Conference Agenda

Precipitable Water Vapour (PWV) represents the total amount of water vapour contained in a vertical column of the atmosphere that is available for precipitation. It is a key variable in meteorological studies, as well as in the monitoring of extreme weather events. This study proposes and evaluates a method for estimating PWV using Global Navigation Satellite System (GNSS) data (PWV-GNSS), obtained from the Zenith Total Delay (ZTD), which comprises the Zenith Hydrostatic Delay (ZHD) and the Zenith Wet Delay (ZWD). The approach combines data from GNSS stations with external meteorological sources to improve atmospheric monitoring systems. The evaluation was conducted in the region of Santa Maria, Brazil (RS), selected due to the proximity (~2km) between the radiosonde (SBSM), GNSS (SMAR), and meteorological (A803) stations and its variable climate, characterised by frequent precipitation events. As a case study, the month of September 2023 was analysed, during which accumulated rainfall exceeded 450 mm. Results indicated strong agreement between PWV estimates derived from GNSS (PWV-GNSS) and radiosonde observations (PWV-RDS) used as a reference, with a root mean square error (RMSE) of 2.37 mm, a bias of –1.90 mm, and a coefficient of determination (R²) of 0.97 between PWV-RDS and PWV-GNSS. Furthermore, PWV-GNSS demonstrated a characteristic response to heavy rainfall events, with reductions of up to 40 mm following intense precipitation.

Accurate atmospheric profiling is essential for understanding climate dynamics and improving weather forecasting, particularly in data-scarce regions. This study evaluates the performance of the Radio Occultation (RO) technique - using data from the COSMIC-2 (sigla) satellite constellation= - by comparing vertical temperature profiles with radiosonde (RSO) observations over the São Francisco River Basin (Brazil) during January and July 2020, which represent contrasting seasonal conditions. Statistical analyses, including root mean square error (RMSE), correlation coefficient, relative error, and mean differences, are applied to assess the agreement between the two datasets. Results reveal high consistency, with RMSE values below 1.6 °C, correlation coefficients exceeding 0.84, and average differences generally below 1 °C. The student’s t-test confirmed the absence of statistically significant differences at the 5% level, reinforcing the reliability of the RO-derived profiles. Such findings suggest that RO is a promising method for capturing atmospheric temperature structures, making it a valuable complementary tool for climate monitoring in regions with limited in-situ observations.

NPTool (Neutrospheric Products Tool) is an automated system developed for neutrospheric modeling, with applications in geodesy and atmospheric sciences. It provides neutrospheric parameters and allows the estimation of neutrospheric delay, such as Zenithal Total Delay (ZTD), Zenithal Hydrostatic Delay (ZHD) and Zenithal Wet Delay (ZWD), as well as the derivation of Precipitable Water Vapor (PWV). These variables are essential for improving both atmospheric monitoring and the accuracy of GNSS positioning. By integrating data from multiple sources - such as radiosondes, surface meteorological stations, and GNSS observations - NPTool automates the processing and retrieval of neutrospheric products. This integration fosters a synergy between geodetic and meteorological approaches, supporting studies related to weather forecasting, climate analysis, and natural hazard assessment. Additionally, based on theoretical references and educational materials, NPTool serves as a complementary learning resource for students and teachers, offering practical insights through its visualizations from graphs and measurement outputs.

Ionospheric effects represent a significant source of error in Interferometric Synthetic Aperture Radar (InSAR) measurements, especially for long-wavelength sensors such as ALOS-2 PALSAR-2. This study investigates the accuracy of estimating ionospheric phase using the Split Spectrum Method (SSM) in a time series of L-band SAR acquisitions spanning 2020–2021 in an adverse environment that limits the performance of SSM. The estimated ionospheric phase is evaluated by calculating the phase closure of differential ionospheric phase triplets and averaging them in space for each date. Additionally, estimates are compared with Vertical Total Electron Content (VTEC) maps from two external sources: the International GNSS Service (IGS) Global Ionosphere Maps (GIMs) and the Madrigal upper atmospheric science database.

Results show high phase closure values for dates that, due to low coherence, do not form many interferometric pairs. A strong correlation (r=0.82) is observed between InSAR-derived and Madrigal-based ionospheric phase, despite data gaps in the latter. In contrast, low correlation is found with the IGS VTEC maps, attributed to their coarse spatial resolution and high modeled ionospheric shell height. The linear correlation between SSM and Madrigal data demonstrates good agreement, highlighting the potential of integrating external VTEC maps as complementary information, particularly in cases of low SAR coherence or limited acquisitions where the SSM becomes less effective.

This paper presents the development and evaluation of a Brazilian software for Precise Point Positioning (PPP) using multiple Global Navigation Satellite Systems (GNSS). The tool, named APPPOLO (Advanced Precise Point Positioning software for Optimized Localization), was designed to support advanced modeling and flexible configuration for academic and applied research in GNSS positioning. A specific performance analysis was conducted focusing on multi-GNSS PPP solutions using real data from Brazilian stations. The assessment considered the impact of different GNSS constellation combinations on positional accuracy and convergence time. The results demonstrated significant improvements in both accuracy and convergence when integrating GPS, GLONASS, Galileo, and BeiDou, compared to standalone systems. This paper contributes to ongoing efforts to develop robust, locally adapted GNSS processing tools capable of addressing atmospheric and infrastructural challenges found in South America.