Separation of spatial and temporal structure of auroral particle precipitation

[1] Knowledge of the dominant temporal and spatial scales of auroral features is instrumental in understanding the various mechanisms responsible for auroral particle precipitation. Single spacecraft data always suffer from temporal/spatial ambiguity. In an effort to separate the temporal and spatial variations of the aurora, we use electron and ion precipitation data from two co-orbiting satellites, F6 and F8 of the Defense Meteorological Satellite Program (DMSP). The two spacecraft have almost identical polar orbits with a small difference in period. As a result the time difference between the two measurements varies with time. We use two statistical tools in order to determine the most probable lifetimes and spatial dimensions of the prevalent auroral features. The first tool is cross-correlation analysis between the magnetic latitude series of electron and ion, number and energy fluxes measured by the two DMSP spacecraft. As one spacecraft overtakes the other, the variable time lag between the two measurements results in different cross-correlation of the two series. We explore the dependence of this variation on the time lag between the satellites. We find that the electron precipitation exhibits a decreasing correlation between the two spacecraft with increasing time lag, whereas there is only a small similar effect for the ion precipitation data. The second statistical tool is cross-spectral analysis, for which we compute the so-called coherence function as a function of frequency (or inverse wavelength) and hence size of the auroral features. The coherence function is a measure of the stability of auroral features of different sizes. We investigate its variation as a function of the time separation between the two measurements. We show that the coherence function of both electrons and ions remains high for up to 1.5 min spacecraft separations for all features larger than about 100 km in width. For smaller features the coherence is lower even for time lags of a few seconds. The results are discussed in the context of characteristic temporal and spatial auroral scales deduced from complementary studies and expected from theory

Investigation of magnetopause reconnection models using two colocated, low‐altitude satellites: A unifying reconnection geometry

Ion precipitation data from two co-orbiting Defense Meteorological Satellite Program satellites (F6 and F8) are used to investigate magnetopause reconnection models. We examine differential fluxes between 30 eV and 30 keV, from a Southern Hemisphere, prenoon pass during the morning of January 10, 1990. Data from the first satellite to pass through the region (F6) show two distinct ion energy dispersions •-1 ø of latitude apart, between 76 ø and 79 ø magnetic latitude. The electron data exhibit similar features at around the same region but with no or little energy dispersion, consistent with their high velocities. We suggest that the two energy dispersions can be explained by two separate injections resulting from two bursts of magnetopause reconnection. Data from the second satellite (F8), which moved through the same region I rain later, reveal the same energy-dispersed structures, only further poleward and with less overall flux. This temporal evolution is consistent with two recently reconnected flux tubes releasing their plasma as they move antisunward away from dayside merging sites. However, an observed overlap between the two ion energy dispersions suggests a more complex reconnection geometry than usual models can accommodate. We propose a generalized reconnection scenario that unifies the Bursty Single X-Line and the Multiple X-Line Reconnection models. A simple time-of-flight particle precipitation model is constructed to reproduce the ion dispersions and their overlap. The modeling results suggest that for time-dependent reconnection the dispersion overlap is observed clearly at low altitudes only for a short period compared with the evolution timescale of the ion precipitation

The CRaTER Special Issue of Space Weather: Building the observational foundation to deduce biological effects of space radiation

[1] The United States is preparing for exploration beyond low-Earth Orbit (LEO). However, the space radiation environment poses significant risks. The radiation hazard is potentially severe but not sufficiently well characterized to determine if long missions outside LEO can be accomplished with acceptable risk [Cucinotta et al., 2001; Schwadron et al., 2010; Cucinotta et al., 2010]. Radiation hazards may be over- or under-stated through incomplete characterization in terms of net quantities such as accumulated dose. Time-dependent characterization often changes acute risk estimates [NCRP, 1989; Cucinotta, 1999; Cucinotta et al., 2000; George et al., 2002]. For example, events with high accumulated doses but sufficiently low dose rates (/h) pose significantly reduced risks. Protons, heavy ions, and neutrons all contribute significantly to the radiation hazard. However, each form of radiation presents different biological effectiveness. As a result, quality factors and radiation-specific weighting factors are needed to assess biological effectiveness of different forms of radiation [e.g., NCRP 116, 1993] (Figure 1). More complete characterization must account for time-dependent radiation effects according to organ type, primary and secondary radiation composition, and acute effects (vomiting, sickness, and, at high exposures, death) versus chronic effects (such as cancer)

Empirical modeling of the quiet time nightside magnetosphere

Empirical modeling of plasma pressure and magnetic field for the quiet time nightside magnetosphere is investigated. Two models are constructed for this study. One model, referred to here as T89R, is basically the magnetic field model of Tsyganenko (1989) but is modified by the addition of an inner eastward ring current at a radial distance of ∼3 RE as suggested by observation. The other is a combination of the T89R model and the long version of the magnetic field model of Tsyganenko (1987) such that the former dominates the magnetic field in the inner magnetosphere, whereas the latter prevails in the distant tail. The distribution of plasma pressure, which is required to balance the magnetic force for each of these two field models, is computed along the tail axis in the midnight meridian. The occurrence of pressure anisotropy in the inner magnetospheric region is also taken into account by determining an empirical fit to the observed plasma pressure anisotropy. This effort is the first attempt to obtain the plasma pressure distribution in force equilibrium with magnetic stresses from an empirical field model with the inclusion of pressure anisotropy. The inclusion of pressure anisotropy alters the plasma pressure by as much as a factor of ∼3 in the inner magnetosphere. The deduced plasma pressure profile along the tail axis is found to be in good agreement with the observed quiet time plasma pressure for geocentric distances between ∼2 and ∼35 RE

Revision of empirical electric field modeling in the inner magnetosphere using Cluster data

Using Cluster data from the Electron Drift (EDI) and the Electric Field and Wave (EFW) instruments, we revise our empirically-based, inner-magnetospheric electric field (UNH-IMEF) model at 22.662 mV/m; K-p\u3c1, 1K(p)\u3c2, 2K(p)\u3c3, 3K(p)\u3c4, 4K(p)\u3c5, and K(p)4(+). Patterns consist of one set of data and processing for smaller activities, and another for higher activities. As activity increases, the skewed potential contour related to the partial ring current appears on the nightside. With the revised analysis, we find that the skewed potential contours get clearer and potential contours get denser on the nightside and morningside. Since the fluctuating components are not negligible, standard deviations from the modeled values are included in the model. In this study, we perform validation of the derived model more extensively. We find experimentally that the skewed contours are located close to the last closed equipotential, consistent with previous theories. This gives physical context to our model and serves as one validation effort. As another validation effort, the derived results are compared with other models/measurements. From these comparisons, we conclude that our model has some clear advantages over the others