Evaluation of PPP software performance for TEC estimation using IRI-2020, CODE, COSMIC, and SWARM with GNSS data
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Reham Nagib
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Mohamed A. Abdelfatah
Abstract
This study evaluates the performance of two Precise Point Positioning (PPP) software solutions, GAPS and GAMP, for estimating Vertical Total Electron Content (VTEC). Their outputs were compared against the International Reference Ionosphere (IRI) models, the Center for Orbit Determination in Europe (CODE) solutions, SWARM satellite data, and COSMIC-1 observations. The results demonstrate that GAPS achieves superior accuracy and reliability after initialization, with VTEC estimates closely aligning with CODE, IRI, COSMIC-1, and SWARM data. In contrast, GAMP, while exhibiting faster convergence, showed greater variability and a tendency to underestimate VTEC, especially under dynamic ionospheric conditions. Statistical analyses revealed that GAPS produced lower Root Mean Square (RMS) errors across stations, with values below 20 Total Electron Content Units (TECU) when compared to CODE, IRI and between 5 and 25 TECU when validated against COSMIC-1 and SWARM data. Conversely, GAMP’s RMS values reached up to 65 TECU, indicating lower precision. GAPS also showed smaller average and absolute differences, confirming its ability to capture localized ionospheric variations more effectively than GAMP and IRI models. A t-test analysis indicated no statistically significant differences between GAPS and CODE, IRI, SWARM, or COSMIC-1 for most stations, demonstrating the robustness of GAPS in representing ionospheric behavior. GAMP, however, often exhibited significant differences in VTEC estimates relative to these references. These findings demonstrate GAPS’s superior performance in ionospheric studies and VTEC estimation, underscoring the importance of choosing suitable PPP solutions and prepossessing for high-precision GNSS and atmospheric research.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Data used in this paper is available from the authors upon request (corresponding author: Reham).
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