Global Ionospheric Tomography Based on Data-Driven Fusion Algorithm Using GNSS

Accurate global-scale ionospheric electron density modeling is crucial for space weather monitoring, exploration, and radio signal applications. This letter presents a novel global-scale ionospheric tomography modeling method, dynamic compressed sensing-principal component analysis (DCS-PCA), building upon the previous region method CS-PCA. The upgraded method operates globally, utilizing dynamic data-driven techniques and undifferenced observation data processing to achieve high-precision quasi-real-time global-scale ionospheric tomography based on global navigation satellite system (GNSS) data. Tomographic models with a 5-min temporal resolution were constructed in this study, utilizing data from various IGS ground stations worldwide and employing the U-DCS-PCA, D-DCS-PCA, and traditional constrained algebraic reconstruction technique (CART). The DCS-PCA model is found to outperform both the CART model and the CODE Global Ionospheric Maps (CODG) model. Specifically, when evaluating differential STEC (dSTEC) errors at independent reference stations across various latitudes, we observed that the error of the DCS-PCA model is not significantly impacted by station sparsity, consistently remaining lower than that of CODG products. In contrast, the error of the CART model increases as the number of stations decreases. Additionally, the U-DCS-PCA model is found to closely align with electron density observations from ionosonde stations. This method is ideal for global 4-D ionospheric monitoring and has potential applications in space weather monitoring, exploration, and radio signal enhancement.