Accurate space weather predictions will require models that can assimilate real-time data. Kalman filtering requires that the uncertainties in the data and the models must be quantitatively understood. The main goal of this project is to systematically study the uncertainties in the various data sets that are used in radiation belt modeling and also to study the inherent uncertainties in the models themselves. Once the uncertainties are understood it will be possible to use Kalman filtering techniques to enhance the quality of predictive radiation belt models. The project will leverage work being done to create the Los Alamos Dynamic Radiation Environment Assimilation Model (DREAM). The ultimate goal is to produce an accurate, data assimilative, predictive model of the radiations belts that can be used by the NOAA Space Environment Center.
The project also includes outreach to high school students through the Los Alamos Space Science Outreach program
