Accurate determination of the ionospheric parameters is one of the important objectives of the Ionospheric Connection Explorer (ICON) mission. Recent analyses of the current ICON Level 2.5 (L2.5) data product have shown that the ionospheric parameters (e.g., the peak electron density, $n_{\mathrm{m}}F_{2}$ , and the peak height, $h_{\mathrm{m}}F_{2}$ ) that are retrieved from the nighttime OI 135.6 nm emission observed by ICON’s Far Ultraviolet (FUV) imager exhibit a systematic bias when compared to external radio measurements. In this study, we demonstrate that the bias was introduced by Tikhonov regularization that was used for the FUV Level 1 data inversion to generate the L2.5 data product. To address the bias, we develop a Bayesian framework for accurate determination of the nighttime ionospheric parameters through the Maximum A Posteriori (MAP) estimation. We show through analysis of synthetic observations that the key to an accurate MAP estimation is to construct a series of prior distributions associated with different $h_{\mathrm{m}}F_{2}$ using climatological empirical models. Implementation of the MAP estimation with this series of prior distributions to the ICON FUV observations and comparison of the ionospheric retrievals with external radio measurements verify that the Bayesian method can reduce the systematic bias to a negligible level of ∼1% in the retrieved $n_{\mathrm{m}}F_{2}$ and ∼1 km in the retrieved $h_{\mathrm{m}}F_{2}$ . Our study provides a novel method for FUV remote sensing data analysis and an improved data set for ionospheric research.
