Removing Orbital Variations From Low Altitude Particle Data: Method and Application

Abstract

Particle flux measurements from polar orbiting low altitude satellites provide a view of the near Earth radiation environment that is extremely valuable for science as well as space weather monitoring. Unlike, geosynchronous satellites that sample only a limited region of space (L = ∼6.6), these low altitude satellites sample the extended radiation environment (L = 1 to >10) at a relatively high time cadence (tens of minutes) that captures its global evolution. While these data are clearly useful, it is also challenging to work with because the particle flux measurements have large orbital variations related to the changing geographic location of the satellites. These orbital flux variations can sometimes obscure the time variations of interest for scientific study or space weather hazard awareness. Here, we describe and evaluate a method for removing these variations that is based on Statistical Asynchronous Regression. We demonstrate the utility and accuracy of the method by applying it to electron flux measurements from the NOAA POES and EUMetSat MetOp low altitude satellites.