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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 6
Geosci. Model Dev., 9, 2279-2292, 2016
https://doi.org/10.5194/gmd-9-2279-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 9, 2279-2292, 2016
https://doi.org/10.5194/gmd-9-2279-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 01 Jul 2016

Development and technical paper | 01 Jul 2016

Improved forecasting of thermospheric densities using multi-model ensembles

Sean Elvidge1, Humberto C. Godinez2, and Matthew J. Angling1 Sean Elvidge et al.
  • 1Space Environment and Radio Engineering Group, University of Birmingham, Birmingham, UK
  • 2Los Alamos National Laboratory, Los Alamos, NM, USA

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 6h, 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.

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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. This paper shows that use of MMEs for forecasting thermospheric densities can reduce errors by 60 %.
This paper presents the first known application of multi-model ensembles to the forecasting of...
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