Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models
Abstract The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce...
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| Format: | Article |
| Language: | English |
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Wiley
2023-05-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003345 |
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| _version_ | 1841536484979507200 |
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| author | Richard J. Licata Piyush M. Mehta |
| author_facet | Richard J. Licata Piyush M. Mehta |
| author_sort | Richard J. Licata |
| collection | DOAJ |
| description | Abstract The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce a density response, but these models are typically computationally expensive or inaccurate for certain space weather conditions. In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE‐GCM), a physics‐based thermosphere model. Our method leverages principal component analysis to reduce the dimensionality of TIE‐GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. The newly developed reduced order probabilistic emulator (ROPE) uses Long‐Short Term Memory neural networks to perform time‐series forecasting in the reduced state and provide distributions for future density. We show that across the available data, TIE‐GCM ROPE has similar error to previous linear approaches while improving storm‐time modeling. We also conduct a satellite propagation study for the significant November 2003 storm which shows that TIE‐GCM ROPE can capture the position resulting from TIE‐GCM density with <5 km bias. Simultaneously, linear approaches provide point estimates that can result in biases of 7–18 km. |
| format | Article |
| id | doaj-art-5cddebd60bca4e0aa80af9228b792a1d |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-5cddebd60bca4e0aa80af9228b792a1d2025-01-14T16:26:43ZengWileySpace Weather1542-73902023-05-01215n/an/a10.1029/2022SW003345Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere ModelsRichard J. Licata0Piyush M. Mehta1Department of Mechanical and Aerospace Engineering West Virginia University Morgantown WV USADepartment of Mechanical and Aerospace Engineering West Virginia University Morgantown WV USAAbstract The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce a density response, but these models are typically computationally expensive or inaccurate for certain space weather conditions. In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE‐GCM), a physics‐based thermosphere model. Our method leverages principal component analysis to reduce the dimensionality of TIE‐GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. The newly developed reduced order probabilistic emulator (ROPE) uses Long‐Short Term Memory neural networks to perform time‐series forecasting in the reduced state and provide distributions for future density. We show that across the available data, TIE‐GCM ROPE has similar error to previous linear approaches while improving storm‐time modeling. We also conduct a satellite propagation study for the significant November 2003 storm which shows that TIE‐GCM ROPE can capture the position resulting from TIE‐GCM density with <5 km bias. Simultaneously, linear approaches provide point estimates that can result in biases of 7–18 km.https://doi.org/10.1029/2022SW003345thermosphereensembleLSTM |
| spellingShingle | Richard J. Licata Piyush M. Mehta Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models Space Weather thermosphere ensemble LSTM |
| title | Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models |
| title_full | Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models |
| title_fullStr | Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models |
| title_full_unstemmed | Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models |
| title_short | Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models |
| title_sort | reduced order probabilistic emulation for physics based thermosphere models |
| topic | thermosphere ensemble LSTM |
| url | https://doi.org/10.1029/2022SW003345 |
| work_keys_str_mv | AT richardjlicata reducedorderprobabilisticemulationforphysicsbasedthermospheremodels AT piyushmmehta reducedorderprobabilisticemulationforphysicsbasedthermospheremodels |