The spatial structure of the oncoming solar wind at Earth and the shortcomings of a solar-wind monitor at L1

Borovsky, Joseph E.

doi:10.1016/j.jastp.2017.03.014

Cited by 48 publications

(48 citation statements)

References 93 publications

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“…The solar wind flow at Earth is systematically aberrated by the Earth's motion about the Sun, and the direction of the plasma flow vector is highly variable in time. An analysis that examined (a) characteristic scale sizes of the spatial structure of the solar wind plasma, (b) the amplitudes of the variations of the flow vector, and (c) the spacecraft orbits at L1 found strong differences between the solar wind variations hitting the Earth and the solar wind variations measured by the upstream monitors (Borovsky, ). Physically, there are a lot of factors acting in the system . …”

Section: Discussionmentioning

confidence: 99%

Time‐Integral Correlations of Multiple Variables With the Relativistic‐Electron Flux at Geosynchronous Orbit: The Strong Roles of Substorm‐Injected Electrons and the Ion Plasma Sheet

Borovsky

2017

JGR Space Physics

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Time‐integral correlations are examined between the geosynchronous relativistic electron flux index Fe1.2 and 31 variables of the solar wind and magnetosphere. An “evolutionary algorithm” is used to maximize correlations. Time integrations (into the past) of the variables are found to be superior to time‐lagged variables for maximizing correlations with the radiation belt. Physical arguments are given as to why. Dominant correlations are found for the substorm‐injected electron flux at geosynchronous orbit and for the pressure of the ion plasma sheet. Different sets of variables are constructed and correlated with Fe1.2: some sets maximize the correlations, and some sets are based on purely solar wind variables. Examining known physical mechanisms that act on the radiation belt, sets of correlations are constructed (1) using magnetospheric variables that control those physical mechanisms and (2) using the solar wind variables that control those magnetospheric variables. Fe1.2‐increasing intervals are correlated separately from Fe1.2‐decreasing intervals, and the introduction of autoregression into the time‐integral correlations is explored. A great impediment to discerning physical cause and effect from the correlations is the fact that all solar wind variables are intercorrelated and carry much of the same information about the time sequence of the solar wind that drives the time sequence of the magnetosphere.

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Section: Discussionmentioning

confidence: 99%

Time‐Integral Correlations of Multiple Variables With the Relativistic‐Electron Flux at Geosynchronous Orbit: The Strong Roles of Substorm‐Injected Electrons and the Ion Plasma Sheet

Borovsky

2017

JGR Space Physics

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“…One reason for this may be the correlation lengths in the solar wind. Borovsky () found the magnetic field correlation to be on the order of 100 R E . Due to the smaller apoapsis of ACE, the spacecraft are never more than roughly 40 R E from the Earth‐Sun line, smaller than a typical spatial scale.…”

Section: Position Of Upstream Monitorsmentioning

confidence: 99%

Quantifying the Uncertainty of Using Solar Wind Measurements for Geospace Inputs

2019

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Predictive models for the Earth's space environment routinely use parameters from the solar wind as inputs. Measurements from spacecraft orbiting the first Lagrange point serve as convenient values for these inputs. The mass, momentum, and energy input into the Earth's space environment, however, are a function of the shocked and processed plasma within the magnetosheath, which can vary significantly from the pristine solar wind at the first Lagrange point. Here statistical measurements from the OMNI data set are combined with measurements by the THEMIS mission within the magnetosheath to generate uncertainty values for pressure and magnetic clock angle. These uncertainties are generated to account for known physical processes in the foreshock and magnetosheath as well as the position of the spacecraft being used to generate the OMNI data set.

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“…The selected block length is consistent with the rule‐of‐thumb that the block length should be approximately N 1/ a where a is between 3 and 4 (Niehof & Morley, , and references therein). For typical solar wind speeds of 300 to 800 km/s, this corresponds to scale lengths of 169–452 R E , several times larger than typical flux tube diameters in the solar wind (Borovsky, ).…”

Section: Error Model For Solar Wind Inputsmentioning

confidence: 95%

“…The transparent purple bar is intended to illustrate a planar front in the IMF, perpendicular to the Parker spiral, that propagates radially outward toward Earth. As the solar wind is not homogeneous along the front illustrated here, we can identify a number of key sources of uncertainty in our solar wind measurement as a driver for a space weather model: Our upstream monitor orbits around the L1 point but is rarely sampling a ballistic trajectory that would reach the nose of the bow shock (e.g., Borovsky, 2017). Solar wind propagation methods assume a certain homogeneity in the solar wind that is being propagated, while observations suggest that the plasma and magnetic field are not homogeneous (e.g., Kessel et al, ; Borovsky, , ). The solar wind properties are discontinuous across boundaries between regions with scale sizes approaching the cross section of the magnetosphere (e.g., Borovsky, , ). The propagation method itself is not perfect and can introduce some uncertainty in the parameters projected to be arriving at the bow shock (e.g., Case & Wild, ; Cash et al, ). …”

Section: Uncertainties In Specifying the Solar Wind State For Magnetomentioning

confidence: 99%

Perturbed Input Ensemble Modeling With the Space Weather Modeling Framework

2018

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To assess the effect of uncertainties in solar wind driving on the predictions from the operational configuration of the Space Weather Modeling Framework, we have developed a nonparametric method for generating multiple possible realizations of the solar wind just upstream of the bow shock, based on observations near the first Lagrangian point. We have applied this method to the solar wind inputs at the upstream boundary of Space Weather Modeling Framework and have simulated the geomagnetic storm of 5 April 2010. We ran a 40‐member ensemble for this event and have used this ensemble to quantify the uncertainty in the predicted Sym‐H index and ground magnetic disturbances due to the uncertainty in the upstream boundary conditions. Both the ensemble mean and the unperturbed simulation tend to underpredict the magnitude of Sym‐H in the quiet interval before the storm and overpredict in the storm itself, consistent with previous work. The ensemble mean is a more accurate predictor of Sym‐H, improving the mean absolute error by nearly 2 nT for this interval and displaying a smaller bias. We also examine the uncertainty in predicted maxima in ground magnetic disturbances. The confidence intervals are typically narrow during periods where the predicted dBH/dt is low. The confidence intervals are often much wider where the median prediction is for enhanced dBH/dt. The ensemble also allows us to identify intervals of activity that cannot be explained by uncertainty in the solar wind driver, driving further model improvements. This work demonstrates the feasibility and importance of ensemble modeling for space weather applications.

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The spatial structure of the oncoming solar wind at Earth and the shortcomings of a solar-wind monitor at L1

Cited by 48 publications

References 93 publications

Time‐Integral Correlations of Multiple Variables With the Relativistic‐Electron Flux at Geosynchronous Orbit: The Strong Roles of Substorm‐Injected Electrons and the Ion Plasma Sheet

Time‐Integral Correlations of Multiple Variables With the Relativistic‐Electron Flux at Geosynchronous Orbit: The Strong Roles of Substorm‐Injected Electrons and the Ion Plasma Sheet

Quantifying the Uncertainty of Using Solar Wind Measurements for Geospace Inputs

Perturbed Input Ensemble Modeling With the Space Weather Modeling Framework

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