Improved forecasting of thermospheric densities using multi-model ensembles

Sean Elvidge, Humberto Godinez, Matthew Angling

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
150 Downloads (Pure)


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 uncertain- ties, 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. TIE-GCM has been used for forecasting. It has been shown that thermospheric forecasts of up to 6 hours, that have been initialised using the MME, have a reduction in the root mean square error (compared to "standard" runs of the model) of greater than 60%. The paper also highlights differences in model performance between times of solar minimum and maximum.
Original languageEnglish
Pages (from-to)2279-2292
JournalGeoscientific Model Development
Issue number6
Publication statusPublished - 1 Jul 2016


  • Modelling
  • Thermosphere
  • drag


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