Los Alamos National Laboratory
Phone| Search
T-5 HomeResearchPublications › Elvidge-Thermosphere-2016
› Contact › People › Research
› Projects › Highlights › Publications
› Jobs › Visitor Info

Cite Details

Sean Elvidge, Humberto C. Godinez and Matthew J. Angling, " Improved Forecasting of Thermospheric Densities using Multi-Model Ensembles ", Geosci. Model Dev., vol. 9, pp. 2279--2292, 2016


This paper presents the first known application of multi-model ensembles to the forecasting of the thermo- sphere. 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 con- ditions 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

BibTeX Entry

author = {Sean Elvidge and Humberto C. Godinez and Matthew J. Angling},
title = { Improved Forecasting of Thermospheric Densities using Multi-Model Ensembles },
year = {2016},
journal = {Geosci. Model Dev.},
volume = {9},
pages = {2279--2292}