The global navigation satellite system (GNSS) is widely used for air navigation and considered as a main sensor. While dual-frequency GNSS has been standardized recently by International Civil Aviation Organization (ICAO), only single-frequency GNSS equipment are available and used. GNSS ground-based augmentation system (GBAS) is an air navigation system which supports precision approach and landing of aircraft. GBAS is based on the range-domain differential GNSS technique where GNSS reference stations on the ground observe GNSS signals and send correction and their integrity information to aircraft. Spatial decorrelation in the ionospheric delay (i.e. ionospheric delay gradient) is one of the major error sources. Results of ionospheric delay gradient observations in Vietnam and Indonesia are reported. Furthermore, ionospheric delay gradients associated with other ionospheric phenomena are reported.

This study investigates the impact of sea surface roughness, influenced by both wind and wave characteristics, on GNSS-R observations using TRITON data. Traditional wind speed inversion algorithms often simplify Delay-Doppler Maps (DDM) to represent sea surface roughness caused solely by wind. However, this research highlights the critical role that wave-induced roughness plays in the scattering of microwave signals. To analyze these effects, the study employs the ResNet18 deep convolutional neural network, which excels at handling the complex features present in DDM data. The model integrates parameters such as wave conditions and GNSS-R incident angles to extract relevant features, enhancing wind speed prediction accuracy. The research uses TRITON data along with ECMWF wind speed and sea surface parameters for model training and evaluation. The findings indicate substantial improvements in wind speed prediction accuracy when accounting for both wind- and wave-induced roughness. This comprehensive approach reduces prediction errors and provides more reliable data for applications such as weather forecasting and climate modeling. These results underscore the potential of deep learning to integrate detailed sea surface characteristics into GNSS-R observations, offering significant advancements in predictive accuracy and operational applications.

At the lower latitude region, the electron density distribution significantly influences high-frequency and satellite communication as well as navigation and positioning services. Over Taiwan, there is a high density of plasma during the day which is associated with the equatorial ionization anomaly, and plasma bubbles/irregularities appear in the evening to midnight period. In order to monitor the variation of ionospheric electron density and the movement of ionospheric irregularities, the Space Weather Operational Office of Central Weather Administration (CWA/SWOO) has collaborated with Taiwan Space Agency (TASA) and domestic scientific research teams from universities to establish various products which by applying the Global Navigation Satellite System (GNSS) signal. By using ground-based and space-based observations, CWA/SWOO provides real-time global and regional total electron content (TEC), scintillation index, rate of TEC index, as well as ionospheric radio occultation profile, etc. Meanwhile, a Global Ionospheric Specification and an ionosphere-thermosphere coupled assimilation system is operated by combining GNSS observations to reproduce three-dimensional electron density structure and short period forecast of ionosphere and thermosphere, respectively. In addition, in order to understand the relationship between positioning error and ionospheric features, CWA/SWOO also conducted a system to monitor positioning of a few reference stations around Taiwan in real-time. It would provide a good dataset to make comparisons of positioning error and irregularities.

The FORMOSAT-7/COSMIC-2 (F7C2) is a joint collaboration program between Taiwan and US to continue the provision of next-generation Global Navigation Satellite System (GNSS) Radio Occultation (RO) data to the global users Numerical Weather Prediction (NWP). The six F7C2 satellites were successfully launched into a 24 deg inclination low Earth orbit on June 25, 2019. The F7C2 mission aims to collect atmospheric and ionospheric remote sensing data (soundings) globally through a constellation system of six satellites, providing high temporal resolution and high data quality. The retrieved atmospheric and ionospheric GNSS-RO data have been freely available online to the users’ community since shortly after the completion of neutral atmosphere Initial Operational Capability (IOC) in March 2020 and Full Operational Capability (FOC) in September 2021, respectively. The F7C2's atmospheric and ionospheric occultation RO requirements are at least 4,000 profiles per day, and after May 2021, the atmospheric and ionospheric occultation RO profiles have reached more than 5,500 and 4,000 average profiles per day, respectively. Atmospheric and ionospheric occultation RO profiles accumulated by F7C2 (as-of-Oct. 15, 2024), the total number of atmospheric occultations is 9,813,366 and the total number of ionospheric occultations is 7,035,981. This paper provides an overview of the F7C2 mission operational telemetry trends after five years of six-satellite in-orbit, demonstrating good system performance in collecting next-generation GNSS RO data and provide the lessons learned after 5 years of operations in space.

The Marine Boundary Layer Height (MBLH) is in general characterized as a discontinuity interface in terms of temperature, moisture and wind field over the open ocean. Monitoring changes in the MBLH over time can provide insights into climatic change. However, there is no extensive observation system to provide the accurate MBLH measurement. Global Navigation Satellite System Radio Occultation (GNSS RO) data have several advantages, including global coverage, high vertical resolution, and the data quality relative insensitivity to factors such as weather patterns, geographical location, and diurnal variations. They can complement scarce observations over the oceans. By using data inversion techniques, various high-level profile products such as bending angle (BA), refractivity (N), temperature, and water vapor pressure can be derived from GNSS RO data. Therefore, many studies have attempted to identify MBLH using GNSS RO data. Different RO products is able to offer distinct advantages in defining MBLH. This study extends and develops a hybrid GNSS RO multi-variable algorithm, with the discontinuity in BA profile as the primary criterion, supplemented by variations in refractivity and water vapor pressure to enhance the reliability of MBLH determination.

Equatorial plasma bubbles (EPBs) are regions within the ionosphere where plasma density is significantly lower than surrounding regions. These EPBs may disrupt satellite communication and GNSS (Global Navigation Satellite System) signals, affecting both the phase and amplitude of the signals. This interference leads to errors in positioning accuracy, a phenomenon commonly referred to as ionospheric scintillations. Due to Taiwan's unique geographical location close to the magnetic equator, it is particularly susceptible to the effects of EPBs. However, accurately detecting the locations of these EPBs remains a challenging task, both regionally and globally. To address these challenges, the primary objective of Center Weather Administration (CWA) is to enhance space weather information and provide local space weather forecasts. For ionospheric monitoring, CWA employs data from both space-based and ground-based sources. To verify the locations of plasma bubbles, CWA utilizes scintillation geolocation data from FORMOSAT-7/COSMIC-2 (Formosa Satellite-7/Constellation Observing System for Meteorology Ionosphere and Climate) measurements as well as ionospheric scintillation data from GNSS ground-based receivers. By combining these two types of observations, it becomes possible to track the movement of EPBs in the regions surrounding Taiwan. This method is probably able to be a valuable tool to provide the information for local space weather services.

FORMOSAT-7/COSMIC-2 have launched on June 2019 and provided high space resolution RO profile data. The data can be benefit for weather and climate studies, as well as increasing the accuracy of weather forecast. This study aims to assess two years COSMIC-2 RO-PWV (precipitable water vapor) accuracy. Ten years GNSS-PWV data inverted from PPP (precise point positioning) are better than from DD (double difference) method when taking radiosonde observed data as reference true value in Taiwan. Hence in this study GNSS-PWV from PPP are compared with the COSMIC-2 RO-PWV to verify the accuracy of RO-PWV. RO-PWV derived from COSMIC-2 in 2020 and 2021 is underestimated when taking GNSS-PWV as reference. Removing the elevation of the lowest point of an RO event above 5km can avoid the RO event with error exceed 40mm, and obtain mean RO error of 6.8mm.

This study uses mobile buoys deployed in the Philippine Sea and the South China Sea, fixed buoys along the coast of Taiwan, and the atmospheric reanalysis database Reanalysis v5 (ERA5) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) to verify and compare the wave heights retrieved from Triton. The results show that the first-fit line slopes of the comparison between Triton- retrieved wave heights and those observed by mobile and fixed buoys are 0.198 and 0.189, respectively, while that of the ECMWF ERA5 database is 0.24. The wave heights retrieved by Triton are overestimated compared to the in-situ buoy observations and the results provided by the ECMWF ERA5 database. The Central Weather Administration of Taiwan has developed high-resolution wave forecasting models, including deterministic and ensemble forecasts, and provides wave forecasts for the Blue Highway around Taiwan and its offshore islands. In the future, the wave heights retrieved by Triton will be used to validate the model forecast outputs and assess the feasibility of forecast bias correction.