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Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach.
- Source :
- Atmosphere; Mar2024, Vol. 15 Issue 3, p351, 19p
- Publication Year :
- 2024
-
Abstract
- Precise regional ionospheric total electron content (TEC) models play a crucial role in correcting ionospheric delays for single-frequency receivers and studying variations in the Earth's space environment. A particle swarm optimization neural network (PSO-NN)-based model for ionospheric TEC over China has been developed using a long-term (2008–2021) ground-based global positioning system (GPS), COSMIC, and Fengyun data under geomagnetic quiet conditions. In this study, a spatial gridding approach is utilized to propose an improved version of the PSO-NN model, named the PSO-NN-GRID. The root-mean-square error (RMSE) and mean absolute error (MAE) of the TECs estimated from the PSO-NN-GRID model on the test data set are 3.614 and 2.257 TECU, respectively, which are 7.5% and 5.5% smaller than those of the PSO-NN model. The improvements of the PSO-NN-GRID model over the PSO-NN model during the equinox, summer, and winter of 2015 are 0.4–22.1%, 0.1–12.8%, and 0.2–26.2%, respectively. Similarly, in 2019, the corresponding improvements are 0.5–13.6%, 0–10.1%, and 0–16.1%, respectively. The performance of the PSO-NN-GRID model is also verified under different solar activity conditions. The results reveal that the RMSEs for the TECs estimated by the PSO-NN-GRID model, with F10.7 values ranging within [0, 80), [80, 100), [100, 130), [130, 160), [160, 190), [190, 220), and [220, +), are, respectively, 1.0%, 2.8%, 4.7%, 5.5%, 10.1%, 9.1%, and 28.4% smaller than those calculated by the PSO-NN model. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20734433
- Volume :
- 15
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Atmosphere
- Publication Type :
- Academic Journal
- Accession number :
- 176270346
- Full Text :
- https://doi.org/10.3390/atmos15030351