S. Zaharia

Comparative study of ring current development using empirical, dipolar, and self‐consistent magnetic field simulations

[1] The effects of nondipolar magnetic field configuration and the feedback of a self-consistently computed magnetic field on ring current dynamics are investigated during a double-dip storm with minima SYM-H = -90 nT at ~2000 UT, 20 November, and SYM-H = -127 nT at ~1000 UT, 21 November 2002. We use our kinetic ring current-atmosphere interactions model with self-consistent magnetic field (RAM-SCB) to study the redistribution of plasma in the inner magnetosphere after its fresh injection from the plasma sheet. The kinetic model is fully extended to nondipolar magnetic (B) field geometry and two-way coupled with an Euler-potential-based equilibrium model that calculates self-consistently the three-dimensional magnetic field in force balance with the anisotropic ring current distributions. The ring current source population is inferred from LANL geosynchronous satellite data; a superdense plasma sheet observed during the second storm main phase contributes significantly to ring current buildup. We find that the bounce-averaged velocities increase while the bounce-averaged geocoronal hydrogen densities decrease on the nightside when a nondipolar B field is used. A depression of the ring current fluxes and a confinement of the ring current close to Earth are thus observed on the nightside as geomagnetic activity increases. In contrast to the dipolar case, the proton anisotropy increases considerably in the postnoon sector, and the nondipolar simulations predict the excitation of intense EMIC waves at large L shells. The total ring current energy and |Dst| index calculated with the self-consistent B field are in best agreement with observations, being smaller compared to the dipolar calculations but larger than the empirical B field predictions.

Role of entropy in magnetotail dynamics

The role of entropy conservation and loss in magnetospheric dynamics, particularly in relation to substorm phases, is discussed on the basis of MHD theory and simulations, using comparisons with PIC simulations for validation. Entropy conservation appears to be a crucial element leading to the formation of thin embedded current sheets in the late substorm growth phase and the potential loss of equilibrium. Entropy loss (in the form of plasmoids) is essential in the earthward transport of flux tubes (bubbles, bursty bulk flows). Entropy loss also changes the tail stability properties and may render ballooning modes unstable and thus contribute to cross-tail variability. We illustrate these effects through results from theory and simulations. Entropy conservation also governs the accessibility of final states of evolution and the amount of energy that may be released.

Dynamic Radiation Environment Assimilation Model: DREAM

The Dynamic Radiation Environment Assimilation Model (DREAM) is a 3-year effort sponsored by the US Department of Energy to provide global, retrospective, or real-time specification of the natural and potential nuclear radiation environments. The DREAM model uses Kalman filtering techniques that combine the strengths of new physical models of the radiation belts with electron observations from long-term satellite systems such as GPS and geosynchronous systems. DREAM includes a physics model for the production and long-term evolution of artificial radiation belts from high altitude nuclear explosions. DREAM has been validated against satellites in arbitrary orbits and consistently produces more accurate results than existing models. Tools for user-specific applications and graphical displays are in beta testing and a real-time version of DREAM has been in continuous operation since November 2009.

L* neural networks from different magnetic field models and their applicability

The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The typical way to numerically calculate the L* value follows the method described by Roederer (1970), which is just a line integration method that is computationally slow and expensive. This work describes the application of an artificial neural network technique to a series of magnetospheric field models for calculating L* values in microseconds instead of seconds without losing significant accuracy, thereby delivering to the radiation belt community various L* neural networks. These neural networks will enable comprehensive solar-cycle long studies of radiation belt processes and can also help the development of operational radiation belt models because of the speed in calculating L*. The main focus of this work is to test the applicability of each L* neural network, an aspect not addressed in the previous studies, under different interplanetary and magnetospheric conditions. Specifically, we describe the conditions when the neural network is providing a good approximation to the full numerical calculation of L* and when the traditional but more time-consuming method should be used. These L* neural networks are available for download at http://lanlstar.net.

Storm-time plasma signatures observed by IMAGE/MENA and comparison with a global physics-based model

] We present energetic neutral atom (ENA) fluxesmeasured by the medium energy neutral atom (MENA)imager onboard the IMAGE satellite for the geomagneticstorm of 21 October 2001 at energies of 6 and 12 keV. Thefluxes indicate strong low altitude emissions close to theEarth and a nightside peak close to local midnight.The fluxes are compared with theoretical ENA fluxescalculated using the ring current-atmosphere interactionmodel (RAM). We find good quantitative agreementbetween MENA data and RAM results, both of whichindicate a peak in ENA emissions on the nightside, close tolocal midnight which varies in radial location between 2and 5R

Current sheet scattering and ion isotropic boundary under 3‐D empirical force‐balanced magnetic field

To determine statistically the extent to which current sheet scattering is sufficient to account for the observed ion isotropic boundaries (IBs) for <30 keV ions, we have computed IBs from our 3-D empirical force-balanced magnetic field, identified IBs in FAST observations, and investigated the model-observation consistency. We have found in both model and FAST results the same dependences of IB latitudes on magnetic local time, ion energy, Kp, and solar wind dynamic pressure (PSW) levels: IB moves to higher latitudes from midnight toward dawn/dusk and to lower latitudes as energy increases and as Kp or PSW increases. The model predicts well the observed energy dependence, and the modeled IB latitudes match fairly well with those from FAST for Kp = 0. As Kp increases, the latitude agreement at midnight remains good but a larger discrepancy is found near dusk. The modeled IBs at the equator are located around the earthward boundary of highly isotropic ions observed by Time History of Events and Macroscale Interactions during Substorms at midnight and postmidnight, but with some discrepancy near dusk under high Kp. Thus, our results indicate that current sheet scattering generally plays the dominant role. The discrepancies suggest the importance of pitch angle scattering by electromagnetic ion cyclotron waves, which occur more often from dusk to noon and are more active during higher Kp. The comparison with the observed IBs is better with our model than under the nonforce-balanced T89, indicating that using a forced-balanced model improves the description of the magnetic field configuration and reinforces our conclusions regarding the role of current sheet scattering.

Application and testing of the L* neural network with the self‐consistent magnetic field model of RAM‐SCB

We expanded our previous work on L* neural networks that used empirical magnetic field models as the underlying models by applying and extending our technique to drift shells calculated from a physics-based magnetic field model. While empirical magnetic field models represent an average, statistical magnetospheric state, the RAM-SCB model, a first-principles magnetically self-consistent code, computes magnetic fields based on fundamental equations of plasma physics. Unlike the previous L* neural networks that include McIlwain L and mirror point magnetic field as part of the inputs, the new L* neural network only requires solar wind conditions and the Dst index, allowing for an easier preparation of input parameters. This new neural network is compared against those previously trained networks and validated by the tracing method in the International Radiation Belt Environment Modeling (IRBEM) library. The accuracy of all L* neural networks with different underlying magnetic field models is evaluated by applying the electron phase space density (PSD)-matching technique derived from the Liouvilles theorem to the Van Allen Probes observations. Results indicate that the uncertainty in the predicted L* is statistically (75%) below 0.7 with a median value mostly below 0.2 and the median absolute deviation around 0.15, regardless of the underlying magnetic field model. We found that such an uncertainty in the calculated L* value can shift the peak location of electron phase space density (PSD) profile by 0.2 RE radially but with its shape nearly preserved.

Adiabatic plasma equilibrium and application to a reconnection problem

The evolution of many plasma systems is adiabatic, i.e., plasma entropy is conserved in each magnetic flux tube. An apparently surprising result found recently from simulations of a forced magnetic reconnection problem (the “Newton Challenge” [J. Birn et al., Phys. Plasmas 13, 092117 (2006)]) is that even in the presence of a dissipative process such as reconnection, the entropy within a flux tube can still be approximately conserved, due to the strong localization of the dissipation. To address plasma equilibrium with such adiabatic constraints, a novel code has been developed that computes equilibria with entropy profile as input, using the alternating dimension method [H. Grad et al., Proc. Natl. Acad. Sci. USA 72, 3789 (1975)]. The code alternates between solving the two–dimensional (2D) Grad-Shafranov equation to obtain the field configuration (flux function A) from the pressure profile P(A) and a 1D ordinary differential equation that uses the entropy conservation to derive the pressure function P(A...

Particle Transport and Energization Associated with Disturbed Magnetospheric Events

Energetic particle flux enhancement events observed by satellites during strongly disturbed events in the magnetosphere (e.g., substorms, storm sudden commencements, etc.) are studied by considering interaction of particles with Earthward propagating electromagnetic pulses of westward electric field and consistent magnetic field of localized radial and azimuthal extent in a background magnetic field. The energetic particle flux enhancement is mainly due to the betatron acceleration process: particles are swept by the Earthward propagating electric field pulses via the EXB drift toward the Earth to higher magnetic field locations and are energized because of magnetic moment conservation. The most energized particles are those which stay in the pulse for the longest time and are swept the longest radial distance toward the Earth. Assuming a constant propagating velocity of the pulse we obtain analytical solutions of particle orbits. We examine substorm energetic particle injection by computing the particle flux and comparing with geosynchronous satellite observations. Our results show that for pulse parameters leading to consistency with observed flux values, the bulk of the injected particles arrive from distances less than 9 R(subscript E), which is closer to the Earth than the values obtained by the previous model and is also closer to the distances obtained by the injection boundary model.

Toward Understanding Radiation Belt Dynamics, Nuclear Explosion‐Produced Artificial Belts, and Active Radiation Belt Remediation: Producing a Radiation Belt Data Assimilation Model

The space radiation environment presents serious challenges to spacecraft design and operations: adding costs or compromising capability. Our understanding of radiation belt dynamics has changed dramatically as a result of new observations. Relativistic electron fluxes change rapidly, on time scales less than a day, in response to geomagnetic activity. However, the magnitude, and even the sign, of the change appears uncorrelated with common geomagnetic indices. Additionally, observations of peaks in radial phase space density are not readily explained by diffusion processes. These observations lead to a complex picture of acceleration and loss process all acting on top of adiabatic changes due to the storm-time magnetic field. Of even greater practical concern for national security applications is the threat posed by artificial radiation belts produced by high altitude nuclear explosions (HANE). The HANE-produced environment, like the natural environment, is subject to global transport, acceleration, and losses. Radiation belt remediation programs aim to exploit our knowledge of natural loss processes to artificially enhance the removal of particles from the radiation belts. The need to open up new orbits and new capabilities has raised questions about the space environment that, up to this time, we have been unable to fully answer. Here we describe the development of a next-generation model for specifying natural and HANE-produced radiation belts using data-assimilation based modeling. We exploit the convergence of inexpensive high-performance parallel computing, new physical understanding, and an unprecedented set of satellite measurements to improve national capability to model, predict, and control the space environment.