Articles | Volume 9, issue 6
Development and technical paper
01 Jul 2016
Development and technical paper |  | 01 Jul 2016

Improved forecasting of thermospheric densities using multi-model ensembles

Sean Elvidge, Humberto C. Godinez, and Matthew J. Angling

Related authors

Improved model for correcting the ionospheric impact on bending angle in radio occultation measurements
Matthew J. Angling, Sean Elvidge, and Sean B. Healy
Atmos. Meas. Tech., 11, 2213–2224,,, 2018
Short summary

Related subject area

Solar-terrestrial science
SSolar-GOA v1.0: a simple, fast, and accurate Spectral SOLAR radiative transfer model for clear skies
Victoria Eugenia Cachorro, Juan Carlos Antuña-Sanchez, and Ángel Máximo de Frutos
Geosci. Model Dev., 15, 1689–1712,,, 2022
Short summary
Application of CCM SOCOL-AERv2-BE to cosmogenic beryllium isotopes: description and validation for polar regions
Kseniia Golubenko, Eugene Rozanov, Gennady Kovaltsov, Ari-Pekka Leppänen, Timofei Sukhodolov, and Ilya Usoskin
Geosci. Model Dev., 14, 7605–7620,,, 2021
Short summary
UBER v1.0: a universal kinetic equation solver for radiation belts
Liheng Zheng, Lunjin Chen, Anthony A. Chan, Peng Wang, Zhiyang Xia, and Xu Liu
Geosci. Model Dev., 14, 5825–5842,,, 2021
Short summary
Azimuthal averaging–reconstruction filtering techniques for finite-difference general circulation models in spherical geometry
Tong Dang, Binzheng Zhang, Jiuhou Lei, Wenbin Wang, Alan Burns, Han-li Liu, Kevin Pham, and Kareem A. Sorathia
Geosci. Model Dev., 14, 859–873,,, 2021
Short summary
Accounting for anthropic energy flux of traffic in winter urban road surface temperature simulations with the TEB model
A. Khalifa, M. Marchetti, L. Bouilloud, E. Martin, M. Bues, and K. Chancibaut
Geosci. Model Dev., 9, 547–565,,, 2016
Short summary

Cited articles

Burrell, A. G., Goel, A., Ridley, A. J., and Bernstein, D. S.: Correction of the photoelectron heating efficiency within the global ionosphere-thermosphere model using Retrospective Cost Model Refinement, J. Atmos. Sol.-Terr. Phys., 124, 30–38,, 2015.
Christensen, J. H., Kjellström, E., Giorgi, F., Lenderink, G., and Rummukainen, M.: Weight assignment in regional climate models, Climate Res., 44, 179–194,, 2010.
Doblas-Reyes, F. J., Déqué, M., and Piedelievre, J.-P.: Multi-model spread and probabilistic seasonal forecasts in PROVOST, Q. J. Roy. Meteor. Soc., 126, 2069–2087, 2000.
Elvidge, S., Angling, M. J., and Nava, B.: On the Use of Modified Taylor Diagrams to Compare Ionospheric Assimilation Models, Radio Science, 49, 737–745, 2014.
Emery, B. A., Coumans, V., Evans, D. S., Germany, G. A., Greer, M. S., Holeman, E., Kadinsky-Cade, K., Rich, F. J., and Xu, W.: Seasonal, Kp, solar wind, and solar flux variations in long-term single-pass satellite estimates of electron and ion auroral hemispheric power, J. Geophys. Res., 113, A06311,, 2008.
Short summary
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 %.