Abstract
Abstract. This paper presents the first known application of multi-model ensembles to the forecasting of the thermosphere. A multi-model ensemble (MME) is a method for combining different, independent models. The main advantage of using an MME is to reduce the effect of model errors and bias, since it is expected that the model errors will, at least partly, cancel. The MME, with its reduced uncertainties, can then be used as the initial conditions in a physics-based thermosphere model for forecasting. This should increase the forecast skill since a reduction in the errors of the initial conditions of a model generally increases model skill. In this paper the Thermosphere–Ionosphere Electrodynamic General Circulation Model (TIE-GCM), the US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Exosphere 2000 (NRLMSISE-00), and Global Ionosphere–Thermosphere Model (GITM) have been used to construct the MME. As well as comparisons between the MMEs and the “standard” runs of the model, the MME densities have been propagated forward in time using the TIE-GCM. It is shown that thermospheric forecasts of up to 6 h, using the MME, have a reduction in the root mean square error of greater than 60 %. The paper also highlights differences in model performance between times of solar minimum and maximum.
Highlights
1.1 BackgroundNASA predicts that, by 2030, orbital collisions could become frequent enough to cause a cascade (Kessler et al, 2010), with the potential to prevent the use of low Earth orbit (LEO) (Koller, 2012)
To compare NRLMSISE-00, Thermosphere–Ionosphere Electrodynamic General Circulation Model (TIE-GCM), and Global Ionosphere– Thermosphere Model (GITM) with CHAllenging Minisatellite Payload (CHAMP), the output of each model was spatially mapped to the CHAMP position using tri-linear interpolation
The work presented in this study shows the possibility of using multi-model ensembles (MMEs) to enhance the forecast skill of thermospheric models
Summary
NASA predicts that, by 2030, orbital collisions could become frequent enough to cause a cascade (Kessler et al, 2010), with the potential to prevent the use of low Earth orbit (LEO) (Koller, 2012). The upper atmosphere forecast models currently in use for orbit prediction are empirical and include US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Exosphere 2000 (NRLMSISE-00), the Jacchia Reference Atmosphere (Jacchia, 1977), and the NASA/MSFC Global Reference Atmospheric Model-1999 Version (Justus and Johnson, 1999). They are finely tuned, but when applied to satellite orbit forecasts they can result in large uncertainties in the orbital parameters.
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