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Recent Progress in Monitoring the Troposphere with GNSS Techniques

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A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 10110

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Editors

Dr. Minghua Wang

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Guest Editor

School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: GNSS meteorology; GNSS and smartphone positioning; multipath error modeling and mitigation
Special Issues, Collections and Topics in MDPI journals

Dr. Hong Liang

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Guest Editor

Meteorological Observation Center of China Meteorological Administration, Beijing 100081, China
Interests: GNSS meteorology; water vapor observation

Prof. Dr. Cuixian Lu

E-Mail Website
Guest Editor

School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Interests: space geodesy; GNSS meteorology; GNSS remote sensing; GNSS precise point positioning; AI in GNSS; geoscience applications

Dr. Jungang Wang

E-Mail Website
Guest Editor

Institut für Geodäsie und Geoinformationstechnik, Technische Universität Berlin, 10623 Berlin, Germany
Interests: space geodetic techniques; atmospheric delay effects; GNSS

Special Issue Information

Dear Colleagues,

Water vapor in the troposphere is believed to be an important amplifier of climate change, and its temporal and spatial distribution plays a crucial role in the formation of clouds and precipitation. The L-band signals of the Global Navigation Satellite System (GNSS), received by users on the ground for positioning, navigation, and timing, pass through the entire atmosphere of the Earth. This opens up the opportunity to use GNSS to monitor water vapor in the troposphere. GNSS works around the clock and in all weather conditions. From GNSS observations, the zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) can be derived with a minute-level or second-level interval. Due to these advantages, the GNSS water vapor retrieval technique has captured the interest of researchers in geodetic and atmospheric communities ever since it was first proposed three decades ago. The assimilation of ZTD and PWV derived from GNSS observations has been demonstrated to have positive impacts on numerical weather forecasts. Due to the development of multi-GNSS, more GNSS observables are being collected at each station, and the spatial resolution of GNSS water vapor observation has improved due to the increase in the number of the stations. These new developments are prompting investigations into new methods and new data processing strategies for GNSS water vapor monitoring, as well as leading to innovation applications for GNSS in meteorology and climatology.

This Special Issue will explore advances in GNSS applications in meteorological and climatological studies. Possible topics include new strategies for estimating tropospheric parameters from multi-GNSS observables, the assimilation of GNSS-derived products into numerical weather models, 3D water vapor tomography, the analysis of temporal and spatial variations in water vapor, severe weather monitoring with GNSS, and other relevant innovations and applications.

For this Special Issue, we welcome the submission of research articles and reviews. The scope includes, but is not limited to, the following research topics:

The estimation of tropospheric parameters;
The retrieval of precipitable water vapor;
3D water vapor tomography;
Water vapor distribution on different spatial–temporal scales;
The assimilation of ZTD and PWV into NWM;
Extreme weather forecasting.

Dr. Minghua Wang
Dr. Hong Liang
Prof. Dr. Cuixian Lu
Dr. Jungang Wang
Guest Editors

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Keywords

GNSS meteorology
zenith total delay estimation
precipitable water vapor
water vapor tomography
severe weather monitoring
data assimilation
numerical weather forecast

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Published Papers (5 papers)

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Research

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25 pages, 7155 KB

Open AccessArticle

Atmospheric Water Vapor Monitoring on Horseshoe Island, Antarctica: GNSS Observations at the Permanent TUR1 and TUR2 Stations Using a Regional Tm Calibration

by Mahmut Oğuz Selbesoğlu, Görkem Yalçın, Mustafa Fahri Karabulut, Hasan Hakan Yavaşoğlu, Esra Günaydın and Vahap Engin Gülal

Remote Sens. 2026, 18(18), 3226; https://doi.org/10.3390/rs18183226 - 19 Sep 2026

Abstract

Atmospheric water vapor plays a critical role in the climate system, governing energy balance, climate variability and precipitation processes. Accurate monitoring of precipitable water vapor (PWV) is therefore essential for both meteorological and climate-related studies. The Global Navigation Satellite System (GNSS) provides an effective and continuous tool for PWV estimation through the calculation of zenith tropospheric wet delay (ZWD). Given the scarcity of continuous ground-based observations in Antarctica, the TUR1 and TUR2 permanent GNSS stations, established on Horseshoe Island during the Turkish Antarctic Expedition-4 (TAE-4) under the TÜBİTAK Polar Research Project (No. 118Y322), provide a valuable infrastructure for continuous atmospheric water vapor monitoring, which constitutes the primary contribution of this study. The conversion of ZWD to PWV requires an accurate estimation of the weighted mean temperature (Tm), which is typically derived from empirical models. However, globally applied Tm models may not adequately represent the regional atmospheric variability of high-latitude environments, where the vertical atmospheric structure and water vapor distribution differ substantially from mid-latitude conditions. This limitation is especially pronounced in Antarctica, which serves as a natural laboratory for climate change research while remaining one of the most observationally constrained regions on Earth due to sparse meteorological infrastructure and logistical challenges. In this study, a locally derived Tm parameterization (HRS) was obtained from radiosonde profiles at near sea-level stations within the 66°S–70°S latitude belt and evaluated against both the existing regional Antarctic Tm model (ANT) and the globally applied GPT2, GPT3 and Bevis models, using radiosonde observations as an independent reference. The HRS parameterization reproduced the radiosonde-derived Tm values with an RMSE of 2.90 K, clearly outperforming the global models (4.20–4.51 K) and showing close agreement with the existing regional Antarctic Tm model (r = 0.987), independently confirming the transferability of the regional approach to the Horseshoe Island region. The GNSS-derived PWV based on the regional Tm parameterization was then evaluated against ERA5 reanalysis data as an independent reference. The estimates showed strong agreement, with a correlation of 0.95, an RMSE of 1.65 mm and a mean bias of +1.22 mm, the seasonal agreement being strongest in austral summer (RMSE ≈ 1.24 mm) and weakest in austral winter (RMSE ≈ 1.92 mm), consistent with the lower water vapor content and stronger surface temperature inversions that characterize the cold season. The close agreement between the two independent TUR1 and TUR2 stations further supports the repeatability of GNSS-based PWV retrieval under coastal Antarctic conditions, highlighting the observational value of these stations for atmospheric water vapor monitoring in Antarctica, where continuous observations remain limited. Full article

(This article belongs to the Special Issue Recent Progress in Monitoring the Troposphere with GNSS Techniques)

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22 pages, 4414 KB

Open AccessArticle

A China-Specific Near-Real-Time GNSS Water Vapor Retrieval Model Based on LightGBM

by Mingchen Zhu, Hui Chang, Zhikang Li, Qian Zhang and Xingwang Fan

Remote Sens. 2026, 18(15), 2637; https://doi.org/10.3390/rs18152637 - 6 Aug 2026

Viewed by 324

Abstract

Atmospheric water vapor is a key atmospheric variable that regulates weather variability, the hydrological cycle, and climate processes. GNSS-based water vapor retrieval provides an effective approach for continuous and near-real-time monitoring of atmospheric water vapor. However, conventional meteorology-independent models still have limitations in regional adaptability, vertical accuracy, and the representation of nonlinear atmospheric variability. To address these limitations, this study proposes a China-specific near-real-time GNSS water vapor retrieval model, termed China LightGBM-based Zenith Hydrostatic Delay and Precipitable Water Vapor Model (CLZP), by integrating LightGBM with near-real-time GNSS observations. First, a high-accuracy gridded ZHD model, CLZP-ZHD, was developed using LightGBM to estimate ZHD from the surface to near the tropopause. Subsequently, a PWV residual compensation model, CLZP-PWV, was developed by incorporating multi-source features, including near-real-time GNSS ZTD, to mitigate error propagation in PWV retrieval. Validation results from both ERA5 and independent radiosonde datasets indicate that CLZP-ZHD reduces RMSE by approximately 25.4% and 28.2% relative to GPT3-ZHD and CTrop-ZHD in the ERA5-based validation, and by 21.7% and 23.0% in the radiosonde-based validation, respectively, while maintaining stable accuracy across different heights. For PWV retrieval, the ERA5-based validation of CLZP-PWV shows that it reduces RMSE by approximately 32.7% and 38.2% relative to GPT3-PWV and CTrop-PWV, respectively, and demonstrates improved performance in humid and climatically complex regions. These results suggest that, once trained, CLZP improves the accuracy and stability of near-real-time GNSS water vapor retrieval over China without requiring in situ meteorological observations during operational application, providing a practical approach for regional GNSS meteorological applications. Full article

(This article belongs to the Special Issue Recent Progress in Monitoring the Troposphere with GNSS Techniques)

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21 pages, 1973 KB

Open AccessArticle

Evaluating Low-Cost GNSS Network Densification for Water-Vapor Tomography over an Urban Area: A Case Study over Lisbon

by Rui Minez, João Catalão and Pedro Mateus

Remote Sens. 2026, 18(8), 1206; https://doi.org/10.3390/rs18081206 - 16 Apr 2026

Cited by 2 | Viewed by 1562

Abstract

This study evaluates GNSS water-vapor tomography across the Lisbon metropolitan area and explores how increasing network density with low-cost receivers improves three-dimensional humidity fields for meteorological applications. Three configurations were tested for December 2022, a month characterized by several rainfall events, including a severe urban-impacting one: (i) a hybrid setup combining permanent and low-cost stations (TOMO_PL), (ii) a dense network of only low-cost stations (TOMO_L), (iii) a sparse arrangement using only permanent stations (TOMO_P). Tomographic water vapor density fields were compared with independent references from the Weather Research and Forecasting (WRF) model, ERA 5 reanalysis, and radiosonde data. All products show the expected exponential decline in water vapor with increasing altitude. Tomography consistently underestimates moisture in the lowest 2.0 to 2.5 km and tends to overestimate it at higher levels, with a weaker correlation above mid-tropospheric heights. Vertical RMSE remains below 2 g m⁻³ for all solutions, but TOMO_P performs the worst due to weak and uneven spatial geometry. Time–height analysis reveals that densified setups capture the changing moisture in the lower atmosphere, including increased near-surface humidity during December 11–13, when rainfall exceeded 120 mm in 24 h, although mid-level intrusions and dry layers observed by radiosondes are not captured. Mean PWV patterns show realistically low points over the Sintra mountain range and align best with TOMO_PL (spatial RMSE 0.6 g m⁻³, bias 0.4 g m⁻³, correlation 0.9), while TOMO_P creates artifacts that mimic mesoscale gradients. Categorized skill analysis shows the highest accuracy under high-moisture conditions and limited ability to detect dry conditions, with TOMO_PL showing the best overall performance against both ERA5 and WRF. Overall, low-cost densification significantly enhances boundary-layer humidity and PWV retrievals, supporting their use for urban heavy-rain monitoring and, with error-aware integration, for short-term forecasting. Full article

(This article belongs to the Special Issue Recent Progress in Monitoring the Troposphere with GNSS Techniques)

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19 pages, 12376 KB

Open AccessArticle

Analysis of GNSS Precipitable Water Vapor and Its Gradients During a Rainstorm in North China in July 2023

by Hualin Su, Yizhu Wang, Yunchang Cao, Hong Liang, Linghao Zhou and Zusi Mo

Remote Sens. 2025, 17(18), 3247; https://doi.org/10.3390/rs17183247 - 19 Sep 2025

Cited by 5 | Viewed by 1743

Abstract

This study presents a water vapor gradient (WVG) retrieval method based on Global Navigation Satellite System (GNSS) tropospheric parameter estimation. A case study examined the method’s applicability to the extreme rainstorm event in North China in July 2023. Precipitable water vapor (PWV) and WVG data from 332 GNSS sites in this area were retrieved. Radar and precipitation data were combined to perform a spatiotemporal comparison study. The results show that GNSS PWV and WVG of this weather process were highly consistent with radar reflectivity and precipitation. When a high PWV (>60 mm) was accompanied by WVG convergence, radar reflectivity was significantly strong and precipitation occurred at the leading edge of large gradients and the convergence region. Based on the edge of big WVGs, observed by multiple GNSS stations, the location and movement of rainfall could be identified. In case of large amounts of PWV accompanied by plummeting WVG (down to 0.1–0.4 mm/km), high or persistent precipitation occurs. During the event, compared to the northern plateau, the plain region demonstrated higher PWV, lesser WVG variation, and more intense precipitation, likely caused by the topographic dynamic effect. GNSS PWV and WVG can be key indicators for short-range weather forecasting of extreme rainstorm events. Full article

(This article belongs to the Special Issue Recent Progress in Monitoring the Troposphere with GNSS Techniques)

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Review

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28 pages, 10210 KB

Open AccessReview

Use of Tropospheric Delay in GNSS-Based Climate Monitoring—A Review

by Aleksandra Maciejewska

Remote Sens. 2025, 17(9), 1501; https://doi.org/10.3390/rs17091501 - 24 Apr 2025

Cited by 14 | Viewed by 5392

Abstract

The troposphere is a key component of the Earth’s climate system, modulating weather patterns and global temperatures through intricate interactions between water vapor, atmospheric pressure, and temperature. Nevertheless, the effective long-term monitoring of tropospheric variations continues to represent a significant challenge in the realm of climate science. While conventional methods such as radiosondes and satellite observations yield valuable data, they frequently face constraints related to temporal resolution, spatial coverage, or weather-dependent variations. In recent years, Global Navigation Satellite System (GNSS) meteorology has emerged as a promising alternative, offering continuous, high-precision atmospheric measurements. The objective of this review is to assess the application of GNSS tropospheric components in climate monitoring. Specifically, the following objectives are pursued: (1) examine how GNSS-derived ZTD, ZWD, and IWV reflect climate variability and long-term trends; (2) compare GNSS-based climate measurements with reanalysis and satellite datasets; (3) discuss the challenges and limitations of using GNSS for climate studies; (4) highlight future developments, including multi-GNSS integration and AI-driven climate data analysis. Full article

(This article belongs to the Special Issue Recent Progress in Monitoring the Troposphere with GNSS Techniques)

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