Global Navigation Satellite System Precipitable Water Vapor Research Articles

An improved global Tm stratification model for GNSS-PWV retrieval

The atmospheric weighted mean temperature (Tm) is a key parameter for converting tropospheric wet delay (ZWD) into precipitable water vapor (PWV) and plays a vital role in Global Navigation Satellite System (GNSS) meteorology. Existing Tm models suffer from limitations such as the lack of consideration of full periodicity, poor accuracy at high altitudes, and the adoption of single-resolution grid data for modeling. Hence, an improved global Tm stratification model is developed based on ERA5 hourly reanalysis data from 2015 to 2019, called IGTmS, which takes seasonal and intraday variations into consideration and adopts a stratification mode to improve the vertical accuracy of Tm estimation. The accuracy and applicability of the IGTmS model are evaluated using Tm data at 417 radiosonde stations in 2020. The results show that the mean bias and RMS of Tm for the IGTmS model are −0.34 K and 3.53 K, and the accuracy is improved by approximately 7.8 %, 4.6 % and 2.5 % compared with that of GPT3, GTm-III and GTrop models on a global scale, respectively. IGTmS model can achieve the mean σPWV and σPWV/PWV values of 0.38 mm and 1.3 %, respectively, which shows optimal performance among the four models, especially in the Antarctic and Tibetan Plateau regions. Furthermore, three resolutions of the IGTmS models have been developed and all show high accuracy compared to GPT3 models. Users can choose the suitable model according to the desired accuracy and resolution. The developed stratification model IGTmS can effectively weaken the influence of large height difference on Tm estimation and can obtain high-accuracy and stable Tm over the globe and provide data support for real-time GNSS PWV retrieval.

High-resolution reconstruction and correction of FY-4A precipitable water vapor in China using back propagation neural network

Precipitable water vapor (PWV) with high spatial and temporal resolution is essential for a deeper understanding of the study area's hydrological cycle and climate change. This study uses the European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) PWV as auxiliary verification data and proposes a reconstruction method based on back propagation neural network (BPNN) downscaling. Combining with high temporal resolution meteorological elements, digital elevation model (DEM), location, and time, reconstructed the Fengyun-4A (FY-4A) PWV data set with high spatial and temporal resolution in China was obtained. Subsequently, using the characteristics of FY-4A PWV with DEM, location, and time, a BPNN-based correction method is proposed to correct the reconstructed FY-4A PWV using the high-precision global navigation satellite system (GNSS) PWV. The results showed that the root mean square error (RMSE), Bias, and Pearson correlation coefficient (R) of the reconstructed model with respect to the FY-4A PWV are 1.32 mm, 0 mm, and 0.99, respectively. Compared with the auxiliary verification data ERA5 PWV, the RMSE, Bias, and R of the reconstructed FY-4A PWV are 0.85 mm, 0.18 mm, and 0.99, respectively. Compared with the GNSS PWV, the RMSE of the corrected FY-4A PWV is 2.44 mm, which is 28.86% lower than that of the reconstructed FY-4A PWV, the Bias is improved from −1.46 mm to 0 mm, and the R is enhanced from 0.98 to 0.99. The reconstruction and correction method of FY-4A PWV with high spatial and temporal resolution proposed in this study dramatically improves the completeness and accuracy of FY-4A PWV in China. It lays the foundation for studying rapid changes in China's hydrological cycle and climate.

Improving GNSS PPP Performance in the South China Under Different Weather Conditions by Using the Weather Research and Forecasting (WRF) Model‐Derived Wet Delay Corrections

AbstractAtmospheric wet delay caused by Precipitable Water Vapor (PWV) significantly impacts the performance of many geodetic surveying systems such as Global Navigation Satellite System (GNSS). In this study, we use wet delay corrections forecast by the Weather Research and Forecasting (WRF) model to enhance GNSS Precise Point Positioning (PPP) during two observation periods with two different weather conditions, that is, period 1: March 01 to 14, 2020 (average PWV: 23.5 kg/m2) and period 2: June 02 to 15, 2020 (flooding weather with average PWV: 55.6 kg/m2), over the South China. PWV data from 277 to 263 GNSS stations are assimilated into WRF model to enhance the WRF water vapor forecasting capability for period 1 and period 2, respectively. Wet delay corrections from two different WRF configurations, that is, WRF no data assimilation and WRF with assimilation of GNSS PWV, are used to augment the PPP. Totally, eight WRF‐enhanced PPP schemes are tested. The results show that WRF‐enhanced PPP schemes generally have a better positioning performance in the up component than traditional PPP. After using WRF wet delay corrections, for static mode, the vertical positioning accuracy is improved by 14.6% and 33.7% for period 1 and period 2, respectively. The corresponding convergence time are reduced by 41.8% and 25.0% for period 1 and period 2, respectively. For kinematic mode, the positioning accuracy improvements in the up component reach 13.8% and 19.0% for period 1 and period 2, respectively. The kinematic PPP convergence time is reduced by up to 8.2% for period 1.

Revealing the water vapor transport during the Henan “7.20” heavy rainstorm based on ERA5 and Real-Time GNSS

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.

The GNSS PWV retrieval using non-observation meteorological parameters based on ERA5 and its relation with precipitation

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.

Investigating the ERA5-Based PWV Products and Identifying the Monsoon Active and Break Spells with Dense GNSS Sites in Guangxi, China

Precipitable water vapor (PWV) with high precision and high temporal resolution estimated by Global Navigation Satellite System (GNSS) is widely used in atmospheric research and weather forecasting. However, most previous works are not consensual concerning the characteristics of the PWV at different time scales and the identification of active and break spells during summ er monsoon climate in Guangxi, China. Taking radiosonde (RS) observations as reference, a strong correlation (R > 0.97) exists between GNSS PWV and RS PWV with a mean root mean square error (RMSE) of 2.68 mm. The annual, seasonal, monthly, and diurnal PWV variations of three years (2017, 2018 and 2020) over Guangxi in were comprehensively investigated using 104 GNSS stations and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5). The mean annual bias and RMSE between GNSS PWV and ERA5 PWV are −1.04 mm and 2.63 mm, respectively. The monthly bias and RMSE range are −0.77 to 3.87 mm, 1.32 to 4.45 mm, and the daily range is −1.41 to 1.07 mm and 1.11 to 5.02 mm, respectively. Additionally, the adopted average standardized rainfall anomaly criteria also identified 7/7/3 active spells and 5/3/7 break spells during the summer monsoon (June–September) from 2017 to 2020, respectively. During the three-year period, the daily amplitude ranges for active spells varied from 1.41 to 2.49 mm, 0.69 to 5.4 mm, and 0.88 to 1.41 mm, while the ranges for break spells were 2.45 to 6.76 mm, 1.66 to 8.17 mm, and 1.48 to 2.99 mm, respectively. The results show a superior performance of GNSS PWV compared to ERA5 PWV in Guangxi, and the maximum, minimum and occurrence time of PWV anomaly vary slightly with the season and the topography of stations. Despite temperature primarily exhibiting a negative correlation with rainfall, acting as a dampener, a positive correlation remains evident between PWV and rainfall. Therefore, densely distributed GNSS stations exhibit excellent capabilities in quantifying atmospheric water vapor and facilitating real-time monitoring of small and medium-scale weather phenomena.

An ERA5 based local modelling of weighted mean temperature over hilly region in India for improved spatiotemporal analysis of extreme weather event using GNSS PWV

Precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) offers the benefits of high spatiotemporal resolution logging-consistency and all-weather operability. Estimation of water vapor weighted mean temperature (Tm) is a crucial part and a major error source in extracting GNSS PWV from atmospheric delay. Empirical modelling of Tm is a convenient approach, especially in cases where, the meteorology community suffers from inefficient and inconsistent logging of temperature and humidity profiles etc., in the existing meteorological observing systems, particularly for harsh weather conditions. In a first, a novel site specific model (Tm-SSM) is developed for the atmospheric weighted mean temperature (Tm) for 13 CORS (continuously operating reference stations) in the Survey of India (SOI) network, recently established in the hilly state of Uttarakhand (UK) using the ERA5 reanalysis data from 2016 to 2018. GNSS observations at 10 CORS are then used to observe the spatiotemporal characteristics of PWV from Tm-SSM during the heavy rainfall event in October 2021. The newly developed Tm-SSM performs better with the mean bias error (MBE) and root mean square error (RMSE) of 0.498 and 2.9 K, respectively, when compared with other globally accepted models. The Tm-SSM PWV estimates are well correlated with the precipitation data from AWS stations. The newly developed Tm-SSM is well conditioned and highly stable, with the mean stability indicator value as low as 1.043%. Further, a close relationship can be observed from the spatiotemporal characteristics of GNSS PWV during heavy rainfall event, with the in-situ precipitation measurements. It is apparent from the results that PWV steadily builds up before the arrival of rain, with sharp peaks in PWV observed just before rainfall event. It then, follows a downward slope through which it decreases to a stable value of around 30 mm when the rainfall ceases. The fluctuations in the PWV from Tm-SSM and the passage of the severe mesoscale convective system in a complex terrain such as UK were shown to have high spatial and temporal correlation, which is significant for the monitoring and forecasting such extreme weather. The findings imply that GNSS PWV represents the compendious signature of the water vapor dynamics during severe weather, demonstrating the potential to improve the early detection and sensing of severe weather.

Linear and nonlinear GNSS PWV features for heavy rainfall forecasting

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.

Assimilating GNSS PWV and radiosonde meteorological profiles to improve the PWV and rainfall forecasting performance from the Weather Research and Forecasting (WRF) model over the South China

Precipitable Water Vapor (PWV) and rainfall play important roles in the meteorological processes. In this study, we have investigated the impact of assimilation of Global Navigation Satellite System (GNSS) PWV and radiosonde profiles on the performance of Weather Research and Forecasting (WRF) model in forecasting PWV and rainfall over the South China region for April 01, 2020 to May 31, 2020. PWV observations derived from 213 GNSS stations and meteorological profiles recorded by 23 radiosonde stations are assimilated into WRF model. Five different WRF schemes are adopted: WRF scheme 0: no data assimilation (DA); WRF scheme 1: assimilation of PWV from 76 GNSS stations; WRF scheme 2: assimilation of PWV from 213 GNSS stations; WRF scheme 3: assimilation of meteorological profiles from 23 radiosonde stations; WRF scheme 4: assimilation of both PWV from 213 GNSS stations and meteorological profiles from 23 radiosonde stations. PWV observations derived from 170 independent GNSS (have not been used in assimilation) and rainfall data recorded by 648 surface meteorological stations are used to evaluate WRF forecasting performance in PWV and rainfall, respectively. The results indicate that all DA schemes improve the WRF forecasting performance for both PWV and rainfall. For the first 6 h after data assimilation, WRF schemes 1 to 4 improve the PWV forecasting accuracy by 11.6%, 14.5%, 2.9%, and 14.8%, respectively. For the accumulated rainfall within the first 6 h after data assimilation, WRF scheme 2 and WRF scheme 4 have a similar performance and outperform other DA schemes while the WRF scheme 4 is the best one among all five schemes. WRF scheme 4 improves the rainfall forecast probability of detection and equitable threat score by 0.085 and 0.057, respectively.

A Novel Global Grid Model for Atmospheric Weighted Mean Temperature in Real-Time GNSS Precipitable Water Vapor Sounding

The atmospheric weighted mean temperature (Tm) is an important parameter in calculating the precipitable water vapor from Global Navigation Satellite System (GNSS) signals. As both GNSS positioning and GNSS precipitable water vapor detection require high-precision spatial and temporal resolutions for calculating Tm, high-precision modeling of Tm has gained widespread attention in recent years. The previous models for calculating Tm have the limitation of too many model parameters or single grid data. Therefore, this study presents a global high-precision Tm model (GGTm-H model) developed from the latest MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version-2) atmospheric reanalysis data provided by United States National Aeronautics and Space Administration (NASA). The accuracy of GGTm-H model was verified by combining the MERRA-2 surface Tm data and 319 radiosonde data. The results highlighted that: (1) When the MERRA-2 Tm data was used as a reference value, the mean annual RMSE of the GGTm-H model was observed to be 2.72K. When compared with the Bevis model, GPT2w-5 and GPT2w-1 models, GGTm-H model showed an improvement of 1.5K, 0.33K, and 0.21K, respectively. (2) When the radiosonde data was used as a reference value, the mean bias and RMSE of the GGTm-H model were <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-0.41$</tex-math></inline-formula> K and 3.82K, respectively. Compared with the other models, the GGTm-H model had the lowest mean annual bias and RMSE. The developed model does not consider any meteorological parameters while calculating Tm. Therefore, it has important applications in the real-time and high-precision monitoring of precipitable water vapor from GNSS signal.

Harmonic Analysis of the Relationship between GNSS Precipitable Water Vapor and Heavy Rainfall over the Northwest Equatorial Coast, Andes, and Amazon Regions

This study finds the relationship between increases in precipitable water vapor (PWV), and intense rainfall events in four different climatological regions of South America’s equatorial northwest: the coast, Andes valley, high mountains, and Amazon. First, the PWV was derived from tropospheric zenith delay measured by Global Navigation Satellite System (GNSS) instrumentation located near meteorological stations within the regions of interest using hourly data from the year 2014. A harmonic analysis approach through continuous wavelet cross-spectrum and coherence, as well as discrete wavelets, was used to determine a measure of the lags found between PWV and specific heavy rain events and then compared with satellite IR images and meteorological anomalies. The link between PWV peaks and rainfall was the most evident on the coast, and less discernible in the other stations possibly due to local dynamic factors. The results showed a lag of 11 h between the preceding PWV increase and an intense rainfall event. This was apparent in all of the stations, except in Amazon where it was 6 h, with the highest precision at the coast and with the largest dispersion in the high mountains. The interpretation of this lag for each region is also discussed.

Evaluation of surface temperature and pressure derived from MERRA-2 and ERA5 reanalysis datasets and their applications in hourly GNSS precipitable water vapor retrieval over China

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.

Prediction of CORS Water Vapor Values Based on the CEEMDAN and ARIMA-LSTM Combination Model

By relying on the advantages of a uniform site distribution and continuous observation of the Continuously Operating Reference Stations (CORS) system, real-time high-precision Global Navigation Satellite System/Precipitable Water Vapor (GNSS/PWV) data interpretation can be carried out to achieve accurate monitoring of regional water vapor changes. The study of the atmospheric water vapor content and distribution changes is the basis for the realization of rainfall forecasting and water vapor circulation research. Such research can provide data support for the effective forecasting of regional precipitation in megacities and the construction of a more sensitive flood prevention and warning system. Nowadays, a single model is often adopted for GNSS/PWV time series. This makes it challenging to match the high randomness characteristic of water vapor change. This study proposes a hybrid model that takes into account the linear and nonlinear aspects of water vapor data by using complete empirical mode decomposition (CEEMDAN) of adaptive noise, differential autoregressive integrated moving average (ARIMA), and the long-short-term memory network (LSTM). The CEEMDAN is used to decompose the water vapor data series. Then, the high- and low-frequency data are modeled separately, reducing the sequence’s complexity and non-stationarity. In selecting the prediction model, we use the ARIMA model for the high-frequency series and the ARIMA–GWO–LSTM ensemble model for the low-frequency sub-series and residual series. The model is verified using GNSS/PWV time series data collected at the Hong Kong CORS station in July 2021. The results show the following: (1) The LSTM model optimized by the grey wolf optimization algorithm (GWO) is comparable with the single LSTM model in the low-frequency sequence prediction process, and the error items are reduced by 30% after calculation. (2) During the process from CEEMDAN decomposition to the use of the combination model for prediction, the accuracy evaluation indexes of the station increase by more than 20%. The interpolation method can accurately determine the regional water vapor spatial variation, which is of practical significance for local rainfall forecasting. High-frequency data obtained by CEEMDAN decomposition demonstrate the dramatic changes in water vapor before and after the rainfall, which can provide ideas for improving the accuracy of rainfall forecasting.

Fusion of CMONOC and ERA5 PWV Products Based on Backpropagation Neural Network

Data fusion is an effective method to obtain high-precision and high-spatiotemporal-resolution precipitable water vapor (PWV) products, which play an important role in understanding climate change and meteorological monitoring. However, existing fusion methods have some shortcomings, such as ignoring the applicability of the model space or the high complexity of model operation. In this study, the high-precision and high-temporal-resolution Global Navigation Satellite System (GNSS) PWV was used to calibrate and optimize the ERA5 PWV product of the European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) with high spatial resolution to improve its accuracy, and its applicability was verified at the spatiotemporal scale. First, this study obtained accurate GNSS PWV from meteorological data from stations and used it as the true value to analyze the distribution of the ERA5 PWV in mainland China. The results showed that the ERA5 PWV showed significant spatial and temporal differences. Then, a backpropagation neural network (BPNN) fusion correction model with additional constraints was established. The correction results showed that the bias of the ERA5 PWV mainly fluctuated near 0, the correlation between the ERA5 PWV and GNSS PWV was increased to 0.99, and the positive improvement rate of the root-mean-square error (RMSE) was 95%. In the temporal scale validation, the RMSE of the ERA5 PWV decreased from 2.05 mm to 1.67 mm, an improvement of 18.54%. In the spatial scale validation, the RMSE of the four seasons decreased by 0.26–80% (spring), 0.28–70.71% (summer), 0.28–45.23% (autumn), and 0.30–40.75% (winter). Especially in the summer and plateau mountainous areas where the ERA5 PWV performance was poor, the model showed suitable stability. Finally, the fusion model was used to generate a new PWV product, which improved the accuracy of ERA5 PWV on the basis of ensuring the spatial resolution.

An Improved Spatiotemporal Weighted Mean Temperature Model over Europe Based on the Nonlinear Least Squares Estimation Method

Weighted average temperature (Tm) plays a crucial role in global navigation satellite system (GNSS) precipitable water vapor (PWV) retrieval. Aiming at the poor applicability of the existing Tm models in Europe, in the article, we used observations from 48 radiosonde stations over Europe from 2014 to 2020 to establish a weighted average temperature model in Europe (ETm) by the nonlinear least squares estimation method. The ETm model takes into account factors such as ground temperature, water vapor pressure, latitude, and their annual variation, semiannual variation and diurnal variation. Taking the Tm obtained from the radiosonde data by the integration method in 2021 as the reference value, the accuracy of the ETm model was evaluated and compared with the commonly used Bevis model, ETmPoly model, and GPT2w model. The results of the 48 modeled stations showed that the mean bias and root mean square (RMS) values of the ETm model were 0.06 and 2.85 K, respectively, which were 21.7%, 11.5%, and 31.8% higher than the Bevis, ETmPoly, and GPT2w-1 (1° × 1° resolution) models, respectively. In addition, the radiosonde data of 12 non-modeling stations over Europe in 2021 were selected to participate in the model accuracy validation. The mean bias and RMS values of the ETm model were –0.07 and 2.87 K, respectively. Compared with the Bevis, ETmPoly, and GPT2w-1 models, the accuracy (in terms of RMS values) increased by 20.5%, 10.6%, and 35.2%, respectively. Finally, to further verify the superiority of the ETm model, the ETm model, and other Tm models were applied to the GNSS PWV calculation. The ETm model had mean RMSPWV and RMSPWV/PWV values of 0.17 mm and 1.03%, respectively, which were less than other Tm models. Therefore, the ETm model has essential applications in GNSS PWV over Europe.

High-precision GNSS PWV retrieval using dense GNSS sites and in-situ meteorological observations for the evaluation of MERRA-2 and ERA5 reanalysis products over China

Precipitable water vapor (PWV) product with high accuracy and high spatiotemporal resolution is important for climate change research. Hourly PWV products with a high spatiotemporal resolution can be provided by global navigation satellite system (GNSS) and global reanalysis data. The National Aeronautics and Space Administration (NASA) and the European Center for Medium-Range Weather Forecasts (ECMWF) have recently released their global reanalysis of the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and the fifth-generation ECMWF Reanalysis (ERA5), respectively. A comprehensive evaluation of these two PWV reanalysis products over China has yet to be fully investigated. In this study, the PWV products of MERRA-2 and ERA5 reanalysis over China have been systematically evaluated based on 339 GNSS sites and in-situ ground meteorological observations from year 2016 to 2018. To mitigate the uncertainties of this evaluation, three enhanced vertical correction models for temperature, pressure and PWV that we developed previously are adopted. Four weighted mean temperature (Tm) models are established for the four regions of China (North China, South China, Tibet Plateau, and Northwest China), and they exhibit better accuracy than the other classical Tm models in China. The evaluation results of the MERRA-2- and ERA5-derived PWV show that the correlation coefficient, biases, and root mean square error (RMSE) in China are 0.98/0.99, −0.51/0.38 mm, and 2.50/1.99 mm, respectively. They both exhibit relatively large difference when applied to South China. For the same grid spatial resolution, ERA5 PWV product exhibits similar performance with MERRA-2. The monthly-mean-reanalysis-derived PWV exhibits obvious seasonality and has the largest difference in summer. ERA5 PWV can provide more details of the spatial pattern compared to MERRA-2 PWV. The diurnal anomaly variation for ERA5 is more consistent with GNSS data, and some differences at certain moments during autumn and winter have been observed. These results show that ERA5 PWV and MERRA-2 PWV exhibits excellent applicability in China and can help us to efficiently use PWV products for climate research in China.

Weighted Mean Temperature Modelling Using Regional Radiosonde Observations for the Yangtze River Delta Region in China

Precipitable water vapor can be estimated from the Global Navigation Satellite System (GNSS) signal’s zenith wet delay (ZWD) by multiplying a conversion factor, which is a function of weighted mean temperature (Tm) over the GNSS station. Obtaining Tm is an important step in GNSS precipitable water vapor (PWV) conversion. In this study, aiming at the problem that Tm is affected by space and time, observations from seven radiosonde stations in the Yangtze River Delta region of China during 2015−2016 were used to establish both linear and nonlinear multifactor regional Tm model (RTM). Compared with the Bevis model, the results showed that the bias of yearly one-factor RTM, two-factor RTM and three-factor RTM was reduced by 0.55 K, 0.68 K and 0.69 K, respectively. Meanwhile, the RMSE of yearly one-factor, two-factor and three-factor RTM was reduced by 0.56 K, 0.80 K and 0.83 K, respectively. Compared with the yearly three-factor linear RTM, the mean bias and RMSE of the linear seasonal three-factor RTMs decreased by 0.06 K and 0.10 K, respectively. The precision of nonlinear seasonal three-factor RTMs is comparable to linear seasonal three-factor RTMs, but the expressions of the linear RTMs are easier to use. Therefore, linear seasonal three-factor RTMs are more suitable for calculating Tm and are recommended to use for PWV conversion in the Yangtze River Delta region.

Temporal Analysis of GNSS-Based Precipitable Water Vapor during Rainy Days over the Philippines from 2015 to 2017

Precipitable water vapor (PWV) is a parameter used to estimate water vapor content in the atmosphere. In this study, estimates of PWV from PIMO, PLEG and PPPC global navigation satellite system (GNSS) stations are evaluated regarding the PWV obtained from its collocated radiosonde (RS) stations. GNSS PWV were highly correlated with RS PWV (R ~ 0.97). Mean bias error (MBE) between −0.18 mm and −13.39 mm, and root mean square error (RMSE) between 1.86 mm and 2.29 mm showed a good agreement between GNSS PWV and RS PWV. The variations of PWV are presented. Daily variations of PWV conformed to the daily data of rainfall which agrees to the climate types of Quezon City (Type I), Legaspi (Type II), and Puerto Princesa (Type III) based on the Coronas climate classification. Moreover, PWV monthly variation at all sites is high from May to October (~62 mm) and low from November to April (~57 mm). The relationship between PWV and rainfall at all stations showed positive correlation coefficients between +0.49 to +0.83. Meanwhile, it is observed that when PWV is high (low), its variability is low (high). This study shows the potential of GNSS to study water vapor and its contribution to weather analysis.

Assessment and calibration of FY-4A AGRI total precipitable water products based on CMONOC

FengYun 4A (FY-4A) is the first flight unit of the second-generation geostationary meteorological satellite in China (FengYun 4 series, FY-4 series). The Advanced Geostationary Radiation Imager (AGRI) onboard it can provide high spatial resolution precipitable water vapor (PWV) products. This study used Global Navigation Satellite System (GNSS) observations from 230 ground-based GNSS stations within the Crustal Movement Observation Network of China (CMONOC) from March 1, 2019 to February 29, 2020 to obtain PWV, also called total precipitable water (TPW). The Chinese mainland was divided into 13 regions based on climate classification, latitude, and altitude, and GNSS PWV was used to verify the FY-4A AGRI TPW and make calibrations when necessary. The results reveal that the accuracy of FY-4A AGRI TPW shows significant spatial and temporal differences in mainland China. Overall, FY-4A AGRI TPW and GNSS PWV are strongly positively correlated over China, and the correlation coefficients (R) between the two PWV products are highest in autumn (0.78–0.972) while lowest in winter (0.556–0.956). FY-4A AGRI TPW is underestimated in most regions, and the mean bias (MB) values are higher in regions with high PWV content. The root means square error (RMSE) of FY-4A AGRI TPW in all regions are highest in summer (3.34–9.05 mm) and lowest in winter (0.87–3.83 mm). The accuracy of FY-4A AGRI TPW exhibits significant regional and seasonal differences, with higher R, RMSE, and MB in wet seasons and regions, while lower R, RMSE, and MB in dry seasons and regions. This study constructed the calibration model of FY-4A AGRI TPW was based on GNSS PWV in 13 regions. After calibration, the RMSEs can be reduced by 6.6% ~ 32.1%, 5.1% ~ 45%, 5.7% ~ 31.6%, and 7.1% ~ 30.8% in four seasons, respectively, and the MB mainly fluctuates around 0. The accuracy of generated FY-4A AGRI TPW is further improved on the basis of the high resolution.

Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

This paper addresses the subject of inter-annual variability of the tropical precipitable water vapor (PWV) derived from 18 years of global navigation satellite system (GNSS) observations. Non-linear trends of retrieved GNSS PWV were investigated using the singular spectrum analysis (SSA) along with various climate indices. For most of the analyzed stations (~49%) the GNSS PWV anomaly was related to the El Niño Southern Oscillation (ENSO), although its influence on the PWV variability was not homogeneous. The cross-correlations coefficient values estimated between the Multivariate ENSO Index (MEI) and PWV were up to 0.78. A strong cross-correlation was also found for regional climate pattern expressed through CAR, DMI, HAW, NPGO, TNA and TSA indices. A distinct agreement was also found when instead of climate indices, the local sea surface temperature was examined (average correlation 0.60). The SSA method made it also possible to distinguish small-scale phenomena that affect PWV, such as local droughts or wetter rainy seasons. The overall nature of the investigated changes was also verified through linear trend analysis. In general, not a single station was characterized by a negative trend and its weighted mean value, calculated for all stations was equal to 0.08 ± 0.01 mm/year.