Retrieval Of Precipitable Water Vapor Research Articles

Analysis of the Response Relationship Between PWV and Meteorological Parameters Using Combined GNSS and ERA5 Data: A Case Study of Typhoon Lekima

Precipitable water vapor (PWV) is a crucial parameter of Earth’s atmosphere, with its spatial and temporal variations significantly impacting Earth’s energy balance and weather patterns. Particularly during meteorological disasters such as typhoons, PWV and other meteorological parameters exhibit dramatic changes. Studying the response relationship between PWV and typhoon events, alongside other meteorological parameters, is essential for meteorological and climate analysis and research. To this end, this paper proposes a method for analyzing the response relationship between PWV and meteorological parameters based on Wavelet Coherence (WTC). Specifically, PWV and relevant meteorological parameters were obtained using GNSS and ERA5 data, and the response relationships between PWV and different meteorological parameters before and after typhoon events were studied in time–frequency domain. Considering that many GNSS stations are not equipped with meteorological monitoring equipment, this study interpolated meteorological parameters based on ERA5 data for PWV retrieval. In the experimental section, the accuracy of ERA5 meteorological parameters and the accuracy of PWV retrieval based on ERA5 were first analyzed, verifying the feasibility and effectiveness of this approach. Subsequently, using typhoon Lekima as a case study, data from six GNSS stations affected by the typhoon were selected, and the corresponding PWV was retrieved using ERA5. The WTC method was then employed to analyze the response relationship between PWV and meteorological parameters before and after the typhoon’s arrival. The results show that the correlation characteristics between PWV and pressure can reveal different stages before and after the typhoon passes, while the local characteristics between PWV and temperature better reflect regional precipitation trends.

An optimized BP neural network for modeling zenith tropospheric delay in the Chinese mainland using coupled particle swarm and genetic algorithm

ABSTRACT Tropospheric delay influences high-precision navigation positioning and precipitable water vapor retrieval with the Global Navigation Satellite System (GNSS). Existing Zenith Tropospheric Delay (ZTD) models often struggle to accurately capture the non-linear variations in tropospheric delay. Therefore, this study employs the coupled Particle Swarm Optimization (PSO) algorithm with the Genetic Algorithm Back Propagation (GABP) neural network, combined with ERA5 reanalysis meteorological data, to develop an optimized model (PSO-GABP) for ZTD in the Chinese mainland. Nevertheless, ZTD data at the target point are obtained through four different methods: the integration method, model method, and GPT3 models at varying resolutions (EZTD_P, EZTD_S, GPT3_1, and GPT3_5). The analysis reveals the following: (1) The Root Mean Square (RMS) errors of the ZTD values obtained through these different methods are 1.86 cm, 3.42 cm, 3.99 cm, and 4.09 cm, respectively, when verified against GNSS_ZTD data from 2016. The optimized model yields ZTD values with the RMS error of 0.98 cm, 1.96 cm, 2.34 cm, and 2.36 cm, representing improvements of 47.3%, 42.7%, 41.4%, and 42.3% compared to the pre-optimization results. These improvements are significant; (2) The predictive capability of the constructed ZTD model is evaluated using GNSS_ZTD data from 2019 as a reference. The PSO_EZTD_P model demonstrates excellent accuracy and practicality in the Chinese mainland. As a result, the tropospheric delay optimization model based on the PSO-GABP neural network can provide valuable references for real-time GNSS navigation positioning and precipitable water vapor detection in the Chinese mainland.

Precipitable water vapor retrieval for rainfall forecasting using BDS-3 PPP-B2b signal in the coastal region of China

Abstract Currently, rainfall cannot be accurately forecast because of poor network communication at the ocean. The advantage of the BeiDou Global Navigation Satellite System (BDS-3) precise point positioning (PPP-B2b) signal, which does not rely on network communication to receive data, is that it can provide precipitable water vapor (PWV) retrieval application services for the open seas in eastern China, where the communication system presents difficulties. In this study, data from stations in the coastal region of China were used to establish a rainfall forecasting method for monitoring extreme weather on the sea. First, the service performance of PPP-B2b was explored. Then, based on 17 Chinese coastal stations, the PWV accuracy was evaluated. Finally, based on an analysis of the relationship between PWV and actual rainfall, a threshold rainfall forecasting method based on a sliding window was constructed. The experimental results show that the PWV accuracy varies slightly depending on the geographic location, in which the mean absolute error in the North Sea region is the smallest (2.1 mm), that of the South China Sea region is the largest (2.60 mm), and that of the East China Sea region is in the middle (2.48 mm). The optimal predictors of the constructed 12 h sliding-window threshold rainfall prediction method are a PWV maximum of 49 mm, PWV increase of 5 mm, and PWV increase rate of 1.2 mm h−1. The prediction results can reach a critical success index value of more than 45%, indicating high prediction accuracy and applicability to the coastal region of China during the same period.

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

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

Radiometric Calibration of the Near-Infrared Bands of GF-5-02/DPC for Water Vapor Retrieval

The GaoFen (GF)-5-02 satellite is one of the new generations of hyperspectral observation satellites launched by China in 2021. The directional polarimetric camera (DPC) is an optical sensor onboard the GF-5-02 satellite. The precipitable water vapor (PWV) is a key detection parameter of DPC. However, the existing PWV data developed using DPC data have significant errors due to the lack of the timely calibration of the two bands (865, 910 nm) of DPC used for PWV retrieval. In order to acquire DPC PWV data with smaller errors, a calibration method is developed for these two bands. The method consists of two parts: (1) calibrate the 865 nm band of the DPC using the cross-calibration method, (2) calibrate the 910 nm band of the DPC according to the calibrated 865 nm band of the DPC. This method effectively addresses the issue of the absence of a calibration method for the water vapor absorption band (910 nm) of the DPC. Regardless of whether AERONET PWV data or SuomiNET PWV data are used as the reference data, the accuracy of the DPC PWV data developed using calibrated DPC data is significantly superior to that of the DPC PWV data retrieved using data before recalibration. This means that the calibration method for the NIR bands of the DPC can effectively enhance the quality of DPC PWV data.

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.

First PWV Retrieval Using MERSI‐LL Onboard FY‐3E and Cross Validation With Co‐Platform Occultation and Ground GNSS

AbstractFY‐3E plays a vital role in the meteorological global earth observing system. Precipitable water vapor (PWV) is an essential parameter for the water cycle and global climate change. Here, we carry out the PWV retrieval using the MERSI‐LL sensor onboard the FY‐3E satellite for the first time. The retrieval accuracy under different cloudage conditions is validated by the extra PWV from ground‐based GNSS and spaceborne occultation. For the results against ground‐based GNSS, the total accuracy shows an RMSE of 2.69–3.36 mm as the clouds increase, and correlation coefficients higher than 0.95. The spatial accuracy distribution indicates that inland stations have higher accuracy than the coast and island stations. As for the results against spaceborne occultation, the verification accuracy varies with the spatial pairing distance, showing poor accuracy in the low latitude area. This study can provide an essential reference for the community to understand the current water vapor inversion performance of MERSI‐LL.

An Improved Model for the Retrieval of Precipitable Water Vapor in All-Weather Conditions (RCMNT) Based on NIR and TIR Recordings of MODIS

An Improved Model for the Retrieval of Precipitable Water Vapor in All-Weather Conditions (RCMNT) Based on NIR and TIR Recordings of MODIS

Precipitable Water Vapor Retrieval Based on Near-Infrared Channel Data from FengYun-3G Satellite/Medium Resolution Spectral Imager-Rainfall Mission

Precipitable Water Vapor Retrieval Based on Near-Infrared Channel Data from FengYun-3G Satellite/Medium Resolution Spectral Imager-Rainfall Mission

Retrieving Precipitable Water Vapor Over Land From Satellite Passive Microwave Radiometer Measurements Using Automated Machine Learning

AbstractAccurately retrieving precipitable water vapor (PWV) over wide‐area land surface remains challenging. Unlike passive infrared remote sensing, passive microwave (PMW) remote sensing provides almost all‐weather PWV retrievals. This study develops a PMW‐based land PWV retrieval algorithm using automated Machine learning (ML) (AutoML). Data from the Advanced Microwave Scanning Radiometer 2 serve as the main predictor variables and high‐quality Global Positioning System (GPS) PWV data as the target variable. Unprecedentedly large GPS training samples (over 50 million) from more than 12,000 stations worldwide are used to train the AutoML model. New predictors with clear physical mechanisms enable PWV retrieval over almost any land surface type, including snow cover and near open water. Validation shows good agreement between PWV retrievals and ground observations, with a root mean square error of 3.1 mm. This encouraging outcome highlights the potential of the algorithm for application with other PMW radiometers with similar wavelengths.

An Optimized Framework for Precipitable Water Vapor Mapping Using TS-InSAR and GNSS

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

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.

PWV Retrieval Performance Evaluation for the Fresh BDS‐3 With Multisource Data

AbstractThe entire global operation of the newly launched BDS global navigation satellite system (BDS‐3) was completed in July 2020. After that, a series of performance tests were conducted, such as positioning, navigation, and signal experiments. However, the precipitable water vapor (PWV) retrieval performance evaluation of BDS‐3 has not yet been carried out, so in this paper, we thoroughly verified its accuracy with AIRS v7, ERA5, and radiosonde data to know the current performance of BDS‐3 PWV. From the tests applied with different satellite systems, we found that BDS‐3 has a comparable PWV retrieval performance as global positioning system (GPS), and a better outcome was achieved using the combined data from GPS and BDS‐3. Furthermore, BDS‐3 PWV accuracy calibrated by AIRS v7 showed a strong correlation with the spatial distribution. In addition, the PWV content decreased with the climbing of the latitude and elevation, and the relative root mean square error (ReRMSE) indicated better in the low‐latitude areas. Finally, the effect of different surface types on PWV performance was analyzed, showing the BDS‐3 PWV accuracy in the land type was the highest, the sea one was the lowest, and the coast type was in the middle. These findings will provide a good reference for the community to understand the PWV performance of BDS‐3 at the present stage.

Improving the capability of water vapor retrieval from Landsat 8 using ensemble machine learning

Precipitable water vapor (PWV) facilitates the exchange of water and energy between the Earth's surface and the surrounding atmosphere. PWV can be retrieved by employing the water vapor absorption channels in Landsat 8. Unfortunately, the conventional split-window covariance-variance ratio (SWCVR) method is susceptible to various factors, resulting in low retrieval accuracy and availability. Therefore, we improve the traditional SWCVR model and propose a new PWV retrieval method using ensemble machine learning to address these limitations. The new method uses Gradient Boosting Decision Tree (GBDT) to establish the model between brightness temperature, GNSS-derived PWV, and related surface parameters. The results of the test set show that the improved SWCVR model has an RMSE, Bias, and availability of 0.4947 g/cm2, 0.0276 g/cm2, and 29.6%, respectively. By contrast, the GBDT model's corresponding values are 0.2870 g/cm2, −0.0094 g/cm2, and 67.9%, respectively. Compared with SWCVR, the GBDT improves the RMSE and availability by 41.99% and 38.3%, respectively. The GBDT algorithm is significantly better than the SWCVR model in low altitude and complex surface coverage areas. From a temporal perspective, the advantages of the GBDT method are more apparent in the summer. Finally, the SWCVR and GBDT models are externally validated using measured PWV data from the sun photometer and radiosonde, and the RMSE is 0.5148 g/cm2 and 0.3775 g/cm2, respectively. Based on the findings, we know that the accuracy of GBDT has significantly improved against the SWCVR.

Assimilating Sentinel‐3 All‐Sky PWV Retrievals to Improve the WRF Forecasting Performance Over the South China

AbstractWater vapor is a key driver for the evolution of weather system. To investigate the impact of assimilating Sentinel‐3 precipitable water vapor (PWV) on weather forecasting, Sentinel‐3 PWV retrievals over the South China with two different assimilation schemes are assimilated into the Weather Research and Forecasting (WRF) model. In the first assimilation scheme, only Sentinel‐3 clear‐sky PWV are assimilated, while Sentinel‐3 all‐sky PWV are assimilated for the second assimilation scheme. For both data assimilation schemes, we totally conduct 28 WRF data assimilation runs and forecasts for 28 selected days over two periods, that is, 14 days in March 2020 and 14 days in June 2020. The weather condition in June 2020 is much wetter than March 2020. Generally, assimilating Sentinel‐3 PWV improves the WRF forecasting performance, particularly for June 2020. Assimilation of all‐sky PWV outperforms assimilation of clear‐sky PWV. The comparison results with radiosonde profiles show that assimilating Sentinel‐3 PWV appreciably corrects the bias of WRF water vapor mixing ratio forecasting results for June 2020. The rainfall validation results show that both assimilation schemes show a positive impact in June 2020, but a neutral impact in March 2020. For June 2020, assimilating Sentinel‐3 all‐sky PWV improves rainfall forecast skill score by 2.4%, while the rainfall forecast score is improved by 1.0% after assimilating clear‐sky PWV. Additionally, assimilation of Sentinel‐3 PWV can modify the WRF moisture field, which further improves the rainfall spatial pattern.

Precipitable water vapor retrievals using a ground-based infrared sky camera in subtropical South America

Abstract. Atmospheric precipitable water vapor (PWV) is a critical quantity in fast-changing weather processes. Current retrieval techniques lack the spatial and/or temporal resolution necessary for a full PWV characterization. Here we investigate a retrieval method using an all-sky ground-based camera comprising a 14-bit 644 × 512-pixel microbolometer sensor array. The radiometrically calibrated infrared downwelling spectral radiance, Lλ, was acquired at rates of up to 3 min−1. For the studied site (23.56∘ S, 46.74∘W; 786 m a.s.l.) and spectral interval, Lλ is sensitive to the PWV; the vertical distribution of humidity; and their temporal, spatial, or seasonal variations. By comparing measured and simulated Lλ, we show that the PWV can be retrieved from prior knowledge of the local humidity profile. This information can originate from radiosonde data or statistical analysis of past vertical humidity distributions. Comparison with sun photometer PWV retrievals, for stable atmospheric conditions, showed an agreement of the average PWV within 2.8 % and a precision of subsequent retrievals of 1.9 %. The PWV was also retrieved as a bi-dimensional array, allowing for the investigation of spatial inhomogeneities of humidity distribution. The method can be used for daytime or nighttime retrievals, under partly cloudy sky conditions. Potential applications include studies on convection initiation processes.

Global Navigation Satellite System-Based Retrieval of Precipitable Water Vapor and Its Relationship with Rainfall and Drought in Qinghai, China

Qinghai Province is situated deep in inland China, on the Qinghai-Tibet plateau, and it has unique climate change characteristics. Therefore, understanding the temporal and spatial distributions of water vapor in this region can be of great significance. The present study applied global navigation satellite system (GNSS) technology to retrieve precipitable water vapor (PWV) in Qinghai and analyzed its relationship with rainfall and drought. Firstly, radiosonde (RS) data is used to verify the precision of the surface pressure (P) and temperature (T) from the fifth-generation atmosphere reanalysis data set (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as the zenith troposphere delay (ZTD), calculated based on the data from continuously operating reference stations (CORS) in Qinghai. Secondly, a regional atmospheric weighted mean temperature (Tm) (QH-Tm) model was developed for Qinghai based on P, T, and relative humidity, as well as the consideration of the influence of seasonal changes in Tm. Finally, the PWV of each CORS in Qinghai was calculated using the GNSS-derived ZTD and ERA5-derived meteorological data, and its relationship with rainfall and drought was evaluated. The results show that the ERA5-derived P and T have high precision, and their average root mean square (RMS), mean absolute error (MAE) and bias were 1.06/0.85/0.01 hPa and 2.98/2.42/0.03 K, respectively. The RMS, MAE and bias of GNSS-derived ZTD were 13.2 mm, 10.3 mm and −1.8 mm, respectively. The theoretical error for PWV was 1.98 mm; compared with that of RS- and ERA5-derived PWV, the actual error was 2.69 mm and 2.16 mm, respectively. In addition, the changing trend of GNSS-derived PWV was consistent with that of rainfall events, and it closely and negatively correlated with the standardized precipitation evapotranspiration index. Therefore, the PWV retrieved from GNSS data in this study offers high precision and good feasibility for practical applications; thus, it can serve as a crucial tool for investigating water vapor distribution and climate change in Qinghai.

An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data

As one of the most important greenhouse gases, water vapor plays a vital role in various weather and climate processes. In recent years, a near-infrared ratio technique based on satellite images has become a research hotspot in the field of precipitable water vapor (PWV) monitoring. This study proposes a Level 2A PWV data retrieval method based on Sentinel-2 images (S2-L2A), which considers land-cover types and is more suitable for local areas. The radiative transfer model MODTRAN 5 is used to simulate the atmospheric radiative transfer process and obtain lookup tables (LUTs) for PWV retrieval. The spatial distribution of S2-L2A PWV is validated using Global Positioning System (GPS), Terra-MODIS PWV product (MOD05), and Level 2A product provided by ESA (ESA-L2A), while the time series results are evaluated using MOD05. Results show that the PWV retrieved by S2-L2A is both highly correlated and has low bias with the three PWV products, and is closer to the reference data than the MOD05 and ESA-L2A PWV. The relative PWV value in the morning is: bare soil > vegetation-covered area > construction land; as the elevation increases, the PWV value decreases. This study also analyzes the error distribution of the PWV data retrieved by S2-L2A, and finds that inversion error increases with AOT value, but decreases with elevation and normalized difference vegetation index (NDVI). Compared with the three water vapor products, the PWV data retrieved by the proposed method has high accuracy and can provide large-scale and high-spatial-resolution PWV data for many research fields, such as agriculture and meteorology.

Analyzing correlations between GNSS retrieved precipitable water vapor and land surface temperature after earthquakes occurrence

Earthquake is a common and destructive natural disaster. The enormous amount of energy released from seismic events can result in anomalous land surface temperature (LST) and catalyze the accumulation of water vapor in the atmosphere. The majority of previous works are not consensual concerning precipitable water vapor (PWV) and LST after the earthquake. Here, we utilized multi-source data to analyze the changes of PWV and LST anomaly after three Ms 4.0–5.3 crustal earthquakes at low depth (8–9 km) that occurred in Qinghai-Tibet Plateau. Firstly, PWV retrieval using Global Navigation Satellite System (GNSS) technology is performed, showing that its root mean square error (RMSE) is less than 1.8 mm against radiosonde (RS) data or European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) PWV data. The PWV change derived from the nearest GNSS stations around the hypocenter during the earthquakes shows anomalies, and the results reveal that PWV anomalies occurred after the earthquakes, mainly obeying a trend of increasing first and then decreasing. In addition, LST increases three days before PWV peak with a thermal anomaly of 12 °C higher than that of previous days. Robust Satellite Technique (RST) algorithm and ALICE index on Moderate Resolution Imaging Spectroradiometer (MODIS) LST products are introduced to analyze the correlation between the abnormality of LST and PWV. Based on ten-year background field data (2012–2021), the results show that LST during the earthquake has more thermal anomaly occurrences than in previous years. The more severe the LST thermal anomaly is, the higher the probability of a PWV peak occurring.

Analyzing the Spatiotemporal Characteristics of Extreme Rainfall Using CAPE and GNSS-Derived ZTD Across China

The power-law relationship between precipitable water vapor (PWV)/convective available potential energy (CAPE), and extreme rainfall (ER) has been explored. However, the retrieval of PWV is reliant on the zenith total delay (ZTD) of the Global Navigation Satellite System (GNSS), and errors are introduced when converting ZTD to PWV. In this study, we propose the ZCER model, a comprehensive analysis model that integrates ZTD, CAPE, and ER, for investigating long-term variation in ER intensity and frequency, which expresses their close relationships in a novel approach and expands the application area of GNSS-derived ZTD. Daily ZTD, CAPE, and rainfall data were collected from 219 GNSS stations in China from 2011 to 2020 (10 years). Time- and frequency-domain information encapsulated in the three variables was extracted using wavelet coherence, proving that both ZTD and CAPE contributed to rainfall. The relationships between ZTD/log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> (CAPE) and log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> (ER) were investigated at the annual, seasonal, and monthly scales. The results revealed that the contribution of ZTD/CAPE to ER varied spatially and temporally. Furthermore, the synergistic contributions of ZTD and CAPE to ER were further investigated. Statistical results showed that CAPE and ZTD not only complemented each other on the geographical scales to ER in China but also on the seasonal and monthly scales. Moreover, the qualitative relationships between the ZTD, CAPE, and ER frequency were elucidated. Our findings validate the strong links between high ZTD, CAPE, and ER intensity and frequency in China on geographical and temporal scales.