Retrospective-Cost-Based Adaptive Input and State Estimation for the Ionosphere–Thermosphere

Abstract

The upper atmosphere is a strongly driven system in which the global state is rapidly altered by the solar drivers. One of the main drivers of the upper atmosphere is the solar irradiance in the extreme ultraviolet and x-ray bands. The solar irradiance in these bands is proxied by ground-based measurements of , which is the solar irradiance at the wavelength of 10.7 cm. The problem of estimating and physical states in the upper atmosphere is considered by assimilating the neutral density measurements in the global ionosphere–thermosphere model and using retrospective-cost adaptive input and state estimation. Retrospective-cost adaptive input and state estimation is a non-Bayesian estimator that estimates the input by minimizing the difference between the estimator output and the output of the physical system. In this paper, we use retrospective-cost adaptive input and state estimation to estimate using simulated data as well as real satellite data.