Influence of solar wind-magnetosphere coupling functions on the<i>Dst</i>index

Spencer, E. A.; Kasturi, P.; Patra, S.; Horton, W.; Mays, M. L.

doi:10.1029/2011ja016780

Cited by 15 publications

(14 citation statements)

References 32 publications

(66 reference statements)

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“…This is an impulsive response to a time‐integrated property of the IMF and arguably (Freeman & Morley, ; Morley et al, ) also to an additional trigger from upstream IMF variations in around half of cases (Milan et al, ). It is likely that more of the variability of the B z ‐controlled modes would be explained by a nonlinear function of the solar wind parameters (e.g., Boynton et al, ; Finch & Lockwood, ; Newell et al, ; Spencer et al, ), but we do not investigate this here.…”

Section: Discussionmentioning

confidence: 97%

Interplanetary Magnetic Field Control of Polar Ionospheric Equivalent Current System Modes

2019

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We analyze the response of different ionospheric equivalent current modes to variations in the interplanetary magnetic field (IMF) components By and Bz. Each mode comprises a fixed spatial pattern whose amplitude varies in time, identified by a month‐by‐month empirical orthogonal function separation of surface measured magnetic field variance. Here we focus on four sets of modes that have been previously identified as DPY, DP2, NBZ, and DP1. We derive the cross‐correlation function of each mode set with either IMF By or Bz for lags ranging from −10 to +600 mins with respect to the IMF state at the bow shock nose. For all four sets of modes, the average correlation can be reproduced by a sum of up to three linear responses to the IMF component, each centered on a different lag. These are interpreted as the statistical ionospheric responses to magnetopause merging (15‐ to 20‐min lag) and magnetotail reconnection (60‐min lag) and to IMF persistence. Of the mode sets, NBZ and DPY are the most predictable from a given IMF component, with DP1 (the substorm component) the least predictable. The proportion of mode variability explained by the IMF increases for the longer lags, thought to indicate conductivity feedbacks from substorms. In summary, we confirm the postulated physical basis of these modes and quantify their multiple reconfiguration timescales.

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Section: Discussionmentioning

confidence: 97%

Interplanetary Magnetic Field Control of Polar Ionospheric Equivalent Current System Modes

2019

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“…A great many combinations of near‐Earth interplanetary parameters (so‐called coupling functions ) have been proposed over many years to describe the transfer of energy, and/or mass, and/or momentum, and/or electric field from the solar wind into the Earth's magnetosphere‐ionosphere‐thermosphere system (e.g., Balikhin et al, ; Bargatze et al, , ; Borovsky, ; Burton et al, ; Crooker et al, ; Feynman & Crooker, ; Kan & Lee, ; McPherron et al, , ; Murayama, ; Newell et al, ; Papitashvili et al, ; Perreault & Akasofu, ; Reiff et al, ; Scurry & Russell, ; Spencer et al, ; Temerin & Li, , ; Tenfjord & Ostgaard, ; Vassiliadis et al, ; Vasyliunas et al, ; Wing & Sibeck, ; Wu & Lundstedt, ; Wygant et al, ). These are derived and tested by comparison with terrestrial space weather disturbance indices (sometimes in combination), which respond to the energy, mass, electric field, and/or momentum input to the magnetosphere from the solar wind (in a combination thereof that depends on which terrestrial index is used).…”

Section: Introductionmentioning

confidence: 99%

The Development of a Space Climatology: 1. Solar Wind Magnetosphere Coupling as a Function of Timescale and the Effect of Data Gaps

et al. 2019

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Different terrestrial space weather indicators (such as geomagnetic indices, transpolar voltage, and ring current particle content) depend on different coupling functions (combinations of near-Earth solar wind parameters), and previous studies also reported a dependence on the averaging timescale, τ. We study the relationships of the am and SME geomagnetic indices to the power input into the magnetosphere P α , estimated using the optimum coupling exponent α, for a range of τ between 1 min and 1 year. The effect of missing data is investigated by introducing synthetic gaps into near-continuous data, and the best method for dealing with them when deriving the coupling function is formally defined. Using P α , we show that gaps in data recorded before 1995 have introduced considerable errors into coupling functions. From the near-continuous solar wind data for 1996-2016, we find that α = 0.44 ± 0.02 and no significant evidence that α depends on τ, yielding P α ∝B 0.88 V sw 1.90 (m sw N sw ) 0.23 sin 4 (θ/2), where B is the interplanetary magnetic field, N sw the solar wind number density, m sw its mean ion mass, V sw its velocity, and θ the interplanetary magnetic field clock angle in the geocentric solar magnetospheric reference frame. Values of P α that are accurate to within ±5% for 1996-2016 have an availability of 83.8%, and the correlation between P α and am for these data is shown to be 0.990 (between 0.972 and 0.997 at the 2σ uncertainty level), 0.897 ± 0.004, and 0.790 ± 0.03, for τ of 1 year, 1 day, and 3 hr, respectively, and that between P α and SME at τ of 1 min is 0.7046 ± 0.0004.Plain Language Summary This is the first step of three toward constructing a climatology describing the statistics of how space weather has varied over the past 400 years. This climatology will be valuable in the design of systems vulnerable to space weather. To do this, we here investigate how best to quantify the power extracted from the solar wind by the magnetosphere. We need to do this over a range of timescales from the annual averages used to describe long-term changes (space climate) down to fluctuations over minutes and hours, which drive space weather events.A great many combinations of near-Earth interplanetary parameters (so-called coupling functions) have been proposed over many years to describe the transfer of energy, and/or mass, and/or momentum, and/or LOCKWOOD ET AL. 133

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“…Apart from V and B z , other quantities which have been shown to correlate with geomagnetic activity are solar wind dynamic pressure P , clock angle

\tan θ = \frac{B_{y}}{B_{z}}

, Akasofu ε [ Pudovkin and Semenov , ], and solar wind magnetosphere coupling functions [ Spencer et al , ].…”

Section: One Step Ahead Predictionmentioning

confidence: 99%

Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach

2017

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We present a methodology for generating probabilistic predictions for the Disturbance Storm Time(Dst) geomagnetic activity index. We focus on the One Step Ahead prediction task and use the OMNI hourly resolution data to build our models. Our proposed methodology is based on the technique of Gaussian Process Regression. Within this framework we develop two models; Gaussian Process Autoregressive (GP‐AR) and Gaussian Process Autoregressive with eXogenous inputs (GP‐ARX). We also propose a criterion to aid model selection with respect to the order of autoregressive inputs. Finally, we test the performance of the GP‐AR and GP‐ARX models on a set of 63 geomagnetic storms between 1998 and 2006 and illustrate sample predictions with error bars for some of these events.

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Influence of solar wind-magnetosphere coupling functions on theDstindex

Cited by 15 publications

References 32 publications

Interplanetary Magnetic Field Control of Polar Ionospheric Equivalent Current System Modes

Interplanetary Magnetic Field Control of Polar Ionospheric Equivalent Current System Modes

The Development of a Space Climatology: 1. Solar Wind Magnetosphere Coupling as a Function of Timescale and the Effect of Data Gaps

Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach

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