We present our latest result of assimilating radiation belt data into a radiation belt diffusion model using an ensemble Kalman filter. The state vector in the Kalman filter is augmented by the outer boundary condition as a free parameter that can be estimated by the filter. We find that while the outer boundary stays mostly low, the geosynchronous regions shows strong enhancements in phase space densities. These enhancements cannot be explained by radial inward diffusion but by local acceleration.

We use the Kalman filter and the probability distribution of the forecast ensemble to identify regions and time periods where the model is drifting away from the observations. Although our model does not contain a source/loss term, the Kalman filter algorithm can add very localized sources and losses. We determine that most of the source is added between L*=4-6.

We are also working on a so-called re-analysis to fill spacial and temporal gaps in the spacecraft coverage. The figure shows such a re-analysis map for the second half of 2002 as an L* versus time plot. The color represents the phase space densities of the assimilated state on a logarithmic scale. White dots show the locations where satellite data was available. We assimilated data from five satellites: POLAR, GPS ns-41, and three LANL geosynchronous satellites: LANL-97a, 1991-080, 1990-095. We plan to extend the the dataset to include many more spacecraft covering a whole solar cycle. The resulting re-analysis maps will provide global statistical properties that allow us to predict orbital dosages and event probabilities for future space missions superseding existing models like AE-8 and AP-8.

The presented work is part of a larger "Dynamic Radiation Environment Assimilation Model" (DREAM) at Los Alamos National Laboratory. DREAM is a Laboratory Directed Research and Development project that will develop a next-generation space radiation model using extensive satellite measurement, new theoretical insights, global physics-based magnetospheric models, and the techniques of data assimilation.

Supplementary URL: http://www.lanl.gov/projects/dream/