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 3 publications

(1 citation statement)

References 131 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Assimilating GNSS TEC with an LETKF over Yunnan, China

et al. 2023

View full text Add to dashboard Cite

show abstract

Global 3D ionospheric shape function modeling with kriging

Lyu,

Hernández-Pajares,

et al. 2024

J Geod

View full text Add to dashboard Cite

Ionospheric tomography for SWARM satellite orbit determination using single-frequency GNSS data

Prol,

Pignalberi,

Smirnov

et al. 2024

GPS Solut

View full text Add to dashboard Cite

Ionospheric tomography offers three-dimensional (3D) description of the electron density distribution, enabling the direct incorporation of electron density data into the slant total electron content (STEC) computation. As a result, STEC derived from tomography helps mitigate the ionospheric delay experienced in the line of sight between global navigation satellite systems (GNSS) and satellites positioned in low Earth orbits (LEO). Tomography can therefore be effectively employed to correct single-frequency GNSS observations and allow enhanced positioning of spaceborne platforms. We demonstrate the accuracy and performance of a global-scale ionospheric tomography method for determining satellite orbits, utilizing single-frequency GNSS measurements combined with a precise point positioning (PPP) algorithm. We compare the tomographic outcomes against orbit determination derived from the GRoup and PHase ionospheric correction (GRAPHIC) observable and based on an ionospheric climatological model. Near the peak of solar cycle 24, the overall accuracy achieved with tomography was around 3.8 m. notably, compared to the background climatological model, tomography demonstrated improvements ranging from 15 to 20%. The GRAPHIC method outperformed tomography, achieving an accuracy of 0.7 m, whereas we obtained around 7 m accuracy when no ionospheric model is employed. Although the developed ionospheric tomography has yet to match the precision of GRAPHIC, our results bring us relatively closer to this objective.

show abstract

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

Cited by 3 publications

References 131 publications

Assimilating GNSS TEC with an LETKF over Yunnan, China

Assimilating GNSS TEC with an LETKF over Yunnan, China

Global 3D ionospheric shape function modeling with kriging

Ionospheric tomography for SWARM satellite orbit determination using single-frequency GNSS data

Contact Info

Product

Resources

About