Methodology of evaluating the science benefit of various satellite/sensor constellation orbital parameters to an assimilative data forecast model

Abstract

AbstractA methodology for evaluating the science benefit of adding space weather sensor data from a modest number of small satellites to the Utah State University Global Assimilation of Ionospheric Measurements—Full Physics (GAIM‐FP) model is presented. Three orbital scenarios are presented, two focusing on improved coverage of narrowly specified regions of interest, and one on global coverage of the ionosphere as a whole. An Observing System Simulation Experiment is used to obtain qualitative and quantitative results of the impact of the various orbital scenarios on the ionospheric specifications. A simulated “truth” run of the ionosphere is obtained from a first principle model of the ionosphere/plasmasphere model and used to generate global simulated Global Positioning Satellite total electron content (GPS‐TEC) data as well as in situ plasma density observations. Initially, only GPS data were assimilated by GAIM‐FP, and the results of this limited run were compared to the truth run. Next, the simulated in situ plasma densities corresponding to our three orbital scenarios were assimilated together with the GPS data, and the results were compared to both the truth run and the limited GPS‐TEC only GAIM‐FP run. These model simulations have shown that adding a constellation of small satellites/sensors in addition to global TEC inputs does indeed converge the GAIM‐FP model closer to truth in the situations described.