Abstract
Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the “threshold-based” models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010–2017 at the co-located HKSC–KP (King’s Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model.
Highlights
Atmospheric water vapor, as one of the main greenhouse gases, plays an important role in meteorological applications and is often expressed in the form of precipitable water vapor (PWV), which has been estimated using the Global Navigation Satellite Systems (GNSS) technique since the early 1990s [1]
Based on an existing five-predictor PWV-based model that was based on monthly thresholds to make predictions for heavy precipitation events, a new model based on diurnal thresholds was developed
The new model was evaluated by comparing its prediction results for the summer in 2018 and 2019 against the hourly precipitation records in the same duration
Summary
Atmospheric water vapor, as one of the main greenhouse gases, plays an important role in meteorological applications and is often expressed in the form of precipitable water vapor (PWV), which has been estimated using the Global Navigation Satellite Systems (GNSS) technique since the early 1990s [1]. With the rapid development of the GNSS and the widespread establishment of various scale ground-based tracking networks, the application of the GNSS-derived PWV (named GNSS-PWV hereinafter) to climate research and weather predictions has been well advanced [5,6,7,8], especially for severe weather events [9,10]. This is due to the high accuracy, high spatiotemporal resolution, and allweather availability of the GNSS-PWV [11,12,13,14]. Based on data collected at GNSS tracking stations in Zhejiang Province, the model led to an 82% correct detection rate but high false alarm rates (FAR): 60–70%
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