Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV

Abstract

Nowadays, the Global Navigation Satellite Systems (GNSS) have become an effective atmospheric observing technique to remotely sense precipitable water vapor (PWV) mainly due to their high spatiotemporal resolutions. In this study, from an investigation for the relationship between GNSS-derived PWV (GNSS-PWV) and heavy precipitation, it was found that from several hours before heavy precipitation, PWV was probably to start with a noticeable increase followed by a steep drop. Based on this finding, a new model including five predictors for heavy precipitation prediction is proposed. Compared with the existing 3-factor model that uses three predictors derived from the ascending trend of PWV time series (i.e., PWV value, PWV increment and rate of the PWV increment), the new model also includes two new predictors derived from the descending trend: PWV decrement and rate of PWV decrement. The use of the two new predictors for reducing the number of misdiagnosis predictions is proposed for the first time. The optimal set of monthly thresholds for the new five-predictor model in each summer month were determined based on hourly GNSS-PWV time series and precipitation records at three co-located GNSS/weather stations during the 8-year period 2010–2017 in the Hong Kong region. The new model was tested using hourly GNSS-PWV and precipitation records obtained at the above three co-located stations during the summer months in 2018 and 2019. Results showed that 189 of the 198 heavy precipitation events were correctly predicted with a lead time of 5.15 h, and the probability of detection reached 95.5%. Compared with the 3-factor method, the new model reduced the FAR score by 32.9%. The improvements made by the new model have great significance for early detection and predictions of heavy precipitation in near real-time.

Understanding the Present-Day Spatiotemporal Variability of Precipitable Water Vapor over Ethiopia: A Comparative Study between ERA5 and GPS

“…In any column of air, the amount of water vapor, or precipitable water vapor (PWV), provides an upper limit to the potential precipitation which could fall from that column of air [1]. In particular, many studies have confirmed that PWV variations can effectively reveal the occurrences and the life cycles of precipitation events [3][4][5][6].…”

A New Method for Determining an Optimal Diurnal Threshold of GNSS Precipitable Water Vapor for Precipitation Forecasting

“…where T s is the surface temperature ( • C) of the site. More information regarding the retrieval of GNSS-PWV can be found in our previous study [17], in which the accuracy of the retrieved GNSS-PWV at the HKSC station was also evaluated, with a root mean square error of 2.2 mm, and proved to be acceptable for meteorological research.…”

“…However, these monthly thresholds were often given empirically or selected from a certain value range [15,27]. In our recent study [17], an optimal set of monthly precipitation thresholds were determined based on the CSI, and the threshold for each predictor was unique for each of the three summer months. Generally, the method for the determination of monthly precipitation thresholds for a predictor based on the CSI mainly contained the following steps: (1) A set of candidate threshold values are selected, and the principles for the selection is elaborated in Section 3.3.1; (2) the above candidate threshold values, together with the GNSS-PWV sample data and precipitation records, were used to calculate n 11 , n 12 , and n 21 in Equation ( 5) during the period for thresholds determination, e.g., the 8 years from 2010 to 2017 our previous study [17]; (3) the CSI score resulting from each of the candidate thresholds was calculated using Equation ( 5), and the candidate threshold value that leads to the highest CSI score was determined as the optimal threshold.…”

“…In the studies [15,31], empirical thresholds for predictors were simply selected from values given within a certain range without justification. In contrast, in our recent study [17], an optimal set of thresholds were determined according to the critical success index (CSI), and each predictor in each summer month had a unique threshold value (rather than a threshold range).…”

“…Zhao et al [16] developed an improved model based on GNSS-PWV timeseries with a 5-minute resolution, and the model resulted in a better than 90% correct detection rate. In our previous study [17], a new model including five predictors derived from GNSS-PWV timeseries was developed, and the model's probability of detection (POD) and FAR were 95.5% and 28.9%, respectively. Apart from these threshold-based models, for which a set of predefined thresholds for the predictors adopted in the model were used to make predictions, the neural network (NN) technique has also been applied to precipitation prediction by incorporating PWV with other atmospheric parameters as input variables of the model [18,19].…”