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

The upper atmosphere is a strongly driven system in which the global state is rapidly altered by the solar drivers. Oneof themaindrivers of theupper atmosphere is the solar irradiance in the extremeultraviolet andx-raybands.The solar irradiance in these bands is proxied by ground-basedmeasurements ofF10.7, which is the solar irradiance at the wavelength of 10.7 cm. The problem of estimating F10.7 and physical states in the upper atmosphere is considered by assimilating the neutral densitymeasurements in the global ionosphere–thermospheremodel andusing retrospectivecost 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 F10.7 using simulated data as well as real satellite data.