## 1 Introduction
These include the BeiDou B2b service, Japan's Centimeter-Level Augmentation Service (CLAS) for QZSS [38], and Europe's Galileo High Accuracy Service (HAS) [39]. While B2b and CLAS are broadcast via geostationary satellites and thus limited to the Asia-Pacific region, Galileo HAS data are transmitted by a subset of satellites--currently up to 20--from the overall constellation, enabling global coverage with varying levels of availability [40]. The Galileo HAS will operate at two levels [41]. Level 1 provides global coverage with a target horizontal accuracy below 20 cm and vertical accuracy below 40 cm, with convergence times under 300 s. Level 2, limited to Europe, aims to achieve the same accuracy with a faster convergence time (under 100 s) by incorporating regional atmospheric corrections. HAS's development comprises three phases: Phase 1 (testing and experimentation, 2020-2022); Phase 2 (Initial Service), launched on 24 January 2023; and Phase 3 (Full Service), which will deliver Level 2 capabilities [40]. As a central element of the European GNSS ecosystem, Galileo HAS offers free access to PPP corrections globally, broadcast via the E6-B signal or through the internet. Although the service is relatively new, its potential is already evident. Open-source tools such as HASlib [42], HAS PPP [43], and Python-based decoding toolboxes have been developed to enable the real-time reception and processing of HAS corrections [44]. Preliminary studiesusing test signals have demonstrated a promising PPP performance [45; 46; 47]. Furthermore, integration with tools like RTKLIB [48] highlights the potential of open-source platforms to broaden access to high-precision GNSS positioning. Nevertheless, further research is required to enhance the service's reliability and broaden its applicability. One of the key advantages of PPP is its capability to estimate tropospheric delay parameters, particularly the Zenith Tropospheric Delay (ZTD), in both post-processed and real-time modes. These estimates are essential for atmospheric studies, as they can be further converted into Precipitable Water Vapor (PWV) and assimilated into Numerical Weather Prediction (NWP) models to improve short-term weather forecasts. Several studies have highlighted the sensitivity of ZTD estimations to the quality of input products and modeling strategies [49]. For real-time applications, particularly those supporting NWP data assimilation, accuracy thresholds of 0.6 cm (target) and 3 cm (maximum) are typically required [10]. Recent advances in real-time GNSS processing, including PPP based on broadcast ephemerides [46; 49] and the integration of multiple GNSS constellations [50; 51], have shown the potential to meet these requirements. Additionally, current research continues to investigate how spatial resolution and data assimilation methods affect the accuracy of humidity fields derived from GNSS-based PWV products [52], reinforcing the importance of reliable real-time ZTD estimations in operational meteorology. Currently, several studies on PPP positioning using HAS corrections have been carried out. However, they differ in terms of the time periods, processing software, and models used, and they often do not compare the obtained results with other products and corrections. The aim of this study is to analyze PPP performance using different types of products and corrections: broadcast ephemerides, CNES RTS corrections, Galileo HAS corrections, and final precise products from the Center for Orbit Determination in Europe (CODE) within the Multi-GNSS Experiment (MGEX). The analysis is carried out using a uniform PPP processing scheme: all test cases use identical 1 s interval observations from globally distributed stations, the same processing software, and consistent modeling assumptions. This setup enables an objective and direct comparison of PPP performance across the different products.