Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data

Shi, Shuangshuang; Zhang, Kefei; Shi, Junxiang; Hu, Andong; Zhao, Dongsheng; Shi, Zhongchao; Sun, Peng; Wu, Huajing; Wu, Suqin

doi:10.1029/2022sw003357

Cited by 3 publications

(4 citation statements)

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“…The results indicate the potential applications of the GA-NN method in ionospheric studies. Shi et al [31] established a regional TEC model over China based on long-term ground-based and space-borne GNSS observations and the PSO-NN method. The results show that during the solar maximum year, the RMSE variation ranges for the PSO-NN-GRID model are 2.65-4.56 TECU, 2.73-6.02 TECU, and 4.71-9.54 TECU at geographic latitudes of 20 • -30 • N, 30 • -40 • N, and 40 • -50 • N, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…The flow chart depicting the particle swarm optimization neural networks algorithm is shown in Figure 2. To train the data within each spatial grid, neural networks are used with the same set of input and output parameters as the PSO-NN model [31]. The input parameters consist of sine and cosine components of the day of year (DOYs and DOYc), sine and cosine components of local time (HODs and HODc), geographic longitude, geographic latitude, F10.7, and Dst.…”

Section: Methodsmentioning

confidence: 99%

“…Before the establishment of the PSO-NN-GRID model, the calibration of space-borne RO observations to the ground-based GNSS observation level was achieved using particle swarm optimization and neural network techniques [31]. The data in each spatial grid are randomly divided into three data sets: a training data set (70%), a validation data set (15%), and a test data set (15%).…”

Section: Methodsmentioning

confidence: 99%

“…It can be seen from Figure 4 tion. The results of Shi et al [31] indicate that the correlation coefficients of the PSO-NN-GRID model on the four sets are 0.9537, 0.9539, 0.9538, and 0.9538, respectively, and the corresponding RMSEs are 3.91, 3.92, 3.91, and 3.91 TECU, respectively. Notably, the PSO-NN-GRID model outperforms the PSO-NN model, exhibiting higher correlation coefficients and lower RMSE values across these four data sets.…”

Section: Analysis On Different Sample Data Setsmentioning

confidence: 97%

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Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

Song,

Shi

2024

Atmosphere

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Analysis On Different Sample Data Setsmentioning

confidence: 97%

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Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

Song,

Shi

2024

Atmosphere

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Research on filling missing GNSS precipitable water vapor time series data using the PSORF model combined with reanalysis datasets

Wang,

Chai,

Zheng

et al. 2024

Meas. Sci. Technol.

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The inversion of precipitable water vapor (PWV) using the Global Navigation Satellite System (GNSS) has advantages such as all-weather observation, high precision, low cost, and high temporal resolution. Currently, long-term GNSS-PWV data has become an important data source for studying climate change. However, due to factors such as equipment failures, observation technology limitations, and estimation model errors, missing data and outliers often occur in real-time or post-processed PWV time series data. Furthermore, the main sources of GNSS-PWV errors are influenced by the atmospheric weighted mean temperature (Tm) and surface meteorological data (pressure and temperature). The results indicate that the European Centre for Medium⁃Range Weather Forecasts Reanalysis v5 (ERA5) dataset exhibits high accuracy in the Chinese region, making it suitable for GNSS-PWV inversion. By utilizing ERA5 meteorological data to calculate hourly GNSS-PWV and conducting accuracy assessments, it is demonstrated that the PWV inverted based on GNSS and ERA5 meteorological parameters possesses high precision. Based on this, this study selects GNSS stations from the Crustal Movement Observation Network of China (CMONOC) where the proportion of missing measured data is less than 8%. By combining ERA5, random forest (RF), and particle swarm optimization (PSO) algorithms, a new model called PSORF is proposed to fill in missing values in GNSS-PWV time series data. The research findings reveal that the R2 and RMSE of PSORF-PWV are 0.98 and 2.16 mm, respectively. Additionally, GNSS stations with more than 8% missing measured data are utilized to validate the accuracy of the PSORF model. A comparative analysis is conducted between the results obtained through the PSORF model and the ERA5-PWV acquired via traditional interpolation methods. The MAE and RMSE of PSORF-PWV are reduced by 21% and 17%, respectively, indicating that the PSORF model excels in filling missing data and effectively enhances the accuracy and reliability of PWV time series analysis. This study not only presents an effective approach for processing missing PWV data but also evaluates the applicability and accuracy of the ERA5 dataset in PWV inversion. This provides crucial technical support and data security for climate change research, short-term humidity field forecasting, and studies in related fields.

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The WOA–CNN–LSTM–Attention Model for Predicting GNSS Water Vapor

Yan,

Yang,

Gao

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data

Cited by 3 publications

References 50 publications

Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

Research on filling missing GNSS precipitable water vapor time series data using the PSORF model combined with reanalysis datasets

The WOA–CNN–LSTM–Attention Model for Predicting GNSS Water Vapor

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