Retrieval Of Precipitable Water Vapor Research Articles

A Spatial PWV Retrieval Model Over Land for GCOM-W/AMSR2 Using Neural Network Method: A Case in the Western United States

Precipitable water vapor (PWV) is an important and active part of the atmosphere. As known, microwave PWV retrieval is well applied for the ocean but challenging over land. In this article, we creatively established a microwave PWV retrieval spatial model over land using the back-propagation (BP) neural network to combine the high precision ground-based Global Navigation Satellite System (GNSS) data and high spatial continuity satellite-borne data. Three-year data from 167 GNSS stations located in western America were utilized for training the network, and 40 untrained sites were selected as the test set. Root-mean-square error (RMSE) of the test set can reach 3.90 mm and 3.88 mm in the ascending (As) and descending (De) orbits. Then, we analyzed the influence of land cover types on the model over land. As a result, we found that stations located in the area with a single large-scale continuous land cover type had higher retrieval accuracy. In contrast, stations with diversified land cover types had lower precision. Furthermore, after fully considering the impact of land cover type, we performed an improved model with 61 stations on the single large-scale continuous grasslands, and the results of 8 stations as the test set showed that the RMSE could reach 3.41 and 3.31 mm in the As and De orbits. Compared with the spatial model established previously, the accuracy had been improved by about 13%. We think this is due to the stable physical properties (such as microwave emissivity) of the single large-scale continuous land cover type.

Performance of the Semi-Empirical Precipitable Water Vapor Retrieval Algorithm Developed for Polarized Scanning Atmospheric Corrector (PSAC) in the Presence of Sensor Decay

Performance of the Semi-Empirical Precipitable Water Vapor Retrieval Algorithm Developed for Polarized Scanning Atmospheric Corrector (PSAC) in the Presence of Sensor Decay

Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite

GaoFen-5 (02) (GF5-02) is a new Chinese operational satellite that was launched on 7 September 2021. The Directional Polarimetric Camera (DPC) is one of the main payloads and is mainly used for the remote sensing monitoring of atmospheric components such as aerosols and water vapor. At present, the DPC is in the stage of on-orbit testing, and no public DPC precipitable water vapor (PWV) data are available. In this study, a PWV retrieval algorithm based on the spectral characteristics of DPC data is developed. The algorithm consists of three parts: (1) the construction of the lookup table, (2) the calculation of water vapor absorption transmittance (WVAT) in the band at 910 nm, and (3) DPC PWV retrieval. The global PWV results derived from DPC data are spatially continuous, which can illustrate the global distribution of water vapor content well. The validation based on the Aerosol Robotic Network (AERONET) PWV data shows that the DPC PWV data have accuracy similar to that of Moderate-resolution Imaging Spectroradiometer (MODIS) PWV data, with coefficient correlation of determination (R2), mean absolute error (MAE), and relative error (RE) of 0.32, 0.30, and 0.93 using the DPC and 0.23, 0.36, and 0.96 using the MODIS, respectively. The results show that our proposed DPC PWV retrieval algorithm is feasible and has high accuracy. By analyzing the errors, we found that the calibration coefficients of the DPC in the 865 nm and 910 nm bands need to be updated.

Enhanced all-weather precipitable water vapor retrieval from MODIS near-infrared bands using machine learning

Four novel PWV retrieval approaches based on machine learning methods are for the first time developed to estimate all-weather precipitable water vapor (PWV) from near-infrared (NIR) measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The four retrieval approaches are Back Propagation Neural Network (BPNN), Gradient Boosting Decision Tree (GBDT), Generalized Regression Neural Network (GRNN), and eXtreme Gradient Boosting (XGBoost). The transmittance, latitude, longitude, elevation, cloud, season, and solar zenith angle information, in association with the MODIS NIR PWV’s performance, are utilized. The in-situ one-year PWV data collected in 2017 from 453 Global Positioning System (GPS) sites in Australia and 214 GPS sites in China are utilized as target water vapor estimates for model training. Independent of the 2017 training data, two-year data observed in 2018–2019 in Australia and China are utilized to validate the four models’ performance. The results indicate that the retrieval algorithms can greatly improve the PWV retrieval accuracy from MODIS NIR observations under all-weather conditions, reducing the impact of clouds on NIR PWV retrieval. The new all-weather PWV estimates obtain R2 in the range of 0.83 ∼ 0.86, root-mean-square-error (RMSE) in the range of 4.71 mm ∼ 5.28 mm, and mean bias (MB) in the range of 0.18 mm ∼ 0.51 mm, significantly outperforming the official MODIS NIR PWV product (R2 = 0.31, RMSE = 12.03 mm, and MB = -3.04 mm). The reduction in RMSE is 60.85 % for BPNN, 59.68 % for GBDT, 56.69 % for GRNN, and 57.27 % for XGBoost. The new all-weather PWV results show a superior retrieval accuracy compared to the official MODIS NIR confident-clear PWV product, illustrating the effectiveness of the models. This could be because the retrieval models have considered multiple dependence parameters that affect the performance of MODIS-observed NIR PWV. The retrieval algorithms exhibit little spatial or temporal dependence and they can be applied to other regions and periods. This work provides a more accurate way to retrieve all-weather PWV estimates from satellite NIR measurements considering multiple dependence parameters – location, cloud, season, and solar zenith angle information.

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.

The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function

Compared to the zenith hydrostatic delay (ZHD) obtained from the Saastamonien model based on in-situ measured meteorological (IMM) data and radiosonde-derived weighted mean temperature (Tm), the ZHD and Tm deviations of the GPT3 model have shown obvious periodic trends. This article analyzed the seasonal variations of GPT3-ZHD and GPT3-Tm during the 2016–2020 period in the Yangtze River Delta region, and the new improved ZHD and Tm models were established by the multi-order Fourier function. The precision of the improved-ZHD model was verified using IMM-ZHD products from 7 GNSS stations during the 2016–2020 period. Furthermore, the precisions of improved Tm and precipitable water vapor (PWV) were verified by radiosonde-derived Tm and PWV in the 2016–2019 period. Compared with the IMM-ZHD and GNSS-PWV products, the mean Bias and RMS of GPT3-ZHD are −0.5 mm and 2.1 mm, while those of GPT3-PWV are 2.7 mm and 11.1 mm. Compared to the radiosonde-derived Tm, the mean Bias and RMS of GPT3-Tm are −0.8 K and 3.2 K. The mean Bias and RMS of the improved-ZHD model from 2019 to 2020 are −0.1 mm and 0.5 mm, respectively, decreasing by 0.4 mm and 1.6 mm compared to the GPT3-ZHD, while those of the improved-Tm are −0.6 K and 2.7 K, respectively, decreasing by 0.2 K and 0.5 K compared to GPT3-Tm. The mean Bias and RMS of PWV calculated by GNSS-ZTD, improved-ZHD, and improved-Tm are 0.5 mm and 0.6 mm, respectively, compared to the GNSS-PWV, decreasing by 2.2 mm and 10.5 mm compared to the GPT3-PWV. It indicates that the improved ZHD and Tm models can be used to obtain the high-precision PWV. It can be applied effectively in the retrieval of high-precision PWV in real-time in the Yangtze River Delta region.

Comprehensive Analysis and Validation of the Atmospheric Weighted Mean Temperature Models in China

Atmospheric weighted mean temperature (Tm) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical Tm models using meteorological or non-meteorological parameters have been proposed to calculate PWV, but their accuracy and reliability cannot be guaranteed in some regions. To validate and determine the optimal Tm model for PWV retrieval in China, this paper analyzes and evaluates some typical Tm models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the Tm model for China (CTm), the Global Weighted Mean Temperature-H (GTm-H) and the Global Tropospheric (GTrop) models. The Tm values of these models are first obtained at corresponding radiosonde (RS) stations in China over the period of 2011 to 2020. The corresponding Tm values of 87 RS stations in China are also calculated using the layered meteorological data and regarded as the reference. Comparison results show that the accuracy of these five Tm models in China has an obvious geographical distribution and decreases along with increasing altitude and latitude, respectively. The average root mean square (RMS) and Bias for the Linear, GPT3, CTm, GTm-H and GTrop models are 4.2/3.7/3.4/3.6/3.3 K and 0.7/−1.0/0.7/−0.1/0.3 K, respectively. Among these models, Linear and GPT3 models have lower accuracy in high-altitude regions, whereas CTm, GTm-H and GTrop models show better accuracy and stability throughout the whole China. These models generally have higher accuracy in regions with low latitude and lower accuracy in regions with middle and high latitudes. In addition, Linear and GPT3 models have poor accuracy in general, whereas GTm-H and CTm models are obviously less accurate and stable than GTrop model in regions with high latitude. These models show different accuracies across the four geographical regions of China, with GTrop model demonstrating the relatively better accuracy and stability. Therefore, the GTrop model is recommended to obtain Tm for calculating PWV in China.

Comprehensive Precipitable Water Vapor Retrieval and Application Platform Based on Various Water Vapor Detection Techniques

Atmospheric water vapor is one of the important parameters for weather and climate studies. Generally, atmospheric water vapor can be monitored by some techniques, such as the Global Navigation Satellite System (GNSS), radiosonde (RS), remote sensing and numerical weather forecast (NWF). However, the comprehensive retrieval and application of precipitable water vapor (PWV) using multi techniques has been hardly performed before, which becomes the focus of this study. A comprehensive PWV retrieval and application platform (CPRAP) is first established by combing the ground-based (GNSS), space-based (Fengyun-3A, Sentinel-3A) and reanalysis-based (the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting, ERA5) techniques. Additionally, its applications are then extended to drought and rainfall monitoring using the CPRAP-derived PWV. The statistical result shows that PWV derived from ground-based GNSS has high accuracy in China, with the root mean square (RMS), Bias and mean absolute error (MAE) of 2.15, 0.05 and 1.65 mm, respectively, when the RS-derived PWV is regarded as the reference. In addition, the accuracy of PWV derived from the space-based (FY-3A and Sentinel-3A) techniques technique is also validated and the RMS, Bias and MAE of a Medium Resolution Spectral Imager (MERSI) onboard Fengyun-3A (FY-3A) and an Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A are 4.46/0.56/3.61 mm and 2.95/0.01/1.37 mm, respectively. Then, the performance of ERA5-derived PWV is evaluated based on GNSS-derived and RS-derived PWV. The result also shows good accuracy of ERA5-provided PWV with the averaged RMS, Bias and MAE of 1.86/0.11/1.48 mm and 0.90/−0.05/1.51 mm, respectively. Finally, the PWV data derived from the established CPRAP are further used for drought and rainfall monitoring. The applied results reveal that the calculated the standardized precipitation evapotranspiration index (SPEI) using the CPRAP-derived PWV can monitor the drought and the correlation coefficient ranges from 0.83 to 0.9 when compared with the SPEI. Furthermore, in this paper correlation analysis between PWV derived from the CPRAP and rainfall, and its potential for rainfall monitoring was also validated. Such results verify the significance of the established CPRAP for weather and climate studies.

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.

Development and Assessment of an ALLSSA‐Based Atmospheric Weighted Mean Temperature Model With High Time Resolution for GNSS Precipitable Water Retrieval

AbstractThe atmosphere weighted mean temperature, Tm, is an essential parameter for retrieving precipitable water from the ground‐based Global Navigation Satellite Systems (GNSS). The accuracy of high temporal resolution GNSS precipitable water vapor (PWV) estimation requires wideband Tm information and its magnitude. However, existing Tm empirical models use trigonometric functions with only fixed amplitude and low‐frequency for time fitting, which limits real‐time or near real‐time PWV retrieval from GNSS observation. Thus, an improved Tm model for China, LTCm, containing more frequency information of Tm, based on the antileakage least‐squares spectrum analysis by utilizing the ERA5 pressure‐level products during the years 2015–2019, is developed. Both Tm data from ERA5 pressure‐level products and radiosonde stations distributed in China over 2020 are selected as reference values to verify the performance of the LCTm model. The results show that the LCTm model yields significant performance against other models in Tm estimation over China, especially in marine regions and high‐altitude areas. Furthermore, the LCTm model can generally achieve a mean Bias/root mean square (RMS) of −0.33 K/2.06 K in contrast to ERA5 pressure‐level products and 0.03 K/3.47 K in comparison with radiosonde, which corresponds to a 7.2%–13.8% improvement against GPT2w, GTm‐III, and Bevis. Moreover, LCTm has σ pwv and σ pwv /PWV values of 0.27 mm and 1.25% when used to retrieve GNSS‐PWV, respectively. Consequently, the LCTm model that considers high‐frequency information of Tm can obtain more reliable Tm values. Therefore, the LCTm model can be applied to real‐time or near real‐time GNSS PWV retrieval, which is of great significance for GNSS meteorological research.

Machine Learning‐Based Model for Real‐Time GNSS Precipitable Water Vapor Sensing

AbstractGlobal Navigation Satellite Systems (GNSS) provide a promising opportunity for real‐time precipitable water vapor (PWV) sensing. However, relying on meteorological information restrains the implementation of real‐time inversion from tropospheric zenith total delays (ZTD) into PWV. In this study, a stacked machine learning model for mapping ZTD into PWV in the absence of meteorological parameters is developed. The model performance is assessed and validated with information offered by fifth generation European Centre for Medium‐Range Weather Forecasts reanalysis (ERA5) and radiosondes. An accuracy of better than 2.5 mm is achievable for the PWV values. Compared to the physical model which applies GPT3‐derived meteorological parameters, the proposed model reveals an enhanced performance, especially in the high‐latitude regions, with improvements of 28.1% and 22.2% when validated with ERA5 and radiosondes. This model is capable of fulfilling the demands of time‐critical meteorological applications and is also promising for real‐time PWV retrieval of other techniques that own the capability to sense ZTD.

Land Water Vapor Retrieval for AMSR2 Using a Deep Learning Method

In precipitable water vapor (PWV) retrieval, results with high spatial coverage but low temporal resolution can be achieved through satellite-borne sensors, such as the Advanced Microwave Scanning Radiometer 2 (AMSR2). Conversely, the ground-based global navigation satellite system (GNSS) can provide PWV with high temporal resolution and high precision but low spatial coverage. To combine the advantages of these two technologies, we introduce a backpropagation neural network (BPNN) to realize PWV retrieval from AMSR2 with ground-based GNSS data. We first detect the optimal configuration for the BPNN. Then, based on the results of the retrieval accuracy from different types of orbits, we find that the descending (De) orbit has the highest retrieval accuracy, with a root-mean-square error (RMSE) of 3.25 mm. Afterward, the influence of brightness temperature (Tb) data at different frequencies on PWV retrieval is analyzed. The results of GNSS- PWV verification indicate that the 18 +23-GHz frequency combination has the highest PWV retrieval accuracy, and the mean RMSE of all 82 test stations distributed globally can reach 3.53 mm. We also analyze the influence of differently located stations on retrieval accuracy, and the results show that the accuracy of high-latitude and polar regions is remarkably higher than that of other areas but with a lower relative error. Finally, we use radiosonde data as another external verification method to assess PWV retrieval accuracy. The results reveal that RMSE can reach 3.87 mm. Through a BPNN approach, we have creatively realized PWV retrieval from AMSR2 using ground-based GNSS data on a global scale.

Precipitable Water Vapor Retrieval Over Land From GCOM-W/AMSR2 Based on a New Integrated Method

Precipitable water vapor (PWV) is an important parameter reflecting atmospheric water vapor, which plays an important role in the global hydrological cycle. At present, brightness temperatures (Tbs) at frequencies of 18 and 23 GHz from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are commonly applied in PWV data retrieval. Two tools are usually employed to perform the retrieval: physical models and neural networks. In this article, we creatively deduced a more universal physical equation and verified the consistency between theoretical and experimental equations both theoretically and numerically. Then, we fully considered the defects of these two tools and proposed a new integrated method that combines the physical model with a neural network. The Tbs in horizontal and vertical polarizations from AMSR2 and GNSS-derived PWV (GNSS-PWV) data from the SuomiNet global network were used to build the physical model. In the construction of the neural network, we also introduced the related surface parameters from GNSS stations as inputs. Validation results obtained with GNSS-PWV data showed that the accuracy of the test set can reach 2.38 and 2.37 mm in the ascending and descending orbit cases, respectively. Compared with the traditional physical model and neural network model, the improvements of the test set were 24.4% and 17.4% in the ascending orbit and 26.4% and 19.4% in the descending orbit. Radiosonde observation (RAOB) data were applied to carry out another external independent verification, and the accuracy of the test set reached 2.70 and 3.54 mm based on the RAOB data.

A Back Propagation Neural Network-Based Algorithm for Retrieving All-Weather Precipitable Water Vapor From MODIS NIR Measurements

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor can observe precipitable water vapor (PWV) at near-infrared (NIR) bands. In this paper, we proposed a novel Back Propagation Neural Network (BPNN) based water vapor retrieval algorithm to enhance the all-weather retrieval accuracy of the PWV estimation from MODIS NIR observations. The input of the model includes the transmittance, latitude, longitude, elevation, season, cloud, and solar zenith angle information. The water vapor data collected from in-situ 453 Global Positioning System (GPS) sites in Australia in 2017 were employed as the output PWV for the training of the BPNN method. The performance of the retrieval approach was evaluated utilizing reference GPS-measured PWV data during 2018–2019 over Australia, independent of the 2017 training data. The results indicate that the algorithm can notably enhance the accuracy of MODIS NIR PWV retrieval under all weather conditions as well as under each type of weather condition. The BPNN-retrieved weighted mean PWV data calculated with 2-channel ratio approach had the best retrieval accuracy, reducing root-mean-square error (RMSE) by 57.26% from 10.95 mm to 4.68 mm for all-weather conditions and 47.49% from 5.58 mm to 2.93 mm for confident-clear conditions. The new all-weather PWV estimates present a retrieval accuracy superior to official MODIS NIR confident-clear PWV product, illustrating the effectiveness of the retrieval algorithm. This algorithm has been well evaluated in Australia and its performance in other global regions will be investigated in the future, while the MODIS official products are applicable to the global regions.

An Improved MODIS NIR PWV Retrieval Algorithm Based on an Artificial Neural Network Considering the Land-Cover Types

Estimating precipitable water vapor (PWV) with high accuracy and spatial resolution is important in many disciplines. Water vapor absorption and non-absorption channels can be observed in the near-infrared (NIR) ray of the Moderate Resolution Imaging Spectroradiometer (MODIS), which can be used to retrieve PWV. However, traditional algorithms overestimate the NIR PWV in North America. This study proposes a novel NIR retrieval algorithm based on machine learning that considers land-cover types to estimate high-accuracy PWV. To do this, nonlinear models between MODIS NIR transmittance, based on the 2- and 3-Channel Ratio, and global navigation satellite system (GNSS) PWV, recorded by the SuomiNet GNSS network, are established using a backpropagation neural network. Verification shows that the root mean square error (RMSE)/ standard deviation (STD)/Bias of 2-Channel Ratio PWV is 1.29/1.29/0.02 mm, and the improvements of RMSE and STD are 66.32% and 37.98%, respectively. The RMSE/STD/Bias values of the 3-Channel Ratio PWV are 1.29/1.29/0.02 mm, and the improvements in RMSE and STD are 68.67% and 42.31%, respectively. In addition, the surface verification of the proposed method in six land-cover types shows that both the 2- and 3-Channel Ratio methods can yield satisfactory PWV estimates. Compared to the MODIS PWV products, the proposed method yields remarkable progress.

Evaluation of Precipitable Water Vapor Retrieval from Homogeneously Reprocessed Long-Term GNSS Tropospheric Zenith Wet Delay, and Multi-Technique

Water vapor is one of the most important greenhouse gases in the world. There are many techniques that can measure water vapor directly or remotely. In this work, we first study the Global Positioning System (GPS)- and the Global Navigation Satellite System (GLONASS)-derived Zenith Wet Delay (ZWD) time series based on 11 years of the second reprocessing campaign of International Global Navigation Satellite Systems (GNSS) Service (IGS) using 320 globally distributed stations. The amount of measurement, the local environment, and the antenna radome are shown to be the main factors that affect the GNSS ZWDs and the corresponding a posteriori formal errors. Furthermore, antenna radome is able to effectively reduce the systematic bias of ZWDs and a posteriori formal errors between the GPS- and GLONASS-based solutions. With the development of the GLONASS, the ZWD differences between the GPS- and the GLONASS-based solutions have gradually decreased to sub-mm-level after GLONASS was fully operated. As the GPS-based Precipitable Water Vapor (PWV) is usually used as the reference to evaluate the other PWV products, the PWV consistency among several common techniques is evaluated, including GNSSs, spaceborne sensors, and numerical products from the European Center for Medium-Range Weather Forecasts (ECMWF). As an example of the results from a detailed comparison analysis, the long-term global analysis shows that the PWV obtained from the GNSS and the ECMWF have great intra-agreements. Based on the global distribution of the magnitude of the PWV and the PWV drift, most of the techniques showed superior agreement and proved their ability to do climate research. With a detailed study performed for the ZWDs and PWV on a long-term global scale, this contribution provides a useful supplement for future research on the GNSS ZWD and PWV.

GNSS Precipitable Water Vapor Retrieval With the Aid of NWM Data for China

AbstractGlobal Navigation Satellite System (GNSS) has become a powerful tool for sensing precipitable water vapor (PWV), which is important in investigating long‐term climate or nowcasting. Zenith hydrostatic delay (ZHD) and weighted mean temperature (Tm) are the two key parameters for retrieving PWV GNSS observations. ZHD is generally derived from the Saastamoinen model by using surface pressure or numerical weather model (NWM). However, the lack of meteorological observations located at or near the GNSS sites is a big challenge for the calculation of accurate ZHD, leading to inconvenience in GNSS‐derived PWV. In this study, a method for GNSS retrieval in China by using ZHD from the NWM and Tm derived from IGPT2w is proposed. First, a new grid model, namely CGZHD model, is developed for the vertical adjustment of ZHD in China. Then, two types of ZHD products derived from NWM data, that is, postprocessing and forecast ones, namely ZHD and ZHDFC, respectively. Finally, the performance of GNSS PWV estimation for 18 GNSS sites is evaluated with PWVs from collocated radiosonde sites for China. Results show that the CGZHD model has excellent performance against the GPT2w model and previous studies. ZHD and ZHDFC from NWM present high precision after vertical adjustment. In terms of GNSS PWV retrieval, two modes of GNSS PWV estimation results, that is, ZHD and ZHDFC obtained from postprocessing and forecast ones, respectively, show excellent performance in China. The proposed method does not require any in situ meteorological information as input and thus has potential applications for high‐precision and real‐time GNSS PWV retrieval in China.

Retrieving Precipitable Water Vapor from Real-Time Precise Point Positioning Using VMF1/VMF3 Forecasting Products

Real-time precise point positioning (RT-PPP) has become a powerful technique for the determination of the zenith tropospheric delay (ZTD) over a GPS (global positioning system) or GNSS (global navigation satellite systems) station of interest, and the follow-on high-precision retrieval of precipitable water vapor (PWV). The a priori zenith hydrostatic delay (ZHD) and the mapping function used in the PPP approach are the two factors that could affect the accuracy of the PPP-based ZTD significantly. If the in situ atmospheric pressure is available, the Saastamoinen model can be used to determine ZHD values, and the model-predicted ZHD results are of high accuracy. However, not all GPS/GNSS are equipped with an in situ meteorological sensor. In this research, the daily forecasting ZHD and mapping function values from VMF1 forecasting (VMF1_FC) and VMF3 forecasting (VMF3_FC) products were used for the determination of the GPS-derived PWV. The a priori ZHDs derived from VMF1_FC and VMF3_FC were first evaluated by comparing against the reference ZHDs from globally distributed radiosonde stations. GPS observations from 41 IGS stations that have co-located radiosonde stations during the period of the first half of 2020 were used to test the quality of GPS-ZTD and GPS-PWV. Three sets of ZTDs estimated from RT-PPP solutions using the a priori ZHD and mapping function from the following three VMF products were evaluated: (1) VMF1_FC; (2) VMF3_FC (resolution 5° × 5°); (3) VMF3_FC (resolution 1° × 1°). The results showed that, when the ZHDs from 443 globally distributed radiosonde stations from 1 July 2018 to 30 June 2021 were used as the reference, the mean RMSEs of the ZHDs from the three VMF products were 5.9, 5.4, and 4.3 mm, respectively. The ZTDs estimated from RT-PPP at 41 selected IGS stations were compared with those from IGS, and the results showed that the mean RMSEs of the ZTDs of the 41 stations from the three PPP solutions were 8.6, 9.0, and 8.6 mm, respectively, and the mean RMSEs of the PWV converted from their corresponding ZWDs were 1.9, 2.4, and 1.7 mm, respectively, in comparison with the reference PWV from co-located radiosonde stations. The results suggest that the a priori ZHD and mapping function from VMF1_FC and VMF3_FC can be used for the precise determination of real-time GPS/GNSS-PWV in most regions, especially the VMF3_FC (resolution 1° × 1°) product.

A Global Assessment of Precipitable Water Vapor Derived From GNSS Zenith Tropospheric Delays With ERA5, NCEP FNL, and NCEP GFS Products

AbstractIn precipitable water vapor (PWV) retrievals from Global Navigation Satellite System (GNSS) data, the two essential parameters, namely, surface pressure () and weighted mean temperature (), are often not available due to the lack of collocated meteorological sensors or improper data retention. Hence, this study presents a comprehensive assessment of the GNSS PWV retrieval using alternative and data from the European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis 5, National Centers for Environmental Prediction Final (NCEP FNL) Analysis, and NCEP Global Forecast System (GFS) products. The assessment was based on 691 globally distributed GNSS stations over the entire year of 2019. The zenith hydrostatic delay (ZHD) and integrated from the three types of numerical weather prediction (NWP) atmospheric profiles achieve varying accuracies in ranges of 2.4–3.0 mm and 1.1–1.5 K, respectively. PWVs estimated using ZHD and integrated from the NWP profiles obtain accuracies of about 1.6–2.0 mm. These PWVs are slightly better than those using ZHD and calculated by empirical models with surface pressure and temperature from the NWP datasets. The assessment of PWVs with the global pressure and temperature 2 wet model yields a root mean square (RMS) error of 3.73 mm. The relative RMS decreases from 30%‐40% at high latitudes (70–80°S/N) to ∼5% around the equator. The monthly variations of relative RMS show that (a) low‐latitude regions outperform the high‐latitude regions, and (b) winter months have significantly worse performance than other months in both hemispheres.

A Comprehensive Evaluation of Key Tropospheric Parameters from ERA5 and MERRA-2 Reanalysis Products Using Radiosonde Data and GNSS Measurements

Tropospheric delay is a major error source in the Global Navigation Satellite System (GNSS), and the weighted mean temperature (Tm) is a key parameter in precipitable water vapor (PWV) retrieval. Although reanalysis products like the National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-Interim) data have been used to calculate and model the tropospheric delay, Tm, and PWV, the limitations of the temporal and spatial resolutions of the reanalysis data have affected their performance. The release of the fifth-generation accurate global atmospheric reanalysis (ERA5) and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) provide the opportunity to overcome these limitations. The performances of the zenith tropospheric delay (ZTD), zenith wet delay (ZWD), Tm, and zenith hydrostatic delay (ZHD) of ERA5 and MERRA-2 data from 2016 to 2017 were evaluated in this work using GNSS ZTD and radiosonde data over the globe. Taking GNSS ZTD as a reference, the ZTD calculated from MERRA-2 and ERA5 pressure-level data were evaluated in temporal and spatial scales, with an annual mean bias and root mean square (RMS) of 2.3 and 10.9 mm for ERA5 and 4.5 and 13.1 mm for MERRA-2, respectively. Compared to radiosonde data, the ZHD, ZWD, and Tm derived from ERA5 and MERRA-2 data were also evaluated on temporal and spatial scales, with annual mean bias values of 1.1, 1.7 mm, and 0.14 K for ERA5 and 0.5, 4.8 mm, and –0.08 K for MERRA-2, respectively. Meanwhile, the annual mean RMS was 4.5, 10.5 mm, and 1.03 K for ERA5 and 4.4, 13.6 mm, and 1.17 K for MERRA-2, respectively. Tropospheric parameters derived from MERRA-2 and ERA5, with improved temporal and spatial resolutions, can provide a reference for GNSS positioning and PWV retrieval.