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The earth surface system is the part of the earth system that is most closely related to human beings. The study of surface processes is becoming more and more important in earth system research. The innovative application of geophysics and geodesy to surface processes has gradually become a new interdisciplinary development direction. Global navigation satellite system (GNSS) observation technology is widely used in the study of surface processes because of its characteristics of high accuracy, all-weather, large range and quasi-real-time. In this paper, the application of GNSS technology in the study of surface processes is briefly introduced from the aspects of long-term crustal deformation, coseismic and post-seismic deformation, atmospheric precipitable water, load response, magma and volcanic activity, landslide monitoring and reflection measurement, and then the future development is discussed. The advantages of GNSS in observation technology highlight the importance of GNSS in the earth surface processes research. [ABSTRACT FROM AUTHOR]

In GNSS (Global Navigation Satellite Systems) meteorology, the accuracy of precipitable water vapor (PWV) retrieved from the tropospheric delay of GNSS signals is affected by the conversion factor. Compact VMF1 product (known as GGOS Atmosphere data) provides high‐accuracy global grid‐wise weighted mean temperature (Tm) values, which can be utilized to calculate the conversion factor. However, the Tm provided in the compact VMF1 data are solely ground surface values. To enhance the performance of compact VMF1 product, a new Tm lapse rate model for each grid point was developed for the purpose of reducing its surface Tm to the elevation of the GNSS site. Then the reduced Tm values over the neighboring grid points together with horizontal interpolation were used to obtain the interpolated Tm for the GNSS station. The sample data for the development of the new model were the Tm profiles obtained from ERA5 monthly averaged data spanning 2009–2018. To assess the model's performance, global radiosonde data at 504 radiosonde stations spanning 2019–2021 were employed. Results demonstrated that implementing the Tm lapse rate model significantly enhanced the accuracy of interpolating Tm values for GNSS stations with substantial height disparities from adjacent grid points, thereby improving PWV conversion accuracy. This indicates that employing the new Tm lapse rate model to adjust surface Tm data in the compact VMF1 product holds promise for enhancing its utility in GNSS meteorology. Plain Language Summary: Global Navigation Satellite Systems (GNSS), including GPS, GLONASS, GALILEO and BDS, are powerful tools for retrieving precipitable water vapor (PWV), a crucial parameter in weather and climate research. The process involves estimating the wet delay embedded in GNSS signals caused by water vapor in the atmosphere and then converting the delay into PWV using a conversion factor based on the weighted mean temperature (Tm). Compact VMF1 product provides high‐accuracy ground surface grid‐wise Tm values, however, when the altitude of the GNSS site largely differs from that of the nearby grid points, the accuracy of the Tm interpolated from the surface Tm may be poor. To improve the accuracy of Tm interpolation and better PWV inversion, we developed a grid‐wise Tm lapse rate model that adjusts for altitude differences. Key Points: A Tm ${T}_{m}$ lapse rate model was developed to enhance the performance of the grid‐wise compact VMF1 Tm ${T}_{m}$ dataThe accuracy of the predicted Tm ${T}_{m}$ and PWV was significantly improvedThe new model adds a good value to GNSS meteorology for better performance [ABSTRACT FROM AUTHOR]

This study evaluates the capability of an unconstrained tomographic algorithm to capture 3D water vapor density variability throughout 2017 in Houston, U.S. The algorithm relies solely on Global Navigation Satellite System (GNSS) observations and does not require an initial guess or other specific constraints regarding water vapor density variability within the tomographic domain. The test domain, featuring 9 km horizontal, 500 m vertical, and 30 min temporal resolutions, yielded remarkable results when compared to data retrieved from the ECMWF Reanalysis v5 (ERA5), regional Weather Research and Forecasting Model (WRF) data, and GNSS-Radio Occultation (RO). For the first time, a time series of Precipitable Water Vapor maps derived from the Interferometric Synthetic Aperture Radar (InSAR) technique was used to validate the spatially integrated water vapor computed by GNSS tomography. Tomographic results clearly indicate the passage of Hurricane Harvey, with integrated water vapor peaking at 60 kg/m2 and increased humidity at altitudes up to 7.5 km. Our findings suggest that GNSS tomography holds promise as a reliable source of atmospheric water vapor data for various applications. Future enhancements may arise from denser and multi-constellation networks. [ABSTRACT FROM AUTHOR]

The accurate measurement of water vapor is essential for research about and the applications of meteorology, climatology, and hydrology. Based on the BeiDou PPP-B2b service, real-time precipitable water vapor (PWV) can be retrieved with the precise point positioning (PPP) software (XTW-PPP version 0.0). The experiment was conducted in Beijing in January 2023. Three solutions were designed with PPP using the BeiDou system only, the GPS system only, and the BeiDou-GPS combined solution. Real-time PWVs for the three solutions were validated with the ERA5 reanalysis data. Between the PWV values from the single BeiDou and ERA5, there was a bias of 0.7 mm and an RMSE of 1.8 mm. For the GPS case, the bias was 0.73 mm and the RMSE was 1.97 mm. The biases were less than 1 mm and RMSEs were less than 2 mm. Both the BeiDou and the GPS processing performed very well. But little improvement was found for the BeiDou-GPS combined solution, compared with the BeiDou system-only and the GPS system-only solution. This may be due to the poor handling of two different kinds of errors for the GPS and the BeiDou systems in our PPP software. A better PWV estimation with the two systems is to estimate PWV with a single system at the first step and then obtain the optimization by Bayesian model averaging. [ABSTRACT FROM AUTHOR]

Atmospheric water vapor is not only a key element of the global hydrological cycle but also the most abundant greenhouse gas. The phase transition and transportation of water vapor are essential for maintaining global energy balance and regulating hydrological processes. However, due to insufficient meteorological observational data, climate research in Africa faces significant limitations despite its substantial contribution to changes in global precipitable water vapor (PWV). In this study, we used MODIS near-infrared (NIR) PWV products and Berkeley temperature data to depict the spatial–temporal variability in PWV across Africa from 2001 to 2020. The results reveal a significant increasing trend in PWV over Africa, with an increase of 0.0158 cm/year. Nearly 99.96% of Africa shows an increase in PWV, with 88.95% of these areas experiencing statistically significant changes, particularly in central regions of Africa. The increase in PWV is more pronounced in high-value months compared to low-value months. The equatorial region of the Congo Basin exhibits higher PWV, which gradually decreases as latitude increases. Despite significant warming (0.0162 °C/year) in Africa, there is no consistent positive correlation between temperature and water vapor. A positive relationship between PWV and temperature is observed in western Africa, while a negative relationship is noted in eastern and southern Africa on an annual scale. Additionally, an increasing trend in precipitation (4.6669 mm/year) is observed, with a significant positive correlation between PWV and precipitation across most of Africa, although this relationship varies by month. These findings provide valuable insights into the comprehension of the hydrothermal variation in Africa amidst climate warming. [ABSTRACT FROM AUTHOR]

A ground-based microwave radiometer (MWR) provides continuous atmospheric profiles under various weather conditions. The change in total precipitable water vapor (PWV) and liquid water path (LWP) before rainfall events is particularly important for the improvement in the rainfall forecast. However, the analysis of the PWV and LWP before rainfall event on the plateau is especially worth exploring. In this study, the MWR installed at Xining, a city located over the northeastern Tibetan Plateau, was employed during September 2021 to August 2022. The results reveal that the MWR-retrieved temperature and vapor density demonstrate reliable accuracy, when compared with radiosonde observations; PWV and LWP values during the summer account for over 70% of the annual totals in the Xining area; both PWV and LWP at the initiating time of rainfall events are higher during summer, especially after sunset (during 20-00 local solar time); and notably, PWV and LWP anomalies are enhanced abruptly 8 and 28 min prior to the initiating time, respectively. Furthermore, the mean of LWP anomaly rises after the turning time (the moment rises abruptly) to the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more dramatically after the turning time in convective cloud events. This study aims are to analyze the temporal characteristics of PWV and LWP, and assess their potential in predicting rainfall event. [ABSTRACT FROM AUTHOR]

In this study, we have presented the results of the precipitable water vapor (PWV) for the Eastern Anatolia Observatory (in Turkish: Doğu Anadolu Gözlemevi, the acronym is DAG) site in Erzurum, Türkiye. The DAG has Türkiye’s largest and the first near infrared (NIR) telescope with a mirror diameter of 4 meters at the altitude of 3170 m. The DAG telescope is going to take the first light in the end of summer 2024. This study is focused on the examining of the precipitable water vapor data for the NIR observations at the DAG. In this context, the NWC SAF Total Precipitable Water (TPW) data obtained by both the satellite based and the radiosonde balloon validated with six radiosonde stations were examined by temporal, vertical and horizontal analyses for the DAG site between June 2019 to December 2020. The results obtained from these analyzes indicate that the mean and median TPW values at the DAG site were approximately 7 mm and the minimum and maximum values were 0.59 mm and 24.12 mm, respectively. The monthly median TPW values at the DAG site varied between approximately 3-10 mm, with a decreasing trend from June to January and an increase in the first seven months of 2020. These results also indicate that the TPW data obtained by its 15 minutes temporal resolution, aligns closely with the radiosonde measurements. Furthermore, the values of PWV at both lower and upper levels of the atmosphere are minimal while the values increase slightly in the middle layer of the atmosphere. As a result, the effective monitoring of the PWV in a site would result in the generation of higher quality astronomical IR observations and be important in terms of the optimum operating cost for an observatory. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In July 2021, a heavy rainstorm was sweeping across Henan Province, causing geological disasters such as floods, mudslides, and landslides, which seriously threatened the safety of human life and property. Precipitable water vapor (PWV) is related to the occurrence and scale of rainfall. Here, based on Global Navigation Satellite System (GNSS) observations, in-situ meteorological files (GMET), ephemeris products, ERA5 data, and weather station data, the relationship between PWV and rainstorm from July 1st to 30th was studied. The results show that GMET and ERA5 in July 2021 have high consistency in some stations, with a root mean square error (RMSE) for temperature below 1.6 °C, for pressure below 0.5 hPa, and for relative humidity below 9 %. During the week before the heavy rainstorm, the temperature dropped remarkably and the temperature difference decreased, while the relative humidity increased and the relative humidity difference decreased. Compared with ERA5 PWV, the RMSE of GNSS PWV retrieved using real-time ephemeris is 3.238 mm. Different from the normal rainfall, we found that the PWV variation during the Henan rainstorm experienced a unique “accumulation” period. We also observed a clear correlation between PWV and the rainstorm, both temporally and spatially. In addition, the PWV in the severely damaged area was 20 mm higher than the average value of the past decade. Ten days after the rainstorm, the surface of this area had subsided by 1.5–3 mm. Finally, we found that the topography of Henan, the low vortex, the north-biased subtropical high, and the double typhoons all played a role in the successful transport and deposition of water vapor.

Abstract Precise modeling of weighted mean temperature (T m ) is essential for Global Navigation Satellite System (GNSS) meteorology. In retrieving precipitable water vapor (PWV) from GNSS, T m is a crucial parameter for the conversion of zenith wet delay (ZWD) into PWV. In this study, an improved T m model, named EGWMT, was developed to accurately estimate T m at any site in Egypt. This new model was established using hourly ERA5 reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) covering the period from 2008 to 2019 with a spatial resolution of 0.25° × 0.25°. The performance of the proposed model was evaluated using two types of data sources, including hourly ERA5 reanalysis data from 2019 to 2022 and radiosonde profiles over a six-year period from 2017 to 2022. The accuracy of the EGWMT model was compared to that of four other models: Bevis, Elhaty, ANN and GGTm-Ts using two statistical quantities, including mean absolute bias (MAB) and root mean square error (RMSE). The results demonstrated that the EGWMT model outperformed the Bevis, Elhaty, ANN and GGTm-Ts models with RMSE improvements of 32.5%, 30.8%, 39% and 48.2%, respectively in the ERA5 data comparison. In comparison with radiosonde data, the EGWMT model achieved RMSE improvements of 22.5%, 34%, 38% and 19.5% against Bevis, Elhaty, ANN and GGTm-Ts models, respectively. In order to determine the significance of differences in means and variances, statistical tests, including t-test and F-test, were conducted. The results confirmed that there were significant differences between the EGWMT model and the four other models.

The pressure and temperature significantly influence precipitable water vapor (PWV) retrieval. Global Navigation Satellite System (GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. First, this article evaluated the accuracy of pressure and temperature in 68 radiosonde stations in China based on ERA5 Reanalysis data from 2015 to 2019 and compared them with GPT3 model. Then, the accuracy of pressure and temperature calculated by ERA5 were estimated in 5 representative IGS stations in China. And the PWV calculated by these meteorological parameters from ERA5 (ERA5-PWV) were analyzed. Finally, the relation between ERA5-PWV and precipitation was deeply explored using wavelet coherence analysis in IGS stations. These results indicate that the accuracy of pressure and temperature of ERA5 is better than the GPT3 model. In radiosonde stations, the mean BIAS and MAE of pressure and temperature in ERA5 are −0.41/1.15 hpa and −0.97/2.12 K. And the mean RMSEs are 1.35 hpa and 2.87 K, which improve 74.77% and 40.58% compared with GPT3 model. The errors of pressure and temperature of ERA5 are smaller than the GPT3 model in bjfs, hksl and wuh2, and the accuracy of ERA5-PWV is improved by 18.77% compared with the GPT3 model. In addition, there is a significant positive correlation between ERA5-PWV and precipitation. And precipitation is always associated with the sharp rise of ERA5-PWV, which provides important references for rainfall prediction.

Accurately measuring precipitable water vapor (PWV) is challenging, especially in the absence of the measured meteorological data. We propose a novel grid model, CZP, that directly converts zenith tropospheric delay to PWV, leveraging ERA5 reanalysis data from 2016 to 2019. The CZP model considers the seasonal variability of the PWV and the spatial characteristics of the conversion coefficients, achieving high accuracy PWV retrieval in the tropics efficiently. Our findings demonstrate the CZP model's high accuracy and reliability across tropical IGS stations, with a mean bias and root mean square (RMS) well within acceptable limits compared to both ERA5, GNSS and radiosonde (RS) PWV measurements. The bias and RMS values for CZP PWV compared to GNSS PWV are less than 1.1 mm and 1.4 mm, while those values with respect to RS PWV are 1.61 mm and 3.11 mm, respectively. Furthermore, the real-time CZP PWV estimates show superior accuracy over the GPT3 model, with mean bias and RMS of − 0.55 mm and 3.55 mm, respectively. Such results underscore the CZP model's potential for enhancing weather prediction and climate research in data-scarce environments. [ABSTRACT FROM AUTHOR]

Due to the difference in the quality of the global assimilation data and the ability to reproduce the real conditions of the atmosphere, the hourly atmospheric temperature at 2 m above the land surface from ERA5 cannot be used with complete confidence for the atmospheric weighted mean temperature ( T m ) calculations and global navigation satellite system (GNSS) precipitable water vapor (PWV) inversion. A systematic and complete refinement method is proposed, including the compensation of elevation matching bias of gridded temperature, correction of fixed-time cusp data fitting and refinement based on the remove-and-restore model. The usability and accuracy improvement of the refined ERA5 2 m atmospheric temperature in the T m and PWV calculation were validated based on three GNSS stations. The result shows that the average accuracy of the T m and PWV for the entire region could be increased by 74.4% and 75.1%, respectively. The RMS of the highest station was reduced from 4.28 K to 0.62 K for the T m and 0.662 mm to 0.203 mm for the PWV, and the RMS of other stations was reduced from 1.25 to 0.44 K for the T m and 0.211 mm to 0.101 mm for the PWV. This overall refinement method has important implications for atmospheric remote sensing. [ABSTRACT FROM AUTHOR]

In July 2021, a heavy rainstorm was sweeping across Henan Province, causing geological disasters such as floods, mudslides, and landslides, which seriously threatened the safety of human life and property. Precipitable water vapor (PWV) is related to the occurrence and scale of rainfall. Here, based on Global Navigation Satellite System (GNSS) observations, in-situ meteorological files (GMET), ephemeris products, ERA5 data, and weather station data, the relationship between PWV and rainstorm from July 1st to 30th was studied. The results show that GMET and ERA5 in July 2021 have high consistency in some stations, with a root mean square error (RMSE) for temperature below 1.6 °C, for pressure below 0.5 hPa, and for relative humidity below 9 %. During the week before the heavy rainstorm, the temperature dropped remarkably and the temperature difference decreased, while the relative humidity increased and the relative humidity difference decreased. Compared with ERA5 PWV, the RMSE of GNSS PWV retrieved using real-time ephemeris is 3.238 mm. Different from the normal rainfall, we found that the PWV variation during the Henan rainstorm experienced a unique "accumulation" period. We also observed a clear correlation between PWV and the rainstorm, both temporally and spatially. In addition, the PWV in the severely damaged area was 20 mm higher than the average value of the past decade. Ten days after the rainstorm, the surface of this area had subsided by 1.5–3 mm. Finally, we found that the topography of Henan, the low vortex, the north-biased subtropical high, and the double typhoons all played a role in the successful transport and deposition of water vapor. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The accuracy of MODIS IR PWV is poor. • The GPS station and MODIS pixel points do not coincide completely in point position. • The problems of spatial interpolation and calibration model are considered in MODIS calibration process. • Compared with the original MODIS IR PWV, the accuracy of the new model is greatly improved in all aspects. • MODIS IR PWV of the new model is in good agreement with GPS PWV. The monitoring of precipitable water vapor (PWV) is of significant importance for meteorological research and rainstorm prediction. The moderate resolution imaging spectroradiometer (MODIS) is one of the most widely used remote sensing PWV instruments aboard the Terra and Aqua satellites. The main objective of this study is to develop a fused inverse distance weighting and the Fourier transform model (FIDWFT) to calibrate MODIS Infrared (IR) PWV. In this research, one year of ground observation data from 17 GPS stations were utilized to evaluate the accuracy of MODIS IR PWV in Hong Kong. Initially, the PWV obtained by one radiosonde was used to verify the GPS PWV. Comparison between GPS and radiosonde showed good agreement with an R-squared of 0.97. After the estimation of GPS PWV in the study area was evaluated, MODIS IR products in the study area were extracted for evaluation. The comparison between MODIS and GPS showed that the R-squared is 0.81 and the mean bias (MB) is −1.60 mm. Considering the spatial interpolation and calibration problems of MODIS products, new model used an inverse distance weighting method to make MODIS IR PWV and GPS PWV completely coincide in space, and fifteen MODIS pixel points were selected for interpolation. Then the MODIS IR PWV was calibrated by GPS PWV using Fourier transform model. Results showed that compared with the MODIS IR PWV before calibration, the R-squared increases from 0.81 to 0.94, the root mean square error decreases from 8.72 mm to 4.98 mm, and the MB decreases from −1.60 mm to −0.83 mm. Therefore, the method is feasible to estimate MODIS IR water vapor, and it achieves better results. [ABSTRACT FROM AUTHOR]

This study estimates the trends in precipitable water vapor (PWV), atmospheric moisture budget (AMB), and the factors influencing them: air temperature, evapotranspiration (ET), convective available potential energy (CAPE), and vertical velocity (Omega) over the Indian subcontinent using ERA5 reanalysis data sets between 1980 and 2020. PWV is examined across three atmospheric layers (1000–850 hPa: lower layer; 850–500 hPa: middle layer; 500–300 hPa: upper layer), and the entire atmospheric column (EAC; 1000–300 hPa). The observed PWV trends exhibit variability within the EAC, ranging from −0.53 to 1.25 mm/decade across the study area, with the middle layer showing the most pronounced variation (–0.44 to 0.83 mm/decade), followed by the lower layer (0.10 to 0.45 mm/decade), and the upper layer (–0.02 to 0.23 mm/decade). These fluctuations in PWV are attributed to changes in air temperature, ET, CAPE, and Omega. This investigation, however, underscores the necessity of delving into the impacts of these influencing factors on PWV using finer resolution data, to enhance our comprehension of its spatial and temporal dynamics in the region. Furthermore, the annual AMB analysis reveals a declining trend in the study region. These findings collectively contribute to the understanding of regional water-energy cycles and the recent shifts in atmospheric dynamics. [ABSTRACT FROM AUTHOR]

Water vapor plays a pivotal role in the intricate processes of energy exchange and climate dynamics. However, a comprehensive understanding of the spatial distribution and temporal variability of water vapor in distinct climatic regions of China is still limited due to the intricate interplay of complex topography and diverse climatic conditions. This study investigates the multiscale variability of water vapor using 10-year 1-hour data of precipitable water vapor (PWV) from 248 observation stations of the Crustal Movement Observation Network of China (CMONOC). Significant variations in PWV distribution among China's climate regions are observed. Average PWV values vary considerably across regions, with the Tropical and Subtropical Monsoon Climate (TSMC) having the highest mean PWV of 32.09 mm, followed by the Midlatitude Monsoon Climate (MMC) with 15.70 mm, the Humid Continental Climate (HCC) with 9.26 mm, and the Plateau Climate (PC) with 7.66 mm. Seasons exhibit summer concentration and winter reduction across regions. Most stations exhibit increasing PWV trends, while a few show decreasing trends. The amplitude of annual PWV variations is higher in TSMC and MMC regions with a range from 8 to 22 mm, compared to HCC and PC regions with a range from 2 to 15 mm. Semiannual variations range from 0 to 6 mm in each region. PWV negatively correlates with latitude, altitude and pressure, particularly in the TSMC. Positive correlations with temperature exist in all regions, with varying strengths. Thermodynamic factors primarily impact PWV, while dynamic factors have secondary influence, equally significant in the PC region. [ABSTRACT FROM AUTHOR]

The atmospheric Precipitable water vapor (PWV) is a variable key for weather forecasting and climate change. It is a considerable component of the atmosphere, influencing numerous atmospheric processes, and having physical characteristics. It can be measured directly using radiosonde stations (RS), which are not always accessible and difficult to measure with acceptable spatial and time precision. This study uses the artificial neural network (ANN) application to propose a simple model based on RS data to estimate PWV from surface metrological data. Ten RS stations were used to develop the new model for eight and a half years. In addition, two and a half years of data were used to validate the developed model. The study period is based on the data accessible between 2010 and 2020. The new model needs to collect (vapor pressure, temperature, latitude, longitude, height, day of year, and relative humidity) as input parameters in ANN to predict the PWV. The ANN model validations were based on the root mean square (RMS), correlation coefficient (CC), and T-test. According to the results, the proposed ANN can accurately predict the PWV over Egypt. The results of the new ANN model and eight other empirical models (Saastamoinen, Askne and Nordius, Okulov et al., Maghrabi et al., Phokate., Falaiye et al. (A&B), Qian et al. and ERA 5) are compared in addition, the new PWV model can achieve the best performance with RMS of 0.21 mm. The new model can serve as a will be of practical utility with a high degree of precision in PWV estimation. [ABSTRACT FROM AUTHOR]

Abstract In recent years, the Global Navigation Satellite System (GNSS) has witnessed rapid development. However, during the sandstorm season, the precipitable water vapor (PWV GNSS ) determined from the GNSS data produces large fluctuations due to the influence of particulate matter, which can indirectly reflect the change in particulate matter concentration. To study the variations of PWV GNSS during the sandstorm season, daily data of PWV GNSS , particulate matter (PM10), and precipitation in Wuhai from 2017 to 2021 were used in this study. The principal components of PWV residual (PWV RPC ) were obtained by using the least-squares linear fitting, singular spectrum analysis, and least-squares spectral analysis on PWV GNSS . The principal components of PM10 (PM10 PC ) were obtained by using least squares linear fitting and singular spectrum analysis for PM10. This study performed a correlation analysis of PWV RPC with PM10 PC and precipitation data. The results showed a strong correlation between PWV RPC and PM10 PC , with a correlation coefficient greater than 0.6. However, it was found that the correlation between PWV RPC and precipitation was not significant. This indicates that during the sandstorm season, PM10 affects PWV determined from GNSS data. Graphical Abstract

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the Global Navigation Satellite System (GNSS) data collected from 12 stations situated in Yunnan, which are part of the Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) data from 2011 to 2022. The retrieved PWV data at Station KMIN were evaluated by the nearby radiosonde data, and the results show that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm, respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the stations' heights and latitudes. Differences between average PWV in the wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. The amplitudes of the PWV annual cycles are 9.75–20.94 mm. The spatial variation of these amplitudes is similar to that of the average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed. [ABSTRACT FROM AUTHOR]

Future climate change is expected to increase the risk and severity of wildland fires in tropical regions. Synoptic-scale fire weather conditions in Mexico were carefully analyzed using 20 years of satellite hotspot and rainfall data, hourly weather data, and various climate data. Fire analysis results showed that eighty-four percent of all fires in Mexico occurred south of 22° N. Southwest Mexico (SWM, N < 22°, 94–106° W) and Southeast Mexico (SEM, N < 22°, 86–94° W), account for 50% and 34% of all fires in Mexico. Synoptic-scale analysis results using hourly data showed that westerly wind sea breezes from the Pacific Ocean blow toward the coastal land areas of the SWM while easterly wind sea breezes from the Caribbean blow into the SEM. The most sensitive weather parameters were "relative humidity" for the SWM and "temperature" for the SEM. The fire-related indices selected were "precipitable water vapor anomaly" for the SWM and "temperature anomaly" for the SEM. The SWM fire index suggests that future fires will depend on dryness, while the SEM fire index suggests that future fires will depend on temperature trends. I do hope that this paper will improve local fire forecasts and help analyze future fire trends under global warming in Mexico. [ABSTRACT FROM AUTHOR]

Water vapor is one of the important atmospheric components of atmospheric circulation and dynamics, and its accurate and spatiotemporally continuous estimation is important for understanding the water vapor conveying mechanism and the hydrologic cycle. We proposed a new fusion strategy where the precipitable water vapor (PWV) derived from global positioning system (GPS) and surface meteorological observations was introduced. Compared to previous methods using radiosonde data, remote sensing satellite water vapor products, and atmospheric reanalysis products, our fusion strategy adopted the PWV calculated from the surface meteorological observations for the first time. It also has a potential near real-time capability for high temporal resolution water vapor monitoring in areas with sparse GPS station distribution (the average distance exceeds 100 km) such as mountainous areas, basins, and plateaus. Based on PWV data derived from 35 GPS and 237 surface meteorological stations, we established hourly fusion models through simplified spherical cap harmonic analysis for the Loess Plateau of China. The validation results show that when PWV data derived from ERA5 reanalysis and radiosonde in 2020 were used as reference values, the mean root-mean-square (RMS) of the PWV derived from fusion models based on GPS and surface meteorological observations were 3.87 and 2.51 mm, respectively. Compared to the strategy using only GPS observations, the accuracy of the model PWV was improved by approximately 60.4% by fusing GPS and surface meteorological PWV data when the ERA5 data is used as a references value. Compared to the strategy using only surface meteorological observations, the accuracy of the model PWV was respectively improved by approximately 16.0% and 32.7% after fusing GPS-derived PWV data when the ERA5 and radiosonde data are used as reference values. Therefore, through our fusion strategy, the accuracy of GPS PWV data and the spatial resolution of surface meteorological PWV data could be complementary in regard to water vapor field estimation. This study shows that the fusion strategy has a potential application prospect in near real-time water vapor monitoring for numerical weather predictions (NWP) data assimilation and short-term rainfall forecasting. [ABSTRACT FROM AUTHOR]

Objectives: In order to explore the impact of the decrease of human activities on the air quality in China during the period of the public health emergencies in 2019, the temporal and spatial abnormal changes of aerosol optical depth (AOD), precipitable water vapor (PWV) and temperature (T) are analyzed, and the impact of human activities on the air quality is revealed. Methods: First, the accuracy of AOD, PWV and T is verified by comparing with AOD provided by aerosol robotic network and PWV and T provided by radiosonde. Second, we analyze the long-term trends of AOD, PWV and T during the weekend and the week, and find that human activities have a certain impact on the air quality. Third, the temporal and spatial changes of AOD, PWV and T during the period of public health emergencies are studied, which confirmed that there is a good correlation between human activities and air quality. Finally, 184 cities of different grades in China are selected for further analysis to determine the impact of population density on AOD, PWV and T, and further reveal the specific response relationship between human activities and air quality. Results: Through the verification of the accuracy of the used data, it is found that the selected data have high accuracy, which can be used in this experimental study. By analyzing the PWV,AOD and T changes in public health emergencies, it is found that PWV, AOD and T are all affected. Conclusions: Due to the influence of public health emergencies, AOD, PWV and T show different trends. At the same time, it is found that the main reason for this phenomenon is the influence of the intensity of human activities. [ABSTRACT FROM AUTHOR]

Precipitable Water Vapor (PWV) has strong relations with extreme rainfall and their increments in a future warming world are typically associated. It is, however, unclear how different climatic conditions and orographic effects modulate these changes in the equatorial region. We investigate PWV and heavy rainfall over Ethiopia using Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX). An in-depth RCM evaluation is first provided by comparing the modeled annual cycle of PWV with those obtained from Global Positioning System observations and reanalysis, and, by investigating the changes in PWV before and after a heavy-rainfall event. Two characteristic timescales are found for the buildup and decline of PWV before and after such events: a short of about 2 days and a long timescale extending beyond ten days. Overall RCMs reproduce well the PWV annual cycle but substantial biases appear for some models in the very dry and in the tropical wet climate zones. CORDEX models simulate well the peak in PWV anomalies at the day of a heavy-rainfall event but strongly overestimate the timescales of buildup and decline. Future scenarios all point towards a PWV increase (up to 40%) for end-of-the-century RCP8.5 with limited spatial and seasonal variations. PWV changes align with near-surface temperature changes at a rate of 7.7% per degree warming. Changes in daily heavy rainfall, on the other hand, are lower especially in northwestern Ethiopia in the far future (RCP8.5), potentially caused by an overall drying. [ABSTRACT FROM AUTHOR]

In the early April 2021, the tropical cyclone Seroja was formed over the Savu Sea in the southeastern Indonesia. Seroja provided a unique opportunity to observe a tropical cyclone over the Indonesian region using ground-based global navigation satellite systems (GNSS) observations. Precipitable water vapor (PWV) from several permanent GNSS stations in the region was utilized to detect Seroja. From in situ meteorological observations, we found that surface pressure values dropped by more than 14 hPa during Seroja, relative humidity increased, and temperature was reduced. PWV at two nearest stations showed an upward trend (around 70 mm at its peak) during the formation of the cyclone, then dropped immediately (less than 20 mm). After Seroja, the mean PWV was lower (56 mm before and 39 mm after), whereas the standard deviation was higher (5–6 mm before and 9 mm after). We also compared hourly PWV with precipitation from the global satellite mapping of precipitation (GSMaP). Before Seroja, some precipitation events occurred, followed by heavy rains that lasted for several days when the cyclone was passing. After Seroja had passed, both PWV and precipitation dropped significantly. However, while PWV values after Seroja were fluctuating, no rain occurred. We then investigated the water vapor budget to understand the change of PWV over time. We found that precipitation and the divergence of moisture flux played an important role in the change of PWV over time. Heavy precipitation during Seroja resulted in a drop in PWV, although the negative divergence provided a bit of offset. After Seroja had passed, no precipitation occurred, and the change of PWV could be attributed mainly to the moisture divergence. The lagged correlation between PWV and precipitation was determined using moving average over the time series. The highest correlation was found 1–2 days before the event with moving average periods of 7 and 10 days. [ABSTRACT FROM AUTHOR]

Accurately retrieving precipitable water vapor (PWV) over wide‐area land surface remains challenging. Unlike passive infrared remote sensing, passive microwave (PMW) remote sensing provides almost all‐weather PWV retrievals. This study develops a PMW‐based land PWV retrieval algorithm using automated Machine learning (ML) (AutoML). Data from the Advanced Microwave Scanning Radiometer 2 serve as the main predictor variables and high‐quality Global Positioning System (GPS) PWV data as the target variable. Unprecedentedly large GPS training samples (over 50 million) from more than 12,000 stations worldwide are used to train the AutoML model. New predictors with clear physical mechanisms enable PWV retrieval over almost any land surface type, including snow cover and near open water. Validation shows good agreement between PWV retrievals and ground observations, with a root mean square error of 3.1 mm. This encouraging outcome highlights the potential of the algorithm for application with other PMW radiometers with similar wavelengths. Plain Language Summary: Precipitable water vapor plays a critical role in the global hydrological cycle, but retrieving its value from remote‐sensed data is challenging, especially for scientific purposes that require high resolution and accuracy. This work proposes a new retrieval algorithm, which is attractive on three accounts. First is the use of information from the microwave radiometer onboard a solar‐synchronous‐orbit satellite, which has a high spatiotemporal resolution. The second attraction is the use of automated machine learning (AutoML), which could circumvent the complex model selection and tuning processes that are typically involved in machine‐learning tasks. Third, an unprecedented large ground‐based data set is gathered from Global Positioning System stations worldwide, which is to be used as target variables for AutoML training. The validation results reveal that the precipitable water vapor retrieval is remarkably successful over all land surface types, which is rarely seen before. The proposed algorithm can also be transferred and used with radiometers onboard other satellites. Key Points: A machine‐learning‐based passive microwave land precipitable water vapor (PWV) retrieval method is developed using the latest enhanced Global Positioning System PWV data setAdding new features with clear physical meanings improves the PWV retrieval accuracy by about 30%The model performs well in areas that have been excluded in previous studies, such as open waters and permanently frozen areas [ABSTRACT FROM AUTHOR]

Missing data in precipitable water vapor derived from global navigation satellite systems (GNSS-PWV) is commonly a large hurdle in climatical applications, since continuous PWV is an important prerequisite. Interpolation using principal component analysis (PCA) is typically used to resolve this problem. However, the popular PCA-based interpolating methods, e.g., rank-deficient least squares PCA (RDPCA) and data interpolating empirical orthogonal function (DINEOF), often lead to unsatisfactory results. This study analyzes the relationship between missing data and PCA-based interpolation results and proposes an improved interpolation-based RDPCA (IRDPCA) that can take into account the PWV derived from ERA5 (ERA-PWV) as an additional aid. Three key steps are involved in the IRDPCA: initially interpolating missing data, estimating principal components through a functional model and optimizing the interpolation through an iterative process. Using a 6-year GNSS-PWV over 26 stations and ERA-PWV in Yunnan, China, the performance of the IRDPCA is compared with the RDPCA and DINEOF using simulation experiments based on both homogeneous data (i.e., interpolating ERA-PWV using available ERA-PWV) and heterogeneous data (i.e., interpolating GNSS-PWV using ERA-PWV). In the case of using homogeneous data, the root mean square (RMS) values of the interpolation errors are 3.45, 1.18 and 1.17 mm for the RDPCA, DINEOF and IRDPCA, respectively; while the values are 3.50, 2.50 and 1.55 mm in the heterogeneous case. These results demonstrate the superior performance of the IRDPCA in both the heterogeneous and homogeneous cases. Moreover, these methods are also applied to the interpolation of the real GNSS-PWV. The RMS, absolute bias and correlation of the GNSS-PWV are calculated by comparison with ERA-PWV. The results reveal that the interpolated GNSS-PWV using the IRDPCA is not impacted by the systematic discrepancies in the ERA-PWV and agrees well with the original data. [ABSTRACT FROM AUTHOR]

Observations of precipitable water vapor (PWV) in the atmosphere play a crucial role in weather forecasting and global climate change research. Spaceborne Interferometric Synthetic Aperture Radar (InSAR), as a widely used modern geodetic technique, offers several advantages to the mapping of PWV, including all-weather capability, high accuracy, high resolution, and spatial continuity. In the process of PWV retrieval by using InSAR, accurately extracting the tropospheric wet delay phase and obtaining a high-precision tropospheric water vapor conversion factor are critical steps. Furthermore, the observations of InSAR are spatio-temporal differential results and the conversion of differential PWV (InSAR ΔPWV) into non-difference PWV (InSAR PWV) is a difficulty. In this study, the city of Jinan, Shandong Province, China is selected as the experimental area, and Sentinel-1A data in 2020 is used for mapping InSAR ΔPWV. The method of small baseline subset of interferometry (SBAS) is adopted in the data processing for improving the coherence of the interferograms. We extract the atmosphere phase delay from the interferograms by using SRTM-DEM and POD data. In order to evaluate the calculation of hydrostatic delay by using the ERA5 data, we compared it with the hydrostatic delay calculated by the Saastamoinen model. To obtain a more accurate water vapor conversion factor, the value of the weighted average temperature Tm was calculated by the path integral of the ERA5. In addition, GNSS PWV is used to calibrate InSAR PWV. This study demonstrates a robust consistency between InSAR PWV and GNSS PWV, with a correlation coefficient of 0.96 and a root-mean-square error (RMSE) of 1.62 mm. In conclusion, our method ensures the reliability of mapping PWV by using Sentinel-1A interferograms and GNSS observations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AbstractThe Sichuan Luding earthquake that struck on September 5, 2022 is one of the strongest earthquakes in China in recent years. The analysis of precipitable water vapor (PWV) retrieved from the ground-based global navigation satellite system (GNSS), surface pressure (SP), surface latent heat flux (SLHF), and land surface temperature (LST) from the reanalysis dataset was carried out in the epicenter and the nearby areas. The results show that PWV decreases distinctly and reaches the trough at the outburst with significant minimums of 43.21 mm and 37.84 mm over the nearest SCSM and SCTQ station from the epicenter. SLHF also has the same trend, and SP increased. Additionally, the LST analysis from two-temporal series was conducted to reveal that the Luding event accompanies by a low-temperature anomaly. Based on the background field established from the same period of the last ten years, LST at the epicenter on the day of occurrence was 5.68 °C lower than in previous years. Furthermore, the strongest low-temperature anomalies were observed from September 4 to 6, with the anomaly index of −1.95, −1.71, and −1.60, respectively. It is plain that the parameters from the land and atmosphere perform the anomalies at the minimum during the Luding earthquake.

Aiming at the complex variation of haze and the influence of various factors, Xi'an is taken as the research area to study the qualitative and quantitative issues between aerosol optical depth (AOD) and haze before and after correction. Combining atmospheric water vapor content (PWV) and meteorological factor data, it is proposed to use "backward screening" method to carry out regression modeling and verification of the revised PM, s mass concentration, AOD, PWV and meteorological factors. The results show that the correlation between AOD and PM2 s is significantly improved after vertical correction and humidity correction. From the model's decision coefficient A2 and the relative error of the estimated PM2 s mass concentration, it can be seen that the estimation model of PM, s mass concentration based on multiple impact factors is better than the estimation model solely based on AOD. [ABSTRACT FROM AUTHOR]

Precipitable Water Vapor (PWV) constitutes a pivotal parameter within the domains of atmospheric science, and remote sensing due to its profound influence on Earth's climate dynamics and weather patterns. It exerts a significant impact on atmospheric stability absorption and emission of radiation, thus engendering alterations in the Earth's radiative equilibrium. As such, precise quantification of PWV holds the potential to enhance weather prognostication and fortify preparedness against severe meteorological phenomena. This study aimed to elucidate the spatial and temporal changes in seasonal and annual PWV across the Indus River Basin and its sub-basins using ERA5 reanalysis datasets. The present study used ERA5 PWV (entire atmospheric column), air temperature at 2 m (t2m) and 500 hPa (T_500hPa), evapotranspiration, and total cloud cover data from 1960 to 2021. Theil Sen slope estimator and Mann-Kendall test were used for trend analysis. Correlation and multiple regression methods were used to understand the association of PWV with other factors. The findings have unveiled the highest increase in mean PWV during the monsoon (0.40 mm/decade), followed by pre-monsoon (0.37 mm/decade), post-monsoon (0.27 mm/decade), and winter (0.19 mm/decade) throughout the study period. Additionally, the mean PWV exhibited the most pronounced positive trend in the sub-basin Lower Indus (LI), followed by Panjnad (P), Kabul (K), and Upper Indus (UI) across all seasons, except winter. Annual PWV has also risen in the Indus basin and its sub-basins over the last six decades. PWV exhibits a consistent upward trend up to an elevation of 3500 m within the basin which is most pronounced during the monsoon season, followed by the pre-monsoon. The escalating PWV within the basin is reasonably ascribed to increasing air temperatures, augmented evapotranspiration, and heightened cloud cover. These findings hold potential utility for pertinent authorities engaged in water resource management and planning. [ABSTRACT FROM AUTHOR]

Atmospheric water vapor plays a prominent role in weather forecasting and climate change, which can be measured accurately with conventional water vapor observing techniques and the global navigation satellite system (GNSS). However, there are limited studies that assess the retrieval of PWV exclusively using Beidou over oceans, as well as for GLONASS and Galileo. In this contribution, we investigate retrieving the real-time precipitable water vapor (PWV) based on shipborne GNSS kinematic precise point positioning (PPP) solutions through an 8-days experiment over the South China Sea. Observations from multi-constellation and single-constellation, including GPS, GLONASS, Galileo, and Beidou are processed. Real-time shipborne GNSS PWV is validated using ERA5 PWV products. The results obtained from the single-constellation analysis indicate that Beidou performs comparably to Galileo in PWV retrieval, surpasses GLONASS, but slightly falls behind GPS. It exhibits an accuracy of 3.19 mm when compared to the ERA5 PWV products after an average initialization time of 43.9 min. Furthermore, it is demonstrated that real-time multi-GNSS PWV achieves an accuracy improvement of more than 15% compared to single-constellation resolutions, reaching an accuracy of 2.34 mm. Real-time shipborne GNSS can accurately sense atmospheric water vapor over oceans and contribute to time-critical meteorological applications. [ABSTRACT FROM AUTHOR]

Heavy rainfall events usually require sufficient water vapor and intense upward movement of airflow. However, current heavy rainfall prediction models using Global Navigation Satellite System (GNSS)-derived precipitable water vapor (PWV) usually use linear features of PWV (PWV increase and decrease, PWV derivative, etc.), which ignore the process of airflow motion. In this study, an hourly heavy rainfall prediction model using the linear and nonlinear features of GNSS-derived PWV is proposed. It uses five linear and three nonlinear features of PWV, together with the corresponding meteorological data, and uses the Random Forest algorithm to predict heavy rainfall or not. The dataset uses the HKSC station observations in Shamshuipo, Hong Kong and the co-located meteorological station data each summer between 2013 and 2020. The results show that the true detection rate and false alarm rate of the proposed model are 97.4% and 19.6% respectively, which are significant improvements compared with other models. [ABSTRACT FROM AUTHOR]

Prolonged exposure to high concentrations of suspended particulate matter (SPM), especially aerodynamic fine particulate matter that is ≤2.5 μm in diameter (PM2.5), can cause serious harm to human health and life via the induction of respiratory diseases and lung cancer. Therefore, accurate prediction of PM2.5 concentrations is important for human health management and governmental environmental management decisions. However, the time-series processing of PM2.5 concentration based only on a single region and a special time period is less explanatory, and thus, the spatial-temporal applicability of the model is more restricted. To address this problem, this paper constructs a PM2.5 concentration prediction optimization model based on Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM). Hourly data of atmospheric pollutants, meteorological parameters, and Precipitable Water Vapor (PWV) of 10 cities in the Beijing-Tianjin-Hebei metropolitan area during the period of 1–30 September 2021/2022 were used as the training set, and the PM2.5 data of 1–7 October 2021/2022 were used for validation. The experimental results show that the CNN-LSTM model optimizes the average root mean square error (RMSE) by 25.52% and 14.30%, the average mean absolute error (MAE) by 26.23% and 15.01%, and the average mean absolute percentage error (MAPE) by 35.64% and 16.98%, as compared to the widely used Back Propagation Neural Network (BPNN) and Long Short-Term Memory (LSTM) models. In summary, the CNN-LSTM model is superior in terms of applicability and has the highest prediction accuracy in the Beijing-Tianjin-Hebei metropolitan area. The results of this study can provide a reference for the relevant departments in the Beijing-Tianjin-Hebei metropolitan area to predict PM2.5 concentration and its trend in specific time periods. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is a fundamental parameter in measuring atmospheric water vapor. By using the precise point positioning (PPP) technique, the PWV can be retrieved by Global Positioning System (GPS) satellites. Considering that the accuracy of PPP is highly dependent on the quality of the precise products provided by different analysis centers, this study thoroughly investigates the performance of GPS-ZTD (zenith total delay) and GPS-PWV derived from precise products of different analysis centers. The ZTD estimated by the GPS PPP is compared to IGS-ZTD (International Global Navigation Satellite Systems (GNSS) Service), and the results show that the correlation coefficient between them can reach approximately 0.96. In addition, to eliminate the influence of weighted mean temperature on GPS-PWV, three traditional methods of estimation of weighted mean temperature are evaluated. Results indicate that the bias of Braun model is smallest, which is only 1.3 K. Thus, the Braun model is adopted to estimate the GPS-PWV. The GPS-PWV based on the Braun model and IGS products is compared with RS-PWV (radiosonde) and ERA5-PWV (European Centre for Medium-Range Weather Forecasts Re-Analysis 5). The correlation coefficient between GPS-PWV and RS-PWV is approximately 0.97, and between GPS-PWV and ERA5-PWV is about 0.99. These results demonstrate that the GPS-PWV is reliable and stable. Then, the performance of GPS-PWV and rainfall event prediction based on GPS-PWV for different analysis centers are compared and analyzed. In terms of the GPS-PWV, the difference between these analysis centers is very small, the largest average bias is only 0.18 mm. However, for the rainfall event prediction, the maximum bias of successful prediction rate can reach approximately 11.43%. Thus, it can be concluded that the influence of precise products obtained from different analysis centers should be considered for rainfall event prediction based on GPS-PWV. [ABSTRACT FROM AUTHOR]

In recent years, the Global Navigation Satellite System (GNSS) has witnessed rapid development. However, during the sandstorm season, the precipitable water vapor (PWVGNSS) determined from the GNSS data produces large fluctuations due to the influence of particulate matter, which can indirectly reflect the change in particulate matter concentration. To study the variations of PWVGNSS during the sandstorm season, daily data of PWVGNSS, particulate matter (PM10), and precipitation in Wuhai from 2017 to 2021 were used in this study. The principal components of PWV residual (PWVRPC) were obtained by using the least-squares linear fitting, singular spectrum analysis, and least-squares spectral analysis on PWVGNSS. The principal components of PM10 (PM10PC) were obtained by using least squares linear fitting and singular spectrum analysis for PM10. This study performed a correlation analysis of PWVRPC with PM10PC and precipitation data. The results showed a strong correlation between PWVRPC and PM10PC, with a correlation coefficient greater than 0.6. However, it was found that the correlation between PWVRPC and precipitation was not significant. This indicates that during the sandstorm season, PM10 affects PWV determined from GNSS data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Temperature and pressure play key roles in Global Navigation Satellite System (GNSS) precipitable water vapor (PWV) retrieval. The National Aeronautics and Space Administration (NASA) and European Center for Medium-Range Weather Forecasts (ECMWF) have released their latest reanalysis product: the modern-era retrospective analysis for research and applications, version 2 (MERRA-2) and the fifth-generation ECMWF reanalysis (ERA5), respectively. Based on the reanalysis data, we evaluate and analyze the accuracy of the surface temperature and pressure products in China using the the measured temperature and pressure data from 609 ground meteorological stations in 2017 as reference values. Then the accuracy of the two datasets and their performances in estimating GNSS PWV are analyzed. The PWV derived from the pressure and temperature products of ERA5 and MERRA-2 has high accuracy. The annual average biases of pressure and temperature for ERA5 are −0.07 hPa and 0.45 K, with the root mean square error (RMSE) of 0.95 hPa and 2.04 K, respectively. The annual average biases of pressure and temperature for MERRA-2 are −0.01 hPa and 0.38 K, with the RMSE of 1.08 hPa and 2.66 K, respectively. The accuracy of ERA5 is slightly higher than that of MERRA-2. The two reanalysis data show negative biases in most regions of China, with the highest to lowest accuracy in the following order: the south, north, northwest, and Tibet Plateau. Comparing the GNSS PWV calculated using MERRA-2 (GNSS MERRA-2 PWV) and ERA5 (GNSS ERA5 PWV) with the radiosonde-derived PWV from 48 co-located GNSS stations and the measured PWV of the co-location radiosonde stations, it is found that the accuracy of GNSS ERA5 PWV is better than that of GNSS MERRA-2 PWV. These results show the different applicability of surface temperature and pressure products from MERRA-2 and ERA5 data, indicating that both have important applications in meteorological research and GNSS water vapor monitoring in China.