Static and Dynamic Model Calibration for Upper Thermosphere Determination
Abstract Thermospheric density, a crucial factor in spacecraft operations, poses a significant challenge in accurate determination due to the intricate coupling of the thermosphere‐ionosphere system. Despite capturing long‐term trends, the widely used empirical models, which are used for low Earth o...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | Space Weather |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024SW003986 |
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| Summary: | Abstract Thermospheric density, a crucial factor in spacecraft operations, poses a significant challenge in accurate determination due to the intricate coupling of the thermosphere‐ionosphere system. Despite capturing long‐term trends, the widely used empirical models, which are used for low Earth orbit spacecraft operations, often fail to reproduce small‐scale or short‐term variations in the thermosphere. This work presents a novel approach for model improvement. The background model employed is our recently developed empirical model, which blends numerical simulations and satellite measurements. The model residuals are compiled into longitude and latitude bins, and the corresponding basic modes of describing variabilities are subsequently derived via the PCA method. The attendant amplitudes exhibit significant local time and seasonal dependencies, motivating optimized parameterization, that is, static calibration. Moreover, the present study reveals that the most dominant mode correlates to land‐sea contrasts and manifests global synchronicity upon excluding local time and seasonal dependences. This establishes the real‐time model adjustment foundation by exploiting limited calibration observations, which is referred to as dynamic calibration. The results from the dynamic calibration model indicated considerably improved thermospheric representation, especially for small‐scale or short‐term variations, toward a better thermospheric prediction. |
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| ISSN: | 1542-7390 |