Uncertainty quantification of the DTM2020 thermosphere model

Claude Boniface,Sean Bruinsma

doi:10.1051/swsc/2021034

Claude Boniface, Sean Bruinsma

Open Access

https://doi.org/10.1051/swsc/2021034

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Abstract

Aims: The semi-empirical Drag Temperature Models (DTM) calculate the Earth’s upper atmosphere’s temperature, density, and composition. They were applied mainly for spacecraft orbit computation. We developed an uncertainty tool that we implemented in the DTM2020 thermosphere model. The model is assessed and compared with the recently HASDM neutral density released publicly in 2020.Methods: The total neutral density dataset covers all high-resolution CHAMP, GRACE, GOCE, and SWARM data spanning almost two solar cycles. We constructed the uncertainty model using statistical binning analysis and least-square fitting techniques, allowing the development of a global sigma error model to function the main variabilities driving the thermosphere state. The model is represented mathematically by a nonlinear manifold approximation in a 6-D space parameter.Results: The results reveal that the altitude parameter presents the most notable error range during quiet and moderate magnetic activity (Kp ≤ 5). However, the most considerable uncertainty appears during severe or extreme geomagnetic activities. The comparison with density data provided by the SET HASDM database highlights some coherent features on the mechanisms occurring in the thermosphere. Moreover, it confirms the tool’s relevance to provide a qualitative database of neutral densities uncertainties values deduced from the DTM2020 model.

Highlights

The thermosphere operational and research DTM2020 (Drag Temperature Model) models were developed in the framework of the Space Weather Atmosphere Models and Indices project (SWAMI, http://swami-h2020.eu), which was a European Union Horizon 2020 project
We developed a semianalytical model to characterize the uncertainty of atmospheric modeling for DTM2020 as a function of location, season, and two indices (F10.7 and Kp) describing the solar and geomagnetic activities
Altitude is one of the main drivers in error modeling during quiet and disturbed thermosphere states, and so this parameter is compared with the other physical quantities

Summary

Introduction

The thermosphere operational and research DTM2020 (Drag Temperature Model) models were developed in the framework of the Space Weather Atmosphere Models and Indices project (SWAMI, http://swami-h2020.eu), which was a European Union Horizon 2020 project. Semi-empirical thermosphere specification models are used to compute the atmospheric drag force in the orbit prediction of objects in Low Earth Orbit (LEO). Propagation of the neutral density uncertainties to orbital state uncertainties has received little attention, but awareness for this theme is growing (e.g., Vallado & Finkleman, 2014; Emmert et al, 2017; Schiemenz et al, 2019; Lopez-Jimenez et al, 2021). The uncertainty in the thermospheric density modeling is generally 10–15% (Vallado & Finkleman, 2014) for a quiet thermosphere state. It can increase rapidly depending on the solar and geomagnetic activity, notably in the case of geomagnetic

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