Radiation belt electrons respond to multiple solar wind inputs

Abstract

[1] The multivariate statistical basis that underlies both single-and multi-input linear prediction filter analyses is reviewed, providing context necessary to understand the full capabilities and limitations of such models. A brief reanalysis of single-input filters is conducted primarily as a contrast to subsequent analysis of multi-input linear filters, which (1) guarantee similar or better prediction capabilities than single-input linear filters and (2) reduce bias in estimated filter coefficients that is inherent to underspecified linear models when ordinary least squares algorithms are employed. The former is clearly valuable from a practical standpoint, but the latter helps build confidence in any physical interpretations of both the filter coefficients, which often emulate stable low-order dynamical response functions quite well, as well as prediction error statistics that can be used to provide a lower bound on the fractional or percent variance of radiation belt electron flux that can be attributed to each different solar wind input. We find that the solar wind bulk speed tends to be the primary driver of electron flux enhancements at magnetic L shells larger than 4, with little or no relation to flux decreases. Changes in the solar wind's magnetic field strength tend to temporarily reduce electron fluxes between L = 4 and L = 8, while enhancing it between L = 3 and L = 4. In contrast to predictions generated by single-input linear filters, multi-input filters show that solar wind plasma density only contributes weakly to electron flux variability, although it does so consistently across nearly all L shells. Finally, we studied two distinct 4-year intervals within the most recent solar cycle and found that smaller, more time-stationary prediction errors are generated by multi-input linear filters. We therefore conclude that multi-input filters more accurately reflect real dynamic relationships than any single-input linear filter alone.

A state-space model of radiation belt electron flux dynamics

“…To understand this discrepancy, we first reconsider results from Rigler et al (2004), where it was shown that there exists a lack of time stationarity in adaptive single-input FIR filter coefficients used to predict SAMPEX electron fluxes. Similarly, Rigler et al (2007) demonstrated a lack of stationarity in prediction error statistics from static single-and multiinput linear filter output. If instrument error can be discounted, such non-stationary behavior in a geophysical time series is usually indicative of some sort of ''missing inputs'' required by the model.…”

“…If instrument error can be discounted, such non-stationary behavior in a geophysical time series is usually indicative of some sort of ''missing inputs'' required by the model. These ''missing inputs'' may indeed be additional drivers, like those considered by Rigler et al (2007). They might also be time-lagged model output, which amounts to linear dynamic feedback.…”

“…More recently, a series of electron radiation belt studies (Vassiliadis et al, 2002Rigler et al, 2005Rigler et al, , 2007 have applied FIR filters to a much broader range of the Earth's inner magnetosphere. Solar wind data from NASA's OMNI database (King and Papitashvili, 2005) were used to predict electron flux measured by the Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX; Baker et al, 1993;Cook et al, 1993).…”

Space Weather Effects in the Earth’s Radiation Belts

“…There also have been statistical studies focusing on the electrons in the whole outer radiation belt and their relation to the solar wind parameters (e.g., Li et al 1997aLi et al , 1997bBaker et al 1999;Vassiliadis et al 2002;Mann et al 2004;Rigler et al 2007;Li et al 2009). , using >750 keV and >1 MeV electron data from two microsatellites, STRV-1a and 1b, which were in highly elliptical, near-equatorial orbits, studied the correlation between total relativistic electron content in the outer belt and solar wind and geomagnetic parameters during geomagnetic storms at the first six months of 1995.…”

TSDS: high-performance merge, subset, and filter software for time series-like data

“…In this field, the need for improved access to time series-like data is motivated by a frequent complaint from data users who state that they want "all of the data over its full time range" if its size makes local storage reasonable, but most data providers only serve granules or subsets of the data [McPherron, 2008]. The need for TSDS is evident in many research papers where it is clear, based on where the data were obtained, that the researcher needed to do extensive aggregation manually using data from multiple data sources [Weigel and Baker, 2003;Vassiliadis et al, 2003;Green et al, 2004;Weimer, 2005;Bargatze et al, 2005;Tanskanen et al, 2005;O'Brien and Lemon, 2007;Rigler et al, 2007;Bortnik et al, 2007;Gjerloev and Barnes, 2008;Pulkkinen et al, 2008a;Pulkkinen et al, 2008b;Lemon et al, 2008;McPherron et al, 2009].…”