Ionospheric TEC data assimilation based on Gauss–Markov Kalman filter

Qiao, Jiandong; Liu, Yi; Fan, Zhiqiang; Tang, Qiong; Li, Xiaojun; Zhang, Fubin; Song, Yang; He, Fang; Zhou, Chen; Qing, Haiyin; Li, Zhengqiang

doi:10.1016/j.asr.2021.08.004

Cited by 12 publications

(12 citation statements)

References 28 publications

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“…where X f t+1 is the next moment forecast value, ∆t is the time step, and τ denotes the ionospheric time dependent scale. The relevant scale factor can be written as τ = 1/4 * cos(π/12 * (t − 14)) + 3/4 [20].…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

“…Pasumarthi and Devanaboyina [19] used the Kalman filter method to generate hourly assimilated Indian regional TEC maps by the process of data assimilation. Qiao et al [20] used the Gauss-Markov Kalman filter to develop a TEC model in China and adjacent regions. Compared with the Kalman filter algorithm, the advantage of the ensemble Kalman filter (EnKF) is that the error statistics are calculated from an ensemble of the forward model forecasts which run in parallel [21].…”

Section: Introductionmentioning

confidence: 99%

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Ionospheric Assimilation of GNSS TEC into IRI Model Using a Local Ensemble Kalman Filter

et al. 2022

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Ionospheric total electron content (TEC) is important data for ionospheric morphology, and also an important parameter for ionospheric correction in Global Navigation Satellite System (GNSS) precise positioning, navigation, and radio science. In this study, we present a data assimilation model for regional ionosphere based on a local ensemble Kalman filter (LEnKF) with the International Reference Ionosphere 2016 (IRI-2016) model as the background, to assimilate ionospheric TEC observations from GNSS. To demonstrate the results, the TEC estimates from the Crustal Movement Observation Network of China (CMONOC), which is about 260 stations in China, are applied as observation. The assessments are performed against the TEC estimates from BeiDou Navigation Satellite System (BDS) geostationary earth orbit (GEO) and against the final products from the Center for Orbit Determination in Europe (CODE). The assimilation results are in good agreement with BDS GEO TEC and the CODE TEC on a quiet or disturbed day. The correlation coefficient after assimilation is increased by about 17% compared with that before assimilation, and the RMSE after assimilation is decreased by about 42% compared with that before assimilation. Furthermore, the assimilated method is also evaluated in the single-frequency precise point positioning (PPP). The experimental results indicate that the PPP/Assimilated method can improve the GNSS positioning accuracy more effectively in comparison to the PPP/CODE. These results reveal that the LEnKF method can be considered as a useful tool for ionospheric assimilation.

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Section: Ensemble Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ionospheric Assimilation of GNSS TEC into IRI Model Using a Local Ensemble Kalman Filter

et al. 2022

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“…Therefore, whether the error of the background field and observation field can be accurately described significantly affects the results of the assimilation experiment. In previous studies, the errors among the observed data are considered to be uncorrelated and unbiased, and the observation error is considered proportional to the square of the observation value [6,15,19,25,26]. Therefore, the observation-field error covariance matrix R is the diagonal matrix, and the element of the diagonal is proportional to the observation square.…”

Section: Kalman Filtering Algorithmmentioning

confidence: 99%

“…The empirical model does not include the physical process of ionospheric change; thus, it is unable to predict the ionosphere. Its prediction is usually realized by other algorithms, such as the Gaussian Markov algorithm [15]. However, due to its simple deployment and high computational efficiency, empirical models are also widely used in many fields.…”

Section: Introduction 1ionosphere Data Assimilationmentioning

confidence: 99%

Ionospheric Kalman Filter Assimilation Based on Covariance Localization Technique

et al. 2022

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The data assimilation algorithm is a common algorithm in space weather research. Based on the GNSS data from the China Crustal Movement Observation Network (CMONOC) and the International Reference Ionospheric Model (IRI), a fast three-dimensional (3D) electron density nowcasting model for China and its adjacent regions was developed. Unlike the traditional Gaussian background covariance model, the error covariance of the IRI model, based on the IGS grid TEC data, is estimated in this work. Due to the large scale of the high-resolution covariance matrix, it cannot be stored and calculated directly on a personal computer. The covariance localization (CL) technique is introduced to sparse the covariance matrix while removing the pseudo-correlation of the covariance matrix. After localization, the covariance matrix can be converted into a sparse matrix for storage and calculation, which greatly reduces the computer memory requirement of the assimilation model and improves the calculation speed of the model. Based on this algorithm, a series of experiments were carried out in this work. The experimental results show that this algorithm can effectively assimilate the observed GNSS to the background field, make up for the temporal and spatial limitations of the observed data, and improve the accuracy of the ionospheric electron density nowcast. Compared with the digisonde observed foF2 (the critical frequency of the ionospheric F2 layer), the RMSE of the assimilation model is 0.44 MHz lower than that of the IRI model.

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“…Atmosphere 2022, 13, 1039 2 of 19 There are two main methods for short-term ionospheric prediction. One is the artificial neural network method (ANN) based on a large number of observation data, and the other is the data assimilation method, which combines observation data with a theoretical ionospheric model [9,10].…”

Section: Introductionmentioning

confidence: 99%

Ionospheric TEC Prediction Base on Attentional BiGRU

Lei

Liu

et al. 2022

Atmosphere

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Many studies indicated that ionospheric total electron content (TEC) prediction is vital for terrestrial and space-based radio-communication systems. In previous TEC prediction schemes based on RNN, they learn TEC representations from previous time steps, and each time-step made an equal contribution to a prediction. To overcome these drawbacks, we propose two improvements in our study: (1) To predict TEC with both past and future time-step, Bidirectional Gate Recurrent Unit (BiGRU) was presented to improve the capabilities. (2) To highlight critical time-step information, attention mechanism was used to provide weights to each time-step. The proposed attentional BiGRU TEC predicting method was evaluated on the publicly available data set from the Centre for Orbit Determination in Europe. We chose three geographical locations in low latitude, middle latitude, and high latitude to verify the performance of our proposed model. Comparative experiments were conducted using Deep Neural Network (DNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Term memory (BiLSTM), and Gated Recurrent Unit (GRU). Experimental results show that the proposed Attentional BiGRU model is superior to the other models in the selected nine regions. In addition, the paper discussed the effects of latitudes and solar activities on the performance of Attentional BiGRU model. Experimental results show that the higher the latitude, the higher the prediction accuracy of our proposed model. Experimental results also show that in the middle latitude, the prediction accuracy of the model is less affected by solar activity, and in other areas, the model is greatly affected by solar activity.

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Ionospheric TEC data assimilation based on Gauss–Markov Kalman filter

Cited by 12 publications

References 28 publications

Ionospheric Assimilation of GNSS TEC into IRI Model Using a Local Ensemble Kalman Filter

Ionospheric Assimilation of GNSS TEC into IRI Model Using a Local Ensemble Kalman Filter

Ionospheric Kalman Filter Assimilation Based on Covariance Localization Technique

Ionospheric TEC Prediction Base on Attentional BiGRU

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