Precipitable Water Vapor Estimates Research Articles

A direct conversion model from ZTD to PWV based on the random forest

Abstract Precipitable Water Vapor (PWV) is crucial for weather forecasting and climate change research. However, accurate PWV estimation is challenging, especially in the absence of measured meteorological data. In this study, we develop a conversion model based on the random forest algorithm, called RPWV, which can directly derive PWV from zenith total delay (ZTD) without measured meteorological data. The results indicate that the RPWV model demonstrates high accuracy and reliability for PWV retrieval across North America. Specifically, using global navigation satellite system (GNSS) PWV as a reference, the bias and root mean square (RMS) values for RPWV are 0.01 mm and 1.87 mm, respectively. Moreover, compared with the conventional linear model and backpropagation neural network model, the accuracy of the RPWV is improved by 81.3% and 13.4%, respectively. In contrast to radiosonde (RS) PWV, the bias and RMS values of the RPWV are 1.85 mm and 3.40 mm, respectively. This model provides a straightforward and efficient method for estimating PWV and has potential for weather forecasting and climate research in environments where meteorological data is scarce.

Performance of Ground-Based Global Navigation Satellite System Precipitable Water Vapor Retrieval in Beijing with the BeiDou B2b Service

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.

Improving MODIS-IR precipitable water vapor based on the FIDWFT model

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.

Estimation of precipitable water vapor using least squares support vector regression and comparison with other models

Estimation of precipitable water vapor using least squares support vector regression and comparison with other models

Determining precipitable water vapour from upper‐air temperature, pressure and geopotential height

AbstractRadiosonde measurements of relative humidity (RH) are the main source of uncertainty in precipitable water vapour (PWV) calculation from pressure, temperature, and RH/dewpoint (PTU) data. This paper presents a formula expressing PWV in terms of pressure and temperature as functions of geopotential height (GPH), thereby allowing the PWV to be determined: (1) without any moisture‐related calculations other than those involved in measuring GPH (in radiosondes with a pressor sensor) or pressure (otherwise); (2) without relying on humidity measurements by using Global Positioning System (GPS)‐based GPH according to the gravity field, provided that pressure is directly measured. The numerical instability associated with random data errors or deviations from hydrostatic equilibrium makes the second approach unfeasible on short time scales, revealing discrepancies between the PTU‐ and GPS‐based GPHs; however, the estimation of long‐term average PWV above a location is not hindered. The estimation of PWV without humidity data was tested using high‐resolution data from 62 upper‐air stations operated by the NOAA National Weather Service. The seasonal mean {DJF, MAM, JJA, SON} PWV from the surface to 300 hPa calculated from PT and GPS data over the period 2016–2018, after rejecting individual estimates inconsistent with the 0%–100% RH range, showed a mean bias error of {−0.1, +0.1, −1.4, −0.9} kg·m−2 relative to the PTU‐based values across the stations, and a RMSE ranging from 2.4 (DJF) to 3.2 (JJA) kg·m−2. By restricting the analysis to observations with above‐average matching between the PTU‐ and GPS‐based GPH, the bias magnitude and RMSE reduced respectively to less than 0.5 and 1 kg·m−2 in all seasons. The results indicate that evaluating the long‐term agreement between the two PWV calculation methods at different sites could be useful in detecting systematic observation errors in GPS radiosonde systems using a pressure sensor.

A Weighted Mean Temperature Forecast Model Based on Fused Data and Generalized Regression Neural Network and its Impact on GNSS-Based Precipitable Water Vapor Estimation

A Weighted Mean Temperature Forecast Model Based on Fused Data and Generalized Regression Neural Network and its Impact on GNSS-Based Precipitable Water Vapor Estimation

All-weather precipitable water vapor map reconstruction using data fusion and machine learning-based spatial downscaling

Precipitable water vapor (PWV) detection with high spatial resolution and high accuracy is of significant importance for contributing to extreme weather events monitoring and forecasting. Current PWV products, however, suffer from limitations of spatial and temporal discontinuities, low accuracy, and coarse spatial resolution. To overcome this problem, a data fusion and machine learning-based spatial downscaling solution is proposed. At first, spatially complete PWV maps are generated by integrating calibrated PWV of Moderate Resolution Imaging Spectroradiometer (MODIS) and the ERA5 PWV data from 2018 to 2022. Subsequently, three spatial downscaling models based on Gradient Boosting Decision Tree (GBDT), Multi-layer Perceptron Neural Network (MLPNN), and Random Forest (RF), respectively, are developed to produce high-quality, all-weather PWV considering the land-cover type. It has been verified that the high-quality all-weather PWV maps generated by the GBDT, MLPNN, and RF models exhibit strong agreement with Global Navigation Satellite System (GNSS) PWV estimates. The correlation coefficients are 0.95, 0.87, and 0.87, while the overall Bias is 0.29 mm, 0.67 mm, and 0.35 mm, and the root mean square errors (RMSE) are 1.74 mm, 2.98 mm, and 3.06 mm, respectively. These results significantly enhance the accuracy of MODIS PWV products (R2 = 0.73, RMSE = 5.64 mm, Bias = 3.05 mm). Notably, the GBDT model outperforms the other models in terms of performance. Compared to MODIS PWV, the new PWV map with a data fusion and machine learning-based spatial downscaling approach effectively utilizes the advantage of satellite-based and reanalyzed PWV products, providing continuous, detailed, and reasonable variation in time and space. Moreover, it is less influenced by seasonal changes. The new PWV map has a promising application for regional hydrology and meteorology.

A proposed neural network model for obtaining precipitable water vapor

Abstract 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.

Short-Term Rainfall Forecasting by Combining BP-NN Algorithm and GNSS Technique for Landslide-Prone Areas

Extreme rainfall is the main contributing factor to landslides. Therefore, it is of great significance to monitor and forecast short-term rainfall in landslide-prone areas. However, the spatial scale of landslide-prone areas is small, and traditional numerical forecast models have difficulty in accurately forecasting rainfall on this scale. To solve the above problem, this study proposes a short-term rainfall forecasting method for landslide-prone areas by combining the back-propagation neural network (BP-NN) algorithm and global navigation satellite system (GNSS) observations to achieve accurate short-term rainfall forecasting in landslide-prone areas. Firstly, a high-precision atmospheric weighted-average temperature (Tm) model is established using radiosonde data to obtain high-precision precipitable water vapor (PWV) estimates. Secondly, the BP-NN algorithm is introduced, and the GNSS-derived PWV, temperature and pressure from a meteorological station, and rainfall for the previous and next hour are used as input parameters to establish a BP-NN-based rainfall forecast model. As an illustrative case, experiments are conducted in a landslide-prone area in Yunnan Province using data from 15 GNSS stations and the corresponding meteorological station. Statistical results show that the established regional Tm model has high accuracy, with an average root mean square (RMS) and bias of 3 K and 0.15 K, respectively. In addition, the short-term rainfall forecast model based on the BP algorithm achieves a true detection rate of up to 93.70% and a false forecast rate of as low as 38.30%, which is significant for short-term rainfall forecasting in landslide-prone areas.

Estimation of precipitable water vapor from ground based GPS measurements and Radiosonde in Southeast Asian-Pacific

Estimation of precipitable water vapor from ground based GPS measurements and Radiosonde in Southeast Asian-Pacific

Validation of precipitable water vapor estimates from an inexpensive infrared thermometer

Water vapor is a fundamental component of the Earth's atmosphere with a high spatial and temporal variability. This work studies to what extent low-cost infrared thermometers can infer precipitable water (variable commonly used to characterize atmospheric water vapor). In a calibration process, infrared thermometer readings recorded at Badajoz (Spain) during the 2015–2018 period are compared against precipitable water data measured with reference ground-based Global Navigation Satellite Systems (GNSS) in order to obtain conversion factors through regression analyses considering two exponential fits. After this calibration, using the equation of the best fit, thermometer readings for the year 2019 are transformed into precipitable water estimates. A validation analysis in which these estimates are compared with GNSS measurements yields rms differences of 19% and 17% when normal and seasonal calibration had been employed, respectively. These results are similar (or even better) to those obtained with satellite data. In addition, we explore if certain factors, such as solar elevation, precipitable water content, precipitable water measurements used as reference and equations to convert temperature readings into precipitable water estimates, can significantly affect the quality of the estimates. In view of our results, low-cost infrared thermometers could be used to create an extensive and dense network for a better characterization of the spatial and temporal variability of water vapor.

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.

Smart Data Blending Framework to Enhance Precipitation Estimation through Interconnected Atmospheric, Satellite, and Surface Variables

Accurate precipitation estimation remains a challenge, though it is fundamental for most hydrological analyses. In this regard, this study aims to achieve two objectives. Firstly, we evaluate the performance of two precipitation products from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) for Sicily, Italy, from 2016 to 2020 by a set of categorical indicators and statistical indices. Analyses indicate the favorable performance of daily estimates, while half-hourly estimates exhibited poorer performance, revealing larger discrepancies between satellite and ground-based measurements at sub-hourly timescales. Secondly, we propose four multi-source merged models within Artificial Neural Network (ANN) and Multivariant Linear Regression (MLR) blending frameworks to seek potential improvement by exploiting different combinations of Soil Moisture (SM) measurements from the Soil Moisture Active Passive (SMAP) mission and atmospheric factor of Precipitable Water Vapor (PWV) estimations, from the Advanced Microwave Scanning Radiometer-2 (AMSR2). Spatial distribution maps of some diagnostic indices used to quantitatively evaluate the quality of models reveal the best performance of ANNs over the entire domain. Assessing variable sensitivity reveals the importance of IMERG satellite precipitation and PWV in non-linear models such as ANNs, which outperform the MLR modeling framework and individual IMERG products.

Retrieving Accurate Precipitable Water Vapor Based on GNSS Multi‐Antenna PPP With an Ocean‐Based Dynamic Experiment

AbstractAs an attractive technique for measuring water vapor, the Global Navigation Satellite System (GNSS) faces additional challenges in dynamic applications such as in the open sea. We present a new method of retrieving precipitable water vapor (PWV) based on GNSS multi‐antenna precise point positioning (PPP), which uses GNSS data from multiple antennas and incorporates the constraints of known baseline vector and common tropospheric delay. The 4‐day shipborne dynamic experiment along the China coast demonstrates that the baseline vector constraint shortens the convergence time of positioning and atmospheric parameters, and also slightly improves their accuracies. The common tropospheric delay constraint helps to provide compromised, more robust, and sometimes more accurate PWV estimates. An evaluation with radiosonde‐derived PWVs shows that the combination of the two constraints achieves the best accuracy reaching 4.2 mm. This method helps to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting.

Impact of sand and dust storms on tropospheric parameter estimation by GPS

The transport of dust from the Middle East and African deserts affects European and Asian countries at certain times of the year, especially in spring. Turkey is one of these countries, and many dust storm events have occurred in the first half of 2022, which have affected especially the southeastern part of Anatolia. Apart from threatening human health, dust and sand particles, which are described as particulate matter, may possibly affect Global Positioning System (GPS) signals. The purpose of this research is to look into the effects of particulate matter less than 10μm (PM10) on GPS-estimated precipitable water vapor (PWV). Hourly PM10 and PWV data between April 1, 2022, and June 10, 2022, were utilized. Four different extreme dust concentration events and a benchmark period were investigated separately. Hourly data results showed that correlation coefficients vary according to events, wind directions, and the distance between GPS stations and air quality monitoring stations. Also, other meteorological parameters that affect PWV, such as temperature, relative humidity, and pressure, were investigated and found to have no anomalies that could affect PWV. Hourly and daily correlation coefficients in the benchmark period were significantly lower compared to dusty days, which indicates that there is no real correlation between PM10 and PWV concentrations in clear air conditions. Only with the increase of PM10 to extreme levels does the relationship show itself. Therefore, this study suggests that for all GPS applications, such as positioning or PWV estimation, PM10 concentrations should be considered.

Regional modeling and forecasting of precipitable water vapor using least square support vector regression

We propose a new model for spatio-temporal modeling and forecasting of precipitable water vapor (PWV) using least square support vector regression (LS-SVR) method. The LS-SVR uses simple linear equations. As a result, the complexity of the computational algorithm is reduced. In addition, the convergence speed and accuracy of the results increase. The evaluation of the new method has been done with the observations of the GPS networks at north-west and central Alborz in Iran. In the north-west GPS network, observations of 23 GPS stations in the period of 27 October to 10 November 2011 are used. However, in central Alborz network, the observations of 11 GPS stations in the period of 10 to 24 June 2016 have been used. The north-west GPS network is in the mountainous region and its observations are for the winter season. But, the second network is near the coastal area and summer season measurements are used. The latitude, longitude and height of GPS stations, DOY, time, relative humidity, temperature and pressure are considered as an input of the LS-SVR model. The output of the new model is the PWV (LS-SVRPWV). After the training step, the new model is used to estimate the spatio-temporal variation of PWV. The results of the LS-SVR model are compared and evaluated with the standard neural network (SNN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), Saastamoinen, global pressure and temperature 3 (GPT3), voxel-based tomography (VBT) models, as well as with radiosonde measurements. Error evaluation of models has been done in control stations as well as by precise point positioning (PPP) method. For LS-SVR model, the averaged RMSE in the control stations of the north-west GPS network is 1.69 mm, while for the central Alborz GPS network, 1.88 mm is calculated. Also, the averaged relative error of LS-SVR model calculated in the Tabriz and Tehran radiosonde stations are 4.66 % and 6.12 %, respectively. The results of this paper show that the LS-SVR model has a very high capability in forecasting the spatio-temporal variation of PWV at the GPS network territory. The new model can be used for accurate estimation of PWV, meteorological applications and flood forecasting.

GNSS-DERIVED PRECIPITABLE WATER VAPOR MODELING USING MACHINE LEARNING METHODS

Abstract. Atmospheric water vapor plays a vital role in phenomena related to the global hydrological cycle and climate changes, and its Spatio-temporal modeling and prediction help to identify and predict climatic phenomena. Accordingly, in this study, hourly precipitable water vapor (PWV) data sets for 27 stations receiving Global Navigation Satellite Systems (GNSS) observations in one month and machine learning methods were used to estimate PWV. Machine learning methods used in this study 1. Random Forest Regression (RFR) method 2. Extreme Gradient Boosting Regression (XGBR). The root mean square error (RMSE) in PWV estimation with the RFR method (RFR PWV) is 2.42 mm, and in PWV estimation with the XGBR method (XGBR PWV) is 2.75 mm, and the R-squared (R2) of the RFR method is 0.74, and for the XGBR method, these values are equal to 0.71. The obtained results show the efficiency and accuracy of both models in estimating PWV, which shows that machine learning methods have been able to recognize the behavior and changes of precipitable vapor in a small spatial and temporal interval. Although both ways had high accuracies, the RFR model performed slightly better and had better accuracy than the XGBR model.

Development of an empirical model for weighted mean temperature over North Indian region

Weighted mean temperature ( T m ) is an important parameter in the process of GPS precipitable water vapour (PWV) estimation. GPS PWV is obtained by multiplying a proportionality constant in zenith wet delay (ZWD). This proportionality constant depends on parameter T m . The parameter T m depends on surface temperature which varies from one place to another. When this variation is large, a more specific T m is required for a region for accurate PWV estimation. The topography of the northern Indian region is quite complex and the Himalayan mountains play a vital role in several atmospheric processes in this region. Thus a regional T m model is required for this region which should be developed with observational input over the foothills of northwestern Himalayas. In the present study, a regional weighted mean temperature model is developed over this region using five years of radiosonde observations from 2016 to 2020 over five stations New Delhi, Patiala, Dehradun, Jammu, and Srinagar. Site-specific models are also developed over these locations. Their accuracies are assessed with respect to existing site-specific, regional and global models and radiosonde observations. The results show that differences between developed regional model and existing regional and global linear models are less than 3 °K. The developed regional and site specific models have the T m differences of the order of 2 °K to 3 °K which is less than the difference between other models and radiosonde observations. Site-specific models provide slightly better accuracies (less than 1°K) when compared with the developed regional model. PWV is estimated using both the models and an average difference of less than 1 mm is found when compared with radiosonde observations. The developed regional model has better PWV accuracy than any other existing model in this region. Hence, both site-specific and regional models developed in this study can be used for further PWV estimation in the north Indian region.

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.

Spatial–Temporal Relationship Study between NWP PWV and Precipitation: A Case Study of ‘July 20’ Heavy Rainstorm in Zhengzhou

In order to study and forecast extreme weather, a comprehensive and systematic analysis of the spatial and temporal relationship between Precipitable Water Vapor (PWV), predicted by Numerical Weather Predication (NWP) data, and precipitation, is necessary. The goal of this paper was to study the temporal and spatial relationship between PWV and precipitation during the so-called ‘July 20’ (18–21 July 2021) heavy rainstorm in Zhengzhou. Firstly, the PWV data provided by 120 radiosonde stations uniformly distributed throughout the world, and two IGS stations in China, in 2020, was used to evaluate the accuracy of PWV estimation by ERA5 and MERRA-2 data, and the factors affecting the accuracy of NWP PWV were explored. Secondly, ERA5 PWV and the precipitation data of six meteorological stations were used to qualitatively analyze the relationship between PWV and precipitation during the ‘July 20’ heavy rainstorm in Zhengzhou. Finally, a quantitative study was conducted by an eigenvalue matching method. The main experimental results were as follows. Compared with MERRA-2 PWV, the accuracy of ERA5 PWV was slightly higher. Latitude, altitude and season were the influencing factors of the NWP PWV estimation accuracy. The change trend of ERA5 PWV was consistent with both 24 h cumulative precipitation and surface precipitation during the ‘July 20’ heavy rainstorm in Zhengzhou. The average optimal matching degree and optimal matching time between NWP PWV and surface precipitation during the ‘July 20’ heavy rainstorm in Zhengzhou was 56.6% and 3.68 h, respectively. The maximum optimal matching degree was 80.3%. The spatial–temporal relationship between NWP PWV and surface precipitation was strong.