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.
Highlights
Precipitation is one of the most pivotal processes of the hydrologic cycle on the earth
The five predictors proposed in the new model contained the three commonly used predictors, precipitable water vapor (PWV) value, PWV increment and rate of the PWV increment, which are derived from the ascending trend of the PWV time series; the two new predictors, PWV decrement and rate of the PWV decrement, are proposed for the first time in this study for reducing the numbers of misdiagnosis predictions and the false alarm rate (FAR) scores while a better probability of detection (POD) score is ensured
In this study, based on a strong consistency between PWV and heavy precipitation, a new model containing five predictors for short-term heavy precipitation prediction was developed using the data of hourly Global Navigation Satellite Systems (GNSS)-PWV time series and precipitation records over the 8-year period 2010–2017
Summary
Precipitation is one of the most pivotal processes of the hydrologic cycle on the earth. The accuracy of water vapor obtained from these technologies is generally high, these technologies have some limitations, e.g., one-off attribute, high operational cost, low spatiotemporal resolution and limited to some weather conditions [14,15,16] These technologies can hardly satisfy some meteorological applications, especially for short-term weather monitoring and predictions [17,18]. This is the main motivation to use a new means, Global Navigation Satellite Systems (GNSS), to remotely sense the atmospheric water vapor content [19,20,21,22,23,24], due to its 24-h variability, high accuracy, global coverage, low cost, long-term stability, high spatiotemporal resolution and all-weather operability [25,26,27,28,29]. This makes the best use of GNSS data for studies and applications on short-term weather predictions feasible and meaningful [17,30,31,32], in addition to facilitating climate change research [33,34,35]
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