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.

Some seasonal natural floods can be attributed to typhoons that bring a large amount of atmospheric water vapor, and variations in atmospheric water vapor can be reflected in the precipitable water vapor (PWV). Therefore, monitoring typhoons based on the anomalous variations of the PWV is the focus of this paper. The anomalous variations of ERA5(fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting)-derived PWV with other atmospheric parameters related to typhoons, such as precipitation, pressure, and wind, were first analyzed during typhoon periods. After that, a typhoon-monitoring method with and without considering the typhoon’s acceleration was proposed according to the time of the maximum value of the PWV during the typhoon period in this paper. Corresponding experiments based on the measured and simulated data were performed to evaluate the proposed method. The experimental measurement of Typhoon Hato revealed that the velocity of the typhoon’s movement estimated by the proposed method was close to the observed value, and the maximum difference between the estimated and observed values was less than 3 km/h. A simulated experiment was also carried out in which the acceleration of the typhoon’s movement was also considered. The simulated results verified the reliability and feasibility of the proposed method. The estimated velocity and acceleration of the typhoon’s movement were almost equal to the true values under the cases of using different numbers of stations and selecting various typhoon locations. Such results obtained above indicate that the method proposed in this paper has a significant potential application value for typhoon monitoring.

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 two-and three-channel ratio, and global navigation satellite system (GNSS) PWV, recorded by the SuomiNet GNSS network, are established using a backpropagation neural network (BPNN). Verification shows that the root mean square error (RMSE)/standard deviation (STD)/bias of the two-channel ratio PWV is 1.29/1.29/0.02 mm, respectively, and the improvements of RMSE and STD are 66.32% and 37.98%, respectively. The RMSE/STD/bias values of the three-channel ratio PWV are 1.29/1.29/0.02 mm, respectively, 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 two-and three-channel ratio methods can yield satisfactory PWV estimates. Compared with the MODIS PWV products, the proposed method yields remarkable progress.

Ecological restoration and climate change in the Loess Plateau region have become research hotspots. Climate change and anthropogenic activities have led to spatial–temporal pattern variations in vegetation and extreme climatic indices and meteorological factors. Therefore, obtaining a better understanding is necessary of the internal relations between vegetation and meteorological factors. In this paper, the interplay between vegetation index and various factors, including extreme climatic indices and meteorological factors, during a long-term time series is investigated using Mann–Kendall trend analysis, and Pearson correlation coefficient analysis. The mechanisms of interaction between vegetation growth and various factors in the Loess Plateau are then analyzed. Results reveal that (i) the rapid growth of vegetation during 2000–2015 has made a major contribution to the growth trend of the Loess Plateau in the past 33 years (1982–2015). During 2000–2015, the increase of vegetation may inhibit the increase of extreme warm index and the decrease of extreme cold index; (ii) a warm and dry climate developed with decreasing relative humidity and increasing temperature; (iii) the normalized vegetation index (NDVI) is strongly correlated with extreme climatic indices and meteorological factors, especially precipitable water vapor (PWV), with a correlation coefficient of 0.94; and (iv) the daily temperature range, diurnal temperature range and sunshine duration (SSD) exerted different time-delay effects on vegetation growth in the Loess Plateau. The above findings provide an essential theoretical basis for ecological policy formulation in the Loess Plateau.