Days 158–166, 2000, (2) Days 258–266, 2000, (3) Days 225–235, 2001, (4) Days 325–335, 2001, (5) Days 80–88, 2002, and (6) Days 245–260, 2002. In addition to the 6 out of 13 events from Patra et al [2011], we also selected the October 2000 and April 2002 storm events used previously by Spencer et al [2009], since now the inclusion of the tail current contribution and the Dmp contribution adds to the interpretation of the calculated Dst .…”

Section: Storm Eventsmentioning

confidence: 99%

See 2 more Smart Citations

Influence of solar wind-magnetosphere coupling functions on theDstindex

et al. 2011

Self Cite

View full text Add to dashboard Cite

[1] In this paper we investigate the role of different solar wind magnetosphere coupling functions on the Dst index calculated by the low-order nonlinear dynamical WINDMI model. In our previous work we have shown that the geotail current dynamics has a significant role in the two-phase decay of the Dst index. During that investigation we used the rectified solar wind electric field v x B z as a baseline for the simulations and analysis. Here we include an evaluation of four other coupling functions in addition to the rectified vB s . These coupling functions emphasize different physical mechanisms to explain the energy transfer into the magnetosphere due to solar wind velocity, dynamic pressure, magnetic field, and Mach number. One coupling function is due to Siscoe, another by Borovsky, and two by Newell. Our results indicate that for a majority of cases, at most only v x , B y , and B z are needed to sufficiently account for the supply of energy to the ring current and geotail current components that contribute to the Dst index. The more complex coupling functions sometimes perform extremely well on storm data sets but at other times do not reproduce the Dst index faithfully. The AL index was used as an additional constraint on the allowable geotail current dynamics and to further differentiate between coupling functions when the Dst performance was similar. The solar wind dynamic pressure contribution appears to be correctly accounted for through the calculation of the Dmp formula of Burton et al. (1975). The degree to which the B y component affects the Dst index is not entirely clear from our results, but in most cases its inclusion slightly overemphasizes the ring current contribution and slightly underemphasizes the geotail current contribution.

show abstract

D s t_{italicmp} = a * \sqrt{P_{dyn}}

D s t_{t} = α I (t)

where Dst mp is the perturbation due to the magnetopause currents and Dst t is the magnetic field contribution from the tail current I ( t ) which is modeled by WINDMI as I .…”

Section: Description Of the Windmi Modelmentioning

confidence: 99%

Section: Description Of the Windmi Modelmentioning

confidence: 99%

Section: Storm Eventsmentioning

confidence: 99%

See 1 more Smart Citation

Influence of solar wind-magnetosphere coupling functions on theDstindex

et al. 2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…The WINDMI model is described in some detail in , Spencer et al (2007) and more recently in Patra et al (2011). The equations of the model are given by the following:…”

Section: Windmi Modelmentioning

confidence: 99%

The effect of nonlinear ionospheric conductivity enhancement on magnetospheric substorms

Spencer

Patra

2013

Nonlin. Processes Geophys.

Self Cite

View full text Add to dashboard Cite

We introduce the effect of enhanced ionospheric conductivity into a low-order, physics-based nonlinear model of the nightside magnetosphere called WINDMI. The model uses solar wind and interplanetary magnetic field (IMF) parameters from the ACE satellite located at the L1 point to predict substorm growth, onset, expansion and recovery measured by the AL index roughly 50–60 min in advance. The dynamics introduced by the conductivity enhancement into the model behavior is described, and illustrated through using synthetically constructed solar wind parameters as input. We use the new model to analyze two well-documented isolated substorms: one that occurred on 31 July 1997 from Aksnes et al. (2002), and another on 13 April 2000 from Huang et al. (2004). These two substorms have a common feature in that the solar wind driver sharply decreases in the early part of the recovery phase, and that neither of them are triggered by northward turning of the IMF Bz. By controlling the model parameters such that the onset time of the substorm is closely adhered to, the westward auroral electrojet peaks during substorm expansion are qualitatively reproduced. Furthermore, the electrojet recovers more slowly with enhanced conductivity playing a role, which explains the data more accurately

show abstract

“…The Swedish Space Weather Centre (http://src.irf.se/en/forecasts/) predicts the next hourly value of the Dst index -one hour forward in relation to the last entered data -using the recurrent Elman neural network. The project WINDMI RealTime Dst and AL indices provides the Dst prediction, which is performed using a physical model based on the calculation of ring currents in the magnetosphereionosphere system, and which also uses as input the data from the ACE spacecraft [8]. A modern model used to predict the Dst index one hour ahead proposed in [9] is based on artificial neural networks (ANN) combined with an analytical model of the SW-Earth's magnetosphere interaction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of geomagnetic indexes with the help of artificial neural networks

2017

View full text Add to dashboard Cite

Abstract.The results of prediction of geomagnetic indexes characterizing the state of the Earth's magnetosphere obtained with the help of artificial neural networks (ANN) for various prediction horizons are presented. The forecasts are based on multivariate time series including the values of the geomagnetic indices themselves, as well as data about the parameters of solar wind and interplanetary magnetic field, during several latest hours.

show abstract

Study ofDst/ring current recovery times using the WINDMI model

Cited by 20 publications

References 35 publications

Influence of solar wind-magnetosphere coupling functions on theDstindex

Influence of solar wind-magnetosphere coupling functions on theDstindex

The effect of nonlinear ionospheric conductivity enhancement on magnetospheric substorms

Prediction of geomagnetic indexes with the help of artificial neural networks

Contact Info

Product

Resources

About