Improved IRI-2016 model based on BeiDou GEO TEC ingestion across China

Chen, Ming; Liu, Lei; Xu, Chaoqian; Wang, Youkun

doi:10.1007/s10291-019-0938-8

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…In recent years, ionospheric data assimilation has become an important method for obtaining high-precision ionospheric TEC with the improvement of the global navigation satellite system (GNSS) and the increase in the number of ground-based observation networks [4][5][6][7][8][9][10][11][12][13]. The commonly used assimilation methods are mainly divided into two categories: the variational method and the Kalman filter method.…”

Section: Introductionmentioning

confidence: 99%

Assimilating GNSS TEC with an LETKF over Yunnan, China

et al. 2023

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A robust ionospheric model is indispensable for providing the atmospheric delay corrections for global navigation satellite system (GNSS) navigation and positioning and forecasting the space environment. The accuracy of ionospheric models is limited due to the simplified model structures. Complicated spatiotemporal variations in total electron content (TEC) biases between GNSS and international reference ionosphere (IRI) suggest a robust strategy to optimally combine GNSS and IRI TEC for high-precision modeling. In this paper, we propose a novel ionospheric data assimilation method, which is a local ensemble transform Kalman filter (LETKF), to construct an ionospheric model over Yunnan in southwestern China. We used the LETKF method to assimilate the ionospheric TEC extracted from GNSS observations in Yunnan into the IRI-2016 model. The experimental results indicate that the ionospheric data assimilation has a more pronounced improvement effect on the IRI empirical model during periods of geomagnetic quiet than during periods of geomagnetic disturbance. On quiet magnetic days, the skill score (SKS) of the assimilation is 0.60 and the root mean square error (RMSE) values before and after assimilation are 5.08 TECU and 2.02 TECU, respectively. The correlation coefficient after assimilation increases from 0.94 to 0.99. On magnetic storm days, the SKS of the assimilation is 0.42 and the RMSE values before and after assimilation are 5.99 TECU and 3.46 TECU, respectively. The correlation coefficient after assimilation increases from 0.98 to 0.99. The results suggest that the LETKF algorithm can be considered an effective method for ionospheric data assimilation.

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Section: Introductionmentioning

confidence: 99%

Assimilating GNSS TEC with an LETKF over Yunnan, China

et al. 2023

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“…In recent years, data assimilation models have been developed for global or regional ionospheric TEC models to predict ionospheric space weather [2][3][4][5][6][7][8][9][10][11]. Some powerful techniques to assimilate data into ionospheric models are variational algorithm, a Kalman filter, and an ensemble Kalman filter.…”

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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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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“…For example, ErCha et al, using IRI as the background model and GNSS data as the observation values, applied a three-dimensional variational method and the Kalman filter to assimilate the ionospheric data, and generated quasi-real-time predictions of ionospheric TEC over China and adjacent areas [32]. Methods in the second category rely on the ingestion of GNSS data to minimize the difference between the high-precision TEC values extracted from GNSS data and the TEC results output from the IRI model by adjusting the IG 12 index and the RZ 12 index to improve the model's accuracy [35][36][37][38][39][40][41]. For instance, Nicholas Ssessanga et al ingested GNSS-TEC data into the IRI-2012 model, and by adjusting the IG 12 and RZ 12 indices simultaneously, obtained a modified IRI-2012 model that was more accurate than the original model in estimating TEC [39].…”

Section: Introductionmentioning

confidence: 99%

Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China

et al. 2021

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The International Reference Ionosphere (IRI) is an empirical model widely used to describe ionospheric characteristics. In the previous research, high-precision total ionospheric electron content (TEC) data derived from global navigation satellite system (GNSS) data were used to adjust the ionospheric global index IG12 used as a driving parameter in the standard IRI model; thus, the errors between IRI-TEC and GNSS-TEC were minimized, and IRI-TEC was calibrated by modifying IRI with the updated IG12 index (IG-up). This paper investigates various interpolation strategies for IG-up values calculated from GNSS reference stations and the calibrated TEC accuracy achieved using the modified IRI-2016 model with the interpolated IG-up values as driving parameters. Experimental results from 2015 and 2019 show that interpolating IG-up with a 2.5° × 5° spatial grid and a 1-h time resolution drives IRI-2016 to generate ionospheric TEC values consistent with GNSS-TEC. For 2015 and 2019, the mean absolute error (MAE) of the modified IRI-TEC is improved by 78.57% and 77.42%, respectively, and the root mean square error (RMSE) is improved by 78.79% and 77.14%, respectively. The corresponding correlations of the linear regression between GNSS-TEC and the modified IRI-TEC are 0.986 and 0.966, more than 0.2 higher than with the standard IRI-TEC.

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Improved IRI-2016 model based on BeiDou GEO TEC ingestion across China

Cited by 7 publications

References 23 publications

Assimilating GNSS TEC with an LETKF over Yunnan, China

Assimilating GNSS TEC with an LETKF over Yunnan, China

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

Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China

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