No abstract

The existing methods have been used the Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) derived from Global Navigation Satellite System (GNSS) for rainfall forecasting. However, the occurrence of rainfall is highly related to a myriad of atmospheric parameters, and a good forecast result cannot be obtained if it only depends on a single predictor. This study focused on rainfall forecasting by using a number of atmospheric parameters (such as: temperature, relative humidity, dew temperature, pressure, and PWV) based on the improved Back Propagation Neural Network (BP–NN) algorithm. Results of correlation analysis showed that each meteorological parameter contributed to rainfall. Therefore, a short-term rainfall forecast model was proposed based on an improved BP–NN algorithm by using multiple meteorological parameters. Two GNSS stations and collocated weather stations in Singapore were used to validate the proposed rainfall forecast model by using three years of data (2010–2012). True forecast (TFR), false forecast (FFR), and missed forecast (MFR) rate were introduced as evaluation indices. The experimental result revealed that the proposed model exhibited good performance with TFR larger than 96% and FFR of approximately 40%. The proposed method improved TFR by approximately 10%, whereas FFR was comparable to existing literature. This forecasted result further verified the reliability and practicability of the proposed rainfall forecasting method by using the improved BP–NN algorithm.

Standardized precipitation evapotranspiration index (SPEI) is an acknowledged drought monitoring index, and the evapotranspiration (ET) used to calculated SPEI is obtained based on the Thornthwaite (TH) model. However, the SPEI calculated based on the TH model is overestimated globally, whereas the more accurate ET derived from the Penman–Monteith (PM) model recommended by the Food and Agriculture Organization of the United Nations is unavailable due to the lack of a large amount of meteorological data at most places. Therefore, how to improve the accuracy of ET calculated by the TH model becomes the focus of this study. Here, a revised TH (RTH) model is proposed using the temperature (T) and precipitable water vapor (PWV) data. The T and PWV data are derived from the reanalysis data and the global navigation satellite system (GNSS) observation, respectively. The initial value of ET for the RTH model is calculated based on the TH model, and the time series of ET residual between the TH and PM models is then obtained. Analyzed results reveal that ET residual is highly correlated with PWV and T, and the correlate coefficient between PWV and ET is −0.66, while that between T and ET for cases of T larger or less than 0 °C are −0.54 and 0.59, respectively. Therefore, a linear model between ET residual and PWV/T is established, and the ET value of the RTH model can be obtained by combining the TH-derived ET and estimated ET residual. Finally, the SPEI calculated based on the RTH model can be obtained and compared with that derived using PM and TH models. Result in the Loess Plateau (LP) region reveals the good performance of the RTH-based SPEI when compared with the TH-based SPEI over the period of 1979–2016. A case analysis in April 2013 over the LP region also indicates the superiority of the RTH-based SPEI at 88 meteorological and 31 GNSS stations when the PM-based SPEI is considered as the reference.

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.

Precipitable water vapor (PWV) with high precision and high temporal resolution can be obtained based on the global navigation and positioning system (GNSS) technique, which is important for GNSS in disaster prevention and mitigation. However, the related studies on drought monitoring using PWV are rarely performed before, which becomes the focus of this paper. This paper proposes a novel drought monitoring method using GNSS-derived PWV and precipitation, and a multi-time-scale standardized precipitation conversion index (SPCI) is established. This index is different from the traditional index in terms of expression, standardization and time scale. The proposed SPCI is then compared with SPI/SPEI/scPDSI (Standardized Precipitation Index/Standardized Precipitation Evapotranspiration Index/ self-calibrating Palmer Drought Severity Index) and applied to local and global drought monitoring. Validated results show that multi-time-scale SPCI has good consistency with the corresponding SPI/SPEI/scPDSI. The correlation between SPCI and SPEI is the strongest (more than 0.96) in 12-month scale, which indicates the application potential of SPCI in drought monitoring. In addition, applications for regional (Queensland, Australia) and global drought/wet monitoring further verify the capability of the proposed SPCI. The average percentage deviations of drought/wet monitoring between SPCI and SPEI are 2.77% and 3.75%, respectively on a global scale. The above results show that the SPCI developed in this study is efficiently applied to global flood/wet studies.