Detecting heavy rainfall using anomaly-based percentile thresholds of predictors derived from GNSS-PWV

Nowadays, tropospheric products obtained from the Global Navigation Satellite Systems (GNSS) observations, e. g., precipitable water vapor (PWV), have heralded a new era of GNSS meteorological applications, especially for the detection of heavy rainfall. In meteorological studies, the anomaly temporal series of an atmospheric variable is widely used to investigate the deviations of its raw series from a certain normal cycle, which is defined based on a specific purpose, e.g., its responses to a specific weather event. In this study, a new model for detecting heavy rainfall using anomaly-based percentile thresholds of seven predictors derived from PWV was established. The seven predictors, which can effectively reflect the complete picture of the variations in the PWV series prior to heavy rainfall events, include hourly PWV value and its six types of derivatives. The diurnal mean values and anomaly-based percentile thresholds for these predictors were obtained based on their raw time series over the 8 year period 2010-2017 at the co-located HKSC-KP stations. Then these values were applied to the sample data over the period 2018-2019 for determining their anomalies and series of abnormality. Finally, the detection results were compared with the hourly rainfall records for evaluation. Results showed that 97.6% of heavy rainfalls were correctly detected with an average lead time of 4.13 h. The seasonal false alarm rate of 13.4% from the new model was reduced in comparison to existing models. By conducting the verification experiments of the new model at another two pair of stations in the Hong Kong region, similar results were also obtained. These results all indicate that the anomaly-based percentile thresholds of predictors derived from PWV can be effectively applied to the detection of heavy rainfall events.

Automated summary

A new model for detecting heavy rainfall events using anomaly-based percentile thresholds of predictors derived from PWV was established. Results showed that 97.6% of heavy rainfalls were correctly detected with an average lead time of 4.13 h. The new model reduced the seasonal false alarm rate by 13.4% compared to existing models.