Search Results (37)

Accurate estimation of the weighted mean temperature (Tm) is essential for retrieving precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) observations. Machine learning (ML) techniques excel in modeling nonlinear relationships among Tm time series, station geographic coordinates, and surface meteorological parameters, and recent studies have demonstrated that ML and neural network models outperform conventional linear Tm models. However, the full potential of surface meteorological measurements at GNSS stations for high-precision Tm retrieval remains to be fully explored. This study develops regional Tm empirical models using two ML methods—random forest (RF) and Temporal Mixture of Experts with Sequential Attention (TMESA)—to generate reliable real-time Tm estimates and enhance the accuracy of operational GNSS-PWV retrievals over China. A traditional linear model is adopted as the baseline to evaluate the performance improvements of the proposed models. The models are trained and tested using 10-year (2014–2023) hourly ERA5-derived Tm products and in-situ surface pressure, temperature, and relative humidity from 2377 meteorological stations, with Tm diurnal variations, station coordinates, and day of year integrated as auxiliary predictive features. Validation is conducted using 2024 ERA5 reanalysis data and radiosonde profiles from 120 stations across China. Results show that the RF model yields a bias (RMSE) of −0.11 K (2.67 K) against ERA5 and −0.21 K (2.67 K) against radiosonde data, while the TMESA model achieves superior performance with bias (RMSE) of −0.02 K (2.34 K) and 0.09 K (2.46 K), respectively, whose performance levels comparable to state-of-the-art studies. Compared with the traditional linear model, the RF model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the TMESA model achieves reductions of 40% and 33%, respectively. These findings confirm that the proposed ML models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Future work will focus on the operational application of these models for near-real-time GNSS-PWV estimation. Full article

The weighted mean temperature (T_m) is a critical parameter for converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology. Unlike conventional approaches, this study develops a novel high-precision atmospheric Tm grid model with enhanced spatiotemporal resolution through the incorporation of hourly near-surface temperature lapse rates (NSTLR). The core methodology encompasses two principal components: regional estimation of hourly NSTLR variations and establishment of a corresponding Tm grid model. Validation was conducted using ERA5 reanalysis datasets and in situ measurements from 109 meteorological stations across Shandong Province and Sichuan Province, China. Compared with no environmental lapse rate (ELR) correction and constant ELR correction, the accuracy of the constructed Tm grid model improved by 31.59% and 11.51%, respectively. Notably, in high-altitude areas, the improvements were even more substantial, reaching 58.65% and 21.28%, respectively. Therefore, the Tm model constructed in this study has significant practical significance for building ground-based meteorological observation systems, especially for regions with significant terrain variations. Full article

In this study, we present a novel data assimilation framework, the Ensemble Kalman Filter One-Dimensional Variational (EnKF1D-Var) framework, which assimilates observations from a Ground-based Microwave Radiometer (GMWR), a Mie–Raman Aerosol Lidar (MRL), and a Global Navigation Satellite System Meteorology sensor (GNSS/MET). The framework integrates multi-source vertical observations of water vapor and temperature with hourly temporal and 15 m vertical resolutions, driven by GFS forecasts. Three-month-long studies from May to July 2024 at Anqing Station in subtropical China demonstrate that the EnKF1D-Var retrievals reduce biases in temperature and humidity within the low troposphere, especially for daytime retrievals, by dynamically updating the observational error covariance matrices. Maximum humidity corrections reach up to 0.075 g/kg (120 PPMV), and temperature bias reductions exceed 3%. Incremental analysis reveals that the contribution to bias correction differs across instruments. GNSS/MET plays a dominant role in temperature adjustment, while GMWR provides supplementary support. In contrast, the majority of the improvements in water vapor retrieval can be attributed to MRL observations. This study achieved a reasonable application of multiple ground-based remote sensing observations, providing a new approach for the inversion of temperature and humidity profiles in the atmospheric boundary layer. Full article

Water vapor plays a crucial role in maintaining global energy balance and water cycle, and it is closely linked to various meteorological disasters. Precipitable water vapor (PWV), as an indicator of variations in atmospheric water vapor content, has become a key parameter for meteorological and climate monitoring. However, due to limitations in observation costs and technology, traditional atmospheric monitoring techniques often struggle to accurately capture the distribution and variations in space–time water vapor. With the continuous advancement of Global Navigation Satellite System (GNSS) technology, ground-based GNSS monitoring technology has shown rapid development momentum in the field of meteorology and is considered an emerging monitoring tool with great potential. Hence, based on the GNSS observation data from July 2023, this study retrieves PWV using the Global Pressure and Temperature 3 (GPT3) model and evaluates its application performance in the “7·31” extremely torrential rain event in Beijing in 2023. Research has found the following: (1) Tropospheric parameters, including the PWV, zenith tropospheric delay (ZTD), and zenith wet delay (ZWD), exhibit high consistency and are significantly affected by weather conditions, particularly exhibiting an increasing-then-decreasing trend during rainfall events. (2) Through comparisons with the PWV values through the integration based on fifth-generation European Centre for Medium-Range Weather Forecasts (ERA-5) reanalysis data, it was found that results obtained using the GPT3 model exhibit high accuracy, with GNSS PWV achieving a standard deviation (STD) of 0.795 mm and a root mean square error (RMSE) of 3.886 mm. (3) During the rainfall period, GNSS PWV remains at a high level (>50 mm), and a strong correlation exists between GNSS PWV and peak hourly precipitation. Furthermore, PWV demonstrates the highest relative contribution in predicting extreme precipitation, highlighting its potential value for monitoring and predicting rainfall events. Full article

Climate change monitoring and analysis is a critical task that involves the consideration of both spatial and temporal dimensions. Theimproved spatial distribution of the global navigation satellite system (GNSS) ground-based Continuous Operating Reference (COR) stations can lead to enhanced results when coupled with a continuous flow of data over time. In Africa, a significant number of COR stations do not operate continuously and lack collocation with meteorological sensors essential for climate studies. Consequently, Africa faces challenges related to inadequate spatial distribution and temporal data flow from GNSS ground-based stations, impacting climate change monitoring and analysis. This research delves into the pattern of GNSS radio occultation (RO) data across Africa, addressing the limitations of the GNSS ground-based data for climate change research. The spatial analysis employed Ripley’s F-, G-, K-, and L-functions, along with calculations of nearest neighbour and Kernel density. The analysis yielded a Moran’s p-value of 0.001 and a Moran’s I-value approaching 1.0. For temporal analysis, the study investigated the data availability period of selected GNSS RO missions. Additionally, it examined seasonal temperature variations from May 2001 to May 2023, showcasing alignment with findings from other researchers worldwide. Hence, this study suggests the utilisation of GNSS RO missions/campaigns like METOP and COSMIC owing to their superior spatial and temporal resolution. Full article

Weather forecasting is essential for agriculture, yet current methods often lack the localized accuracy required to manage extreme weather events and optimize irrigation. The MAGDA Horizon Europe/EUSPA project addresses this gap by developing a modular system that integrates novel European space-based, airborne, and ground-based technologies. Unlike conventional forecasting systems, MAGDA enables precise, field-level predictions through the integration of cutting-edge technologies: Meteodrones provide vertical atmospheric profiles where traditional data are sparse; GNSS-reflectometry offers real-time soil moisture insights; and all observations feed into convection-permitting models for accurate nowcasting of extreme events. By combining satellite data, GNSS, Meteodrones, and high-resolution meteorological models, MAGDA enhances agricultural and water management with precise, tailored forecasts. Climate change is intensifying extreme weather events such as heavy rainfall, hail, and droughts, threatening both crop yields and water resources. Improving forecast reliability requires better observational data to refine initial atmospheric conditions. Recent advancements in assimilating reflectivity and in situ observations into high-resolution NWMs show promise, particularly for convective weather. Experiments using Sentinel and GNSS-derived data have further improved severe weather prediction. MAGDA employs a high-resolution cloud-resolving model and integrates GNSS, radar, weather stations, and Meteodrones to provide comprehensive atmospheric insights. These enhanced forecasts support both irrigation management and extreme weather warnings, delivered through a Farm Management System to assist farmers. As climate change increases the frequency of floods and droughts, MAGDA’s integration of high-resolution, multi-source observational technologies, including GNSS-reflectometry and drone-based atmospheric profiling, is crucial for ensuring sustainable agriculture and efficient water resource management. Full article

Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average. At the same time, the PWV was inverted by radiosonde data in the study area as a reference to verify the water vapor inversion results of the new model, and it was found that the BIAS of the new model is only 0.8 mm, which has high accuracy. Further, compared with the LSTM model, the new model is more universal when the accuracy is comparable. In addition, in order to evaluate the spatial and temporal variation characteristics of the atmospheric water vapor retrieved by the new model, based on the rainstorm event caused by typhoon in Hong Kong of September 2023, the ERA5 GSMaP rainfall products and inverted PWV information were comprehensively used for analysis. The results show that the PWV increased sharply with the arrival of the typhoon and the occurrence of a rainstorm event. After the rain stopped, the PWV gradually decreased and tended to be stable. The spatial and temporal variation in the PWV have a strong correlation with the occurrence of extreme rainstorm events. This shows that the PWV inverted by the new model can respond well to extreme rainstorm events, which proves the feasibility and reliability of the new model and provides a reference method for meteorological monitoring and weather forecasting. Full article

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have been widely employed in positioning, navigation, and timing (PNT). Furthermore, GNSS refraction, reflection, and scattering signals can remotely sense the Earth’s surface and atmosphere with powerful implications for environmental remote sensing. In this paper, the recent developments and new application progress of multi-GNSS in Earth observation are presented and reviewed, including the methods of BDS/GNSS for Earth observations, GNSS navigation and positioning performance (e.g., GNSS-PPP and GNSS-NRTK), GNSS ionospheric modelling and space weather monitoring, GNSS meteorology, and GNSS-reflectometry and its applications. For instance, the static Precise Point Positioning (PPP) precision of most MGEX stations was improved by 35.1%, 18.7%, and 8.7% in the east, north, and upward directions, respectively, with PPP ambiguity resolution (AR) based on factor graph optimization. A two-layer ionospheric model was constructed using IGS station data through three-dimensional ionospheric model constraints and TEC accuracy was increased by about 20–27% with the GIM model. Ten-minute water level change with centimeter-level accuracy was estimated with ground-based multiple GNSS-R data based on a weighted iterative least-squares method. Furthermore, a cyclone and its positions were detected by utilizing the GNSS-reflectometry from the space-borne Cyclone GNSS (CYGNSS) mission. Over the years, GNSS has become a dominant technology among Earth observation with powerful applications, not only for conventional positioning, navigation and timing techniques, but also for integrated remote sensing solutions, such as monitoring typhoons, river water level changes, geological geohazard warnings, low-altitude UAV navigation, etc., due to its high performance, low cost, all time and all weather. Full article

Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service analysis centers (ACs) have initiated their third reprocessing campaign, known as IGS Repro3. In this campaign, six ACs conducted a homogeneous reprocessing of the ZTD time series spanning the period from 1994 to 2022. This paper primarily focuses on ZTD products. First, the data processing strategies and station conditions of six ACs were compared and analyzed. Then, formal errors within the data were examined, followed by the implementation of quality control processes. Second, a combination method is proposed and applied to generate the final ZTD products. The resulting combined series was compared with the time series submitted by the six ACs, revealing a mean bias of 0.03 mm and a mean root mean square value of 3.02 mm. Finally, the time series submitted by the six ACs and the combined series were compared with VLBI data, radiosonde data, and ERA5 data. In comparison, the combined solution performs better than most individual analysis centers, demonstrating higher quality. Therefore, the advanced method proposed in this study and the generated high-quality dataset have considerable implications for further advancing GNSS atmospheric sensing and offer valuable insights for climate modeling and prediction. Full article

The global navigation satellite system radio occultation (GNSS-RO) is an important means of space-based meteorological observation. It is necessary to test the Global Navigation Satellite System Occultation signal receiver on the ground before the deployment of space-based occultation detection systems. The current approach of testing the GNSS signal receiver on the ground is mainly the mountaintop-based testing approach, which has problems such as high cost and large simulation error. In order to overcome the limitations of the mountaintop-based test approach, this paper proposes an accurate, repeatable, and controllable GNSS atmospheric occultation simulation system and builds a load performance evaluation approach based on the ground-based GNSS atmospheric occultation simulation system on the basis of it. The GNSS atmospheric occultation simulation system consists of the visualization and interaction module, the GNSS-RO simulation signal generation module, the GNSS-RO simulator module, the GNSS-RO signal receiver module, and the GNSS-RO inversion and evaluation module, combined with the preset atmospheric model to generate GNSS-RO simulation signals with a high degree of simulation, and comparing the atmospheric parameters of the inversion performance of the GNSS-RO signal receiver with the parameters of the preset atmospheric model to obtain the error data. The overall performance of the GNSS-RO signal receiver can be evaluated based on the error information. The novel approach to evaluate the GNSS-RO signal receiver performance proposed in this paper is validated by using the FY-3E (FengYun-3E) receiver qualification parts that have been verified in orbit, and the results confirm that the approach can meet the requirements of the GNSS-RO receiver performance test. This study shows that the novel approach to evaluate the GNSS-RO signal receiver performance in terms of the ground-based atmospheric occultation simulation system can efficiently and accurately be used to carry out the receiver test and provides an effective solution for the ground-based test of GNSS-RO signal receivers. Full article

The atmospheric water vapor is commonly monitored from ground Global Navigation Satellite System (GNSS) measurements, by retrieving the tropospheric delay under the Zenith Wet Delay (ZWD) component, linked to the water vapor content in the atmosphere. In recent years, the GNSS ZWD retrieval has been performed on shipborne antennas to gather more atmospheric data above the oceans for climatology and meteorology study purposes. However, when analyzing GNSS data acquired by a moving antenna, it is more complex to decorrelate the height of the antenna and the ZWD during the Precise Point Positioning (PPP) processing. Therefore, the observation modeling and processing parametrization must be tuned. This study addresses the impact of modeling on the estimation of height and ZWD from the simulation of shipborne GNSS measurements. The GNSS simulation is based on an authors-designed simulator presented in this article. We tested different processing models (elevation cut-off angle, elevation weighting function, and random walk of ZWD) and simulation configurations (the constellations used, the sampling of measurements, the location of the antenna, etc.). According to our results, we recommend processing shipborne GNSS measurements with 3° of cut-off angle, elevation weighting function square root of sine, and an average of 5 mm·h${}^{-1/2}$ of random walk on ZWD, the latter being specifically adapted to mid-latitudes but which could be extended to other areas. This processing modeling will be applied in further studies to monitor the distribution of water vapor above the oceans from systematic analysis of shipborne GNSS measurements. Full article

Flash floods in the Eastern Mediterranean (EM) region are considered among the most destructive natural hazards, which pose a significant challenge to model due to their high complexity. Machine learning (ML) methods have made a significant contribution to the advancement of flash flood prediction systems by providing cost-effective solutions with improved performance, enabling the modeling of the complex mathematical expressions underlying physical processes of flash floods. Thus, the development of ML methods for flash flood prediction holds the potential to mitigate risks, inform policy recommendations, minimize loss of human life, and reduce property damage caused by flash floods. Here, we present a novel approach for improving flash flood predictions in the EM region using Support Vector Machines (SVMs) with a combination of precipitable water vapor (PWV) data, derived from ground-based global navigation satellite system (GNSS) receivers, along with surface pressure measurements, and nearby lightning occurrence data to predict flash floods in an arid region of the EM. The SVM model was trained on historical data from 2004 to 2019 and was used to forecast the likelihood of flash floods in the region. The study found that integrating nearby lightning data with the other variables significantly improved the accuracy of flash flood prediction compared to using only PWV and surface pressure measurements. The results of the SVM model were validated using observed flash flood events, and the model was found to have a high predictive accuracy with an area under the receiver operating characteristic curve of 0.93 for the test set. The study provides valuable insights into the potential of utilizing a combination of meteorological and lightning data for improving flash flood forecasting in the Eastern Mediterranean region. Full article

Tropospheric delay models based on GNSS observations are essential for studying tropospheric changes. However, the uneven distribution of GNSS stations reduces the accuracy of GNSS tropospheric delay models in remote areas. Moreover, the accuracy of the tropospheric delay calculated by traditional models, which rely on meteorological parameters, is lower compared to the accuracy achieved by GNSS tropospheric models. At present, there are sufficient surface meteorological observation facilities around the world that can obtain surface meteorological parameters in real time. It is of great importance to make full use of the measured meteorological parameters to establish tropospheric correction models. Moreover, the empirical tropospheric models use free and open data, and one can obtain tropospheric parameters through the model without requiring any auxiliary information. We established a provincial real-time regional tropospheric fusion model using ground-based GNSS observations, a meteorological model, and empirical model data. Results showed that the tropospheric delay observations of the three models can be fused to establish a real-time tropospheric delay model with better accuracy and higher spatiotemporal resolution. The accuracy of Zenith Total Delay (ZTD) estimated by the fusion model reached 0.96/1.04/3.11 cm during the tropospheric quiet/active/typhoon period. Full article

Aquifer surveillance is key to understanding the dynamics of groundwater reservoirs. Attention should be focused on developing strategies to monitor and mitigate the adverse consequences of overexploitation. In this context, ground surface deformation monitoring allows us to estimate the spatial and temporal distribution of groundwater levels, determine the recharge times of the aquifers, and calibrate the hydrological models. This study proposes a methodology for implementing advanced multitemporal differential interferometry (InSAR) techniques for water withdrawal surveillance and early warning assessment. For this, large open-access images were used, a total of 145 SAR images from the Sentinel 1 C-band satellite provided by the Copernicus mission of the European Space Agency. InSAR processing was carried out with an algorithm based on parallel computing technology implemented in cloud infrastructure, optimizing complex workflows and processing times. The surveillance period records 6-years of satellite observation from September 2016 to December 2021 over the city of Chillan (Chile), an area exposed to urban development and intensive agriculture, where ~80 wells are located. The groundwater flow path spans from the Andes Mountain range to the Pacific Ocean, crossing the Itata river basin in the Chilean central valley. InSAR validation measurements were carried out by comparing the results with the values of continuous GNSS stations available in the area of interest. The performance analysis is based on spatial analysis, time series, meteorological stations data, and static level measurements, as well as hydrogeological structure. The results indicate seasonal variations in winter and summer, which corresponds to the recovery and drawdown periods with velocities > −10 mm/year, and an aquifer deterioration trend of up to 60 mm registered in the satellite SAR observation period. Our results show an efficient tool to monitor aquifer conditions, including irreversible consolidation and storage capacity loss, allowing timely decision making to avoid harmful exploitation. Full article

Global Navigation Satellite System (GNSS) is an established atmospheric monitoring technique delivering water vapour data in near-real time with a latency of 90 min for operational Numerical Weather Prediction in Europe within the GNSS water vapour service (E-GVAP). The advancement of GNSS processing made the quality of real-time GNSS tropospheric products comparable to near-real-time solutions. In addition, they can be provided with a temporal resolution of 5 min and latency of 10 min, suitable for severe weather nowcasting. This paper exploits the added value of sub-hourly real-time GNSS tropospheric products for the nowcasting of convective storms in Bulgaria. A convective Storm Demonstrator (Storm Demo) is build using real-time GNSS tropospheric products and Instability Indices to derive site-specific threshold values in support of public weather and hail suppression services. The Storm Demo targets the development of service featuring GNSS products for two regions with hail suppression operations in Bulgaria, where thunderstorms and hail events occur between May and September, with a peak in July. The Storm Demo real-time Precise Point Positioning processing is conducted with the G-Nut software with a temporal resolution of 15 min for 12 ground-based GNSS stations in Bulgaria. Real-time data evaluation is done using reprocessed products and the achieved precision is below 9 mm, which is within the nowcasting requirements of the World Meteorologic Organisation. For the period May–September 2021, the seasonal classification function for thunderstorm nowcasting is computed and evaluated. The probability of thunderstorm detection is 83%, with a false alarm ration of 38%. The added value of the high temporal resolution of the GNSS tropospheric gradients is investigated for a storm case on 24–30 August 2021. Real-time tropospheric products and classification functions are integrated and updated in real-time on a publicly accessible geoportal. Full article