Development of Atmospheric Density Calibration and Preliminary Results

Abstract

This paper proposes a systematic method to calibrate and predict atmospheric densities up to 5 days ahead only from publicly available data. The proposed method consists of calibration of an empirical atmospheric density model and prediction of the calibrated model. The calibration is based on dynamic calibration of atmosphere, which dynamically assimilates the empirical model with orbit-related measurements. The calibrated model is compared with accelerometer-derived densities of the CHAMP satellite, and it is confirmed that errors relative to empirical models decrease by about 17-34 % on a high solar activity day. For the prediction, this paper adopts the Hermitian-Space Dynamic Mode Decomposition with control algorithm, which is a data-driven model discovery approach considering exogenous inputs. Prior studies use time series of density data as input for the algorithm, whereas the proposed method uses time series of density data and its time derivative. The proposed prediction method is validated against accelerometer-derived densities of the CHAMP and GRACE satellites. The proposed prediction method improved errors relative to empirical models by about 26-28 % to forecast densities 5 days ahead. The development of accurate density forecast model in this paper will contribute to the safety of satellite's operations in this congested and contested space environment.