Except for its known aspects of positioning, navigation, and timing (PNT), the Global Navigation Satellite System (GNSS) has extended its application to the rainfall forecasting. GNSS-derived zenith total delay (ZTD) or precipitable water vapor (PWV) has been used as a single factor to predict the occurrence of rainfall; however, the rainfall is highly correlated with myriad atmospheric parameters, which cannot be perfectly reflected by a single predictor. In this article, an improved rainfall forecasting model (IRFM) is proposed to forecast the rainfall. The IRFM considers five predictors: monthly PWV value, seasonal PWV/ZTD variations, and their first derivatives: it can forecast rainfall using a single predicator or an arbitrary combination of those predicators. The merit of IRFM is reducing the false forecasted rainfall (FFR) events and missed detected rainfall (MDR) events as much as possible while guaranteeing the true detected rainfall (TDR) events. An optimized selecting principle of predictors’ threshold has been determined using the percentile method. The test experiment has been performed using five GNSS stations derived from continuously operating reference system (CORS) network of Zhejiang province, China. The analysis reveals that the IRFM considering five predictors provides a better performance than that only using a single predictor or a combination of arbitrary predictors. The statistical result shows the average TDR value of more than 95%, FFR value of less than 30%, and MDR of less than 5%, respectively. Compared to the existing rainfall forecasting methods using ZTD or PWV, the IRFM reduces the FFR and MDR, respectively, with the lowest values, while the TDR value is the highest.

Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is proposed in this study to predict rainfall with high accuracy and time resolution. Hourly PWV derived from Global Navigation Satellite System (GNSS) and temperature data are used as input parameters of the HRF model, and a support vector machine is introduced to train the proposed model. In addition, this model also considers the time autocorrelation of rainfall in the previous epoch. Hourly PWV data of 21 GNSS stations and collocated meteorological parameters (temperature and rainfall) for five years in Taiwan Province are selected to validate the proposed model. Internal and external validation experiments have been performed under the cases of slight, moderate, and heavy rainfall. Average root-mean-square error (RMSE) and relative RMSE of the proposed HRF model are 1.36/1.39 mm/h and 1.00/0.67, respectively. In addition, the proposed HRF model is compared with the similar works in previous studies. Compared results reveal the satisfactory performance and superiority of the proposed HRF model in terms of time resolution and forecast accuracy.

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.

Considering the noncore region influenced by El Niño-Southern Oscillation (ENSO) events, China is hardly investigated in terms of the vegetation variation during the ENSO period.Therefore, this study focused on increasing knowledge of vegetation growth and variation during the ENSO period.The novelty of this paper is introduced the moving window correlation analysis method to determine the thresholds of vegetation response to sea surface temperature anomaly (SSTa) and southern oscillation index (SOI) and analyze the main reasons for such variations.China was divided into seven areas based on different weather conditions, and the variations in vegetation growth in various areas during the ENSO period were analyzed. The response of vegetation to ENSO events in China was analyzed from the perspectives of precipitable water vapor (PWV), temperature, and precipitation, thereby revealing the interplay of multi-factors on vegetation growth.The main conclusions include (1) a positive vegetation response to El Niño exists all over China with thresholds of SSTa ≥ 1.87°C and SOI ≤ –3.27hPa, whereas a negative response of vegetation variation to La Niña exists with thresholds of SSTa ≤ –1.05°C and SOI ≥ 1.7hPa; (2) the correlations (p < 0.05) of PWV to normalized difference vegetation index (NDVI) (PWV-NDVI), temperature-NDVI, and precipitation-NDVI reached 0.84, 0.86, and 0.68, respectively, and PWV, temperature, and precipitation were negatively/positively abnormal during El Niño/La Niña periods; (3) in coastal areas of Southeast China, correlations between NDVI and PWV/temperature/ precipitation are poor and the opposite anomalies of PWV/temperature/ precipitation existed when compared to other areas of China.

Precipitable water vapor (PWV), derived from the Global Navigation Satellite System (GNSS), has contributed significantly to rainfall forecasting. However, another key parameter, convective available potential energy (CAPE), is strongly correlated with increases in extreme rainfall under the background of global warming but has rarely been investigated for rainfall forecasting. Therefore, a real-time rainfall nowcast (RRN) model that combines CAPE and PWV is proposed in this study. In addition, seasonal factors and the time autocorrelation of the predictors were considered. Here, the previous hourly PWV, CAPE, temperature, and rainfall were used to establish the RRN model and simulate/nowcast the next hourly rainfall based on the support vector regression, which was performed in a time span of five years at 23 GNSS stations in Taiwan province under four designed schemes to validate the performance of the proposed RRN model. The average root mean square (rms) and correlation coefficients of the proposed RRN model reached 0.34 mm/h and 0.96, respectively. Additionally, the linear relationships between the daily CAPE/PWV and extreme rainfall were investigated, revealing that PWV may contribute more to trigger extreme rainfall than CAPE. Finally, compared to existing quantitative rainfall forecast studies, the RRN model achieved significantly improved rainfall nowcast accuracy and thus has more potential applications in rainfall forecasting.

Atmospheric weighted mean temperature (T_m) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical T_m 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 T_m model for PWV retrieval in China, this paper analyzes and evaluates some typical T_m models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the T_m model for China (CT_m), the Global Weighted Mean Temperature-H (GT_m-H) and the Global Tropospheric (GTrop) models. The T_m values of these models are first obtained at corresponding radiosonde (RS) stations in China over the period of 2011 to 2020. The corresponding T_m 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 T_m 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, CT_m, GT_m-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 CT_m, GT_m-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 GT_m-H and CT_m 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 T_m for calculating PWV in China.