Abstract

Abstract. In situ satellite exploration of the lower thermosphere–ionosphere system (LTI) as anticipated in the recent Daedalus mission proposal to ESA will be essential to advance the understanding of the interface between the Earth's atmosphere and its space environment. To address physical processes also below perigee, in situ measurements are to be extrapolated using models of the LTI. Motivated by the need for assessing how cost-critical mission elements such as perigee and apogee distances as well as the number of spacecraft affect the accuracy of scientific inference in the LTI, the Daedalus Ionospheric Profile Continuation (DIPCont) project is concerned with the attainable quality of in situ measurement extrapolation for different mission parameters and configurations. This report introduces the methodological framework of the DIPCont approach. Once an LTI model is chosen, ensembles of model parameters are created by means of Monte Carlo simulations using synthetic measurements based on model predictions and relative uncertainties as specified in the Daedalus Report for Assessment. The parameter ensembles give rise to ensembles of model altitude profiles for LTI variables of interest. Extrapolation quality is quantified by statistics derived from the altitude profile ensembles. The vertical extent of meaningful profile continuation is captured by the concept of extrapolation horizons defined as the boundaries of regions where the deviations remain below a prescribed error threshold. To demonstrate the methodology, the initial version of the DIPCont package presented in this paper contains a simplified LTI model with a small number of parameters. As a major source of variability, the pronounced change in temperature across the LTI is captured by self-consistent non-isothermal neutral-density and electron density profiles, constructed from scale height profiles that increase linearly with altitude. The resulting extrapolation horizons are presented for dual-satellite measurements at different inter-spacecraft distances but also for the single-satellite case to compare the two basic mission scenarios under consideration. DIPCont models and procedures are implemented in a collection of Python modules and Jupyter notebooks supplementing this report.

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