ALBERT

Beschreibung: Relativistic electron fluxes of the outer radiation belt often decrease rapidly in response to solar wind disturbances. The importance of the magnetopause shadowing (MPS) effect on such electron losses has yet to be quantified. If the MPS is essential for outer radiation belt electron losses, a close relationship between the outer edge of the outer belt and the magnetopause standoff distance is expected. Using GOES and THEMIS data, we examined earthward movement of the outer edge of the outer belt during electron loss events at geosynchronous orbit and its correlation with the magnetopause standoff distance. In events with significant earthward movement, we found a good correlation. There were no clear correlations in events without significant earthward movement, however. Comparing the observational results with a test particle simulation, the observed dependence between the outer edge and the magnetopause standoff distance is consistent with the MPS effect.

Beschreibung: In this work a one-dimensional radial diffusion model for phase space density, together with observational satellite data, is used in an ensemble data assimilation with the purpose of accurately estimating Earth's radiation belt particle distribution. A particular concern in data assimilation for radiation belt models are model deficiencies, which can adversely impact the solution of the assimilation. To adequately address these deficiencies, a localized adaptive covariance inflation technique is implemented in the data assimilation to account for model uncertainty. Numerical results from identical-twin experiments, where data is generated from the same model, as well as the assimilation of real observational data, are presented. The results show improvement in the predictive skill of the model solution due to the proper inclusion of model errors in the data assimilation.

Beschreibung: Data assimilation methods have become increasingly popular to describe the outer radiation belt energetic electron environment. We use a Kalman filter with inputs of 1) electron phase space density (PSD) for constant first and second adiabatic invariants, μ = 2083[MeV/G] and K = 0.03[G1/2RE] respectively, from a five satellite data set (three LANL-GEO, one GPS, and Polar), and 2) a one-dimensional radial diffusion model with loss and source terms included. We augment the Kalman filter to include the intensity of local acceleration in the state vector. The output is an estimate of PSD for the radial range of the outer radiation belt and the time-dependent amplitude parameter of a Gaussian shaped source rate term for given location and width. To further constrain the source rate parameters, a root mean square (RMS) analysis of the observation residual vector (a.k.a. innovation vector) is performed in a parameter space of source location and width. We analyze five storm periods spanning from July 30th to October 24th of 2002, and each period's unique solution in the location-width parameter space is assimilated with the Kalman filter for a continuous reanalysis of the full 87 day period. The source amplitude parameter is analyzed for insight into time periods of enhanced local heating, suppressed loss, or, as the parameter can take negative values, additional loss. The source is found to peak in the recovery phases of the storms where the rate is sufficient to repopulate the radiation belt in approximately one day, suggesting that local heating is a major contributor to the electron radiation belts during the recovery phase.

Beschreibung: The Dynamic Radiation Environment Assimilation Model (DREAM) was developed to provide accurate, global specification of the Earth's radiation belts and to better understand the physical processes that control radiation belt structure and dynamics. DREAM is designed using a modular software approach in order to provide a computational framework that makes it easy to change components such as the global magnetic field model, radiation belt dynamics model, boundary conditions, etc. This paper provides a broad overview of the DREAM model and a summary of some of the principal results to date. We describe the structure of the DREAM model, describe the five major components, and illustrate the various options that are available for each component. We discuss how the data assimilation is performed and the data preprocessing and postprocessing that are required for producing the final DREAM outputs. We describe how we apply global magnetic field models for conversion between flux and phase space density and, in particular, the benefits of using a self-consistent, coupled ring current–magnetic field model. We discuss some of the results from DREAM including testing of boundary condition assumptions and effects of adding a source term to radial diffusion models. We also describe some of the testing and validation of DREAM and prospects for future development.

Beschreibung: The validation of the magnetically self-consistent inner magnetospheric model RAM-SCB developed at Los Alamos National Laboratory is presented here. The model consists of two codes: a kinetic ring current–atmosphere interaction model (RAM) and a 3-D equilibrium magnetic field code (SCB). The validation is conducted by simulating two magnetic storm events and then comparing the model results against a variety of satellite in situ observations, including the magnetic field from Cluster and Polar spacecraft, ion differential flux from the Cluster/CODIF (Composition and Distribution Function) analyzer, and the ground-based SYM-H index. The model prediction of the magnetic field is in good agreement with observations, which indicates the model's capability of representing well the inner magnetospheric field configuration. This provides confidence for the RAM-SCB model to be utilized for field line and drift shell tracing, which are needed in radiation belt studies. While the SYM-H index, which reflects the total ring current energy content, is generally reasonably reproduced by the model using the Weimer electric field model, the modeled ion differential flux clearly depends on the electric field strength, local time, and magnetic activity level. A self-consistent electric field approach may be needed to improve the model performance in this regard.

Beschreibung: 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.

Beschreibung: The world's ecosystems are subjected to various anthropogenic global change agents, such as enrichment of atmospheric CO 2 concentrations, nitrogen (N) deposition and changes in precipitation regimes. Despite the increasing appreciation that the consequences of impending global change can be better understood if varying agents are studied in concert, there is a paucity of multi-factor long-term studies, particularly on belowground processes. Here, we address this gap by examining the responses of soil food webs and biodiversity to enrichment of CO 2 , elevated N and summer drought in a long-term grassland study at Cedar Creek, Minnesota, USA (BioCON experiment). We use structural equation modeling (SEM), various abiotic and biotic explanatory variables, and data on soil microorganisms, protozoa, nematodes and soil microarthropods to identify the impacts of multiple global change effects on drivers below ground. We found that long-term (13-year) changes in CO 2 and N availability resulted in modest alterations of soil biotic food webs and biodiversity via several mechanisms, encompassing soil water availability, plant productivity and – most importantly – changes in rhizodeposition. Four years of manipulation of summer drought exerted surprisingly minor effects, only detrimentally affecting belowground herbivores, and ciliate protists at elevated N. Elevated CO 2 increased microbial biomass and the density of ciliates, microarthropod detritivores and gamasid mites, most likely by fuelling soil food webs with labile C. Moreover, beneficial bottom-up effects of elevated CO 2 compensated for detrimental elevated N effects on soil microarthropod taxa richness. By contrast, nematode taxa richness was lowest at elevated CO 2 and elevated N. Thus, enrichment of atmospheric CO 2 concentrations and N deposition may result in taxonomically and functionally altered, potentially simplified, soil communities. Detrimental effects of N deposition on soil biodiversity underscore recent reports on plant community simplification. This is of particular concern as soils house a considerable fraction of global biodiversity and ecosystem functions.

Beschreibung: A 3-D model for solving the radiation belt diffusion equation in adiabatic invariant coordinates has been developed and tested. The model, named REM (for Radbelt Electron Model), obtains a probabilistic solution by solving a set of Itô stochastic differential equations that are mathematically equivalent to the diffusion equation. This method is capable of solving diffusion equations with a full 3-D diffusion tensor, including the radial-local cross diffusion components. The correct form of the boundary condition at equatorial pitch-angle α 0  = 90° is also derived. The model is applied to a simulation of the October 2002 storm event. At α 0 near 90°, our results are quantitatively consistent with GPS observations of phase-space density (PSD) increases, suggesting dominance of radial diffusion; at smaller α 0 , the observed PSD increases are overestimated by the model, possibly due to the α 0 -independent radial diffusion coefficients, or to insufficientelectron loss in the model, or both. Statistical analysis of the stochastic processes provides further insights into the diffusion processes, showing distinctive electron source distributions with and without local acceleration.

Beschreibung: [1]  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 IRBEM library. The accuracy of all L ∗ neural networks with different underlying magnetic field models is evaluated by applying the PSD matching technique derived from the Liouville's 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 (MAD) around 0.15, regardless of the underlying magnetic field model. We found that such a 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.