Empirical Data Assimilation for Merging Total Electron Content Data with Empirical and Physical Models

Forootan, Ehsan; Kosary, Mona; Farzaneh, Saeed; Schumacher, Maike

doi:10.1007/s10712-023-09788-7

Cited by 1 publication

(1 citation statement)

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“…Tang et al [23] presented a data assimilation model for a regional ionosphere based on the local EnKF and found that the method could allow effective regional ionospheric data assimilation. Forootan et al [24] presented a data assimilation method based on principal component analysis and the EnKF. The method can be used to improve VTEC estimates for globally available ionospheric models using IGS GIM products and can be easily applied to regional data assimilation by varying the background model and the domain of the observed field.…”

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%