Articles | Volume 9, issue 6
https://doi.org/10.5194/gmd-9-2279-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-2279-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
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 Elvidge
CORRESPONDING AUTHOR
Space Environment and Radio Engineering Group, University of Birmingham, Birmingham, UK
Humberto C. Godinez
Los Alamos National Laboratory, Los Alamos, NM, USA
Matthew J. Angling
Space Environment and Radio Engineering Group, University of Birmingham, Birmingham, UK
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Cited
14 citations as recorded by crossref.
- Multi‐Model Ensembles for Upper Atmosphere Models S. Elvidge et al. 10.1029/2022SW003356
- Ionosphere variability II: Advances in theory and modeling I. Tsagouri et al. 10.1016/j.asr.2023.07.056
- Ionospheric tomography by gradient-enhanced kriging with STEC measurements and ionosonde characteristics D. Minkwitz et al. 10.5194/angeo-34-999-2016
- Thermosphere and satellite drag S. Bruinsma et al. 10.1016/j.asr.2023.05.011
- Improved orbit prediction of LEO objects with calibrated atmospheric mass density model J. Chen et al. 10.1080/14498596.2017.1371089
- Review and comparison of empirical thermospheric mass density models C. He et al. 10.1016/j.paerosci.2018.10.003
- Machine learning in orbit estimation: A survey F. Caldas & C. Soares 10.1016/j.actaastro.2024.03.072
- The First Comparison Between Swarm‐C Accelerometer‐Derived Thermospheric Densities and Physical and Empirical Model Estimates T. Kodikara et al. 10.1029/2017JA025118
- Empirical Data Assimilation for Merging Total Electron Content Data with Empirical and Physical Models E. Forootan et al. 10.1007/s10712-023-09788-7
- Daedalus MASE (mission assessment through simulation exercise): A toolset for analysis of in situ missions and for processing global circulation model outputs in the lower thermosphere-ionosphere T. Sarris et al. 10.3389/fspas.2022.1048318
- The Impact of Solar Activity on Forecasting the Upper Atmosphere via Assimilation of Electron Density Data T. Kodikara et al. 10.1029/2020SW002660
- Climate, weather, space weather: model development in an operational context D. Folini 10.1051/swsc/2018021
- Quantifying the Storm Time Thermospheric Neutral Density Variations Using Model and Observations E. Kalafatoglu Eyiguler et al. 10.1029/2018SW002033
- First Time Estimation of Thermospheric Neutral Density Profiles From Seed Perturbations of ESF Triggering: A Novel Evidence for Ionosphere Thermosphere Coupling G. Manju & R. Aswathy 10.1029/2018JA025967
14 citations as recorded by crossref.
- Multi‐Model Ensembles for Upper Atmosphere Models S. Elvidge et al. 10.1029/2022SW003356
- Ionosphere variability II: Advances in theory and modeling I. Tsagouri et al. 10.1016/j.asr.2023.07.056
- Ionospheric tomography by gradient-enhanced kriging with STEC measurements and ionosonde characteristics D. Minkwitz et al. 10.5194/angeo-34-999-2016
- Thermosphere and satellite drag S. Bruinsma et al. 10.1016/j.asr.2023.05.011
- Improved orbit prediction of LEO objects with calibrated atmospheric mass density model J. Chen et al. 10.1080/14498596.2017.1371089
- Review and comparison of empirical thermospheric mass density models C. He et al. 10.1016/j.paerosci.2018.10.003
- Machine learning in orbit estimation: A survey F. Caldas & C. Soares 10.1016/j.actaastro.2024.03.072
- The First Comparison Between Swarm‐C Accelerometer‐Derived Thermospheric Densities and Physical and Empirical Model Estimates T. Kodikara et al. 10.1029/2017JA025118
- Empirical Data Assimilation for Merging Total Electron Content Data with Empirical and Physical Models E. Forootan et al. 10.1007/s10712-023-09788-7
- Daedalus MASE (mission assessment through simulation exercise): A toolset for analysis of in situ missions and for processing global circulation model outputs in the lower thermosphere-ionosphere T. Sarris et al. 10.3389/fspas.2022.1048318
- The Impact of Solar Activity on Forecasting the Upper Atmosphere via Assimilation of Electron Density Data T. Kodikara et al. 10.1029/2020SW002660
- Climate, weather, space weather: model development in an operational context D. Folini 10.1051/swsc/2018021
- Quantifying the Storm Time Thermospheric Neutral Density Variations Using Model and Observations E. Kalafatoglu Eyiguler et al. 10.1029/2018SW002033
- First Time Estimation of Thermospheric Neutral Density Profiles From Seed Perturbations of ESF Triggering: A Novel Evidence for Ionosphere Thermosphere Coupling G. Manju & R. Aswathy 10.1029/2018JA025967
Latest update: 21 Nov 2024
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 %.
This paper presents the first known application of multi-model ensembles to the forecasting of...
