An Improved Model for Detecting Heavy Precipitation Using GNSS-Derived Zenith Total Delay Measurements

Abstract

In recent years, precipitable water vapor (PWV) have been widely used in heavy precipitation prediction, which is obtained from a conversion of the zenith total delay (ZTD) of the GNSS signal. Since the parameter directly estimated for the tropospheric delay from GNSS data processing is the ZTD, this study investigated the feasibility of directly using ZTD to predict heavy precipitation. Based on the finding that, prior to a heavy precipitation events, ZTD was likely to start with a duration of continuous rise followed by a sharp drop, a new heavy precipitation detection model containing seven predictors derived from ZTD was established. The seven predictors reflect not only the ascending and descending trends, but also long-term and short-term variations in the ZTD time series. Three criteria, representing differnet situations for the formation of heavy precipitation, were also constructed using the predictors to detect heavy precipitation. The optimal set of thresholds for the seven predictors for each summer month were determined based on hourly ZTD and precipitation records at a pair of co-located GNSS/weather station−HKSC-KP in Hong Kong over the period 2010−2017. The model was evaluated using the predictions in 2018 and 2019 to compare against the corresponding precipitation records. Results showed that the new model correctly predicted 98.8% of the heavy precipitation events, with a mean lead time of 4.37 h. Compared with the existing models, the new model also reduced the false alarms by 32.3%. Similar results were also

A Comprehensive Study on Factors Affecting the Calibration of Potential Evapotranspiration Derived from the Thornthwaite Model

“…(1) This practice can avoid a possible degradation in the accuracy caused by errors introduced from the use of meteorological variables in the conversion process from ZTD to PWV. ( 2) Various studies have also confirmed that the time series of ZTD and PWV have quite similar variation trends [35,36], indicating that the two variables may have analogical influences on the linear calibration exercises. The second novelty lies in the fact that the statistical parameter of mean Julian Date (MJD), reflecting the time-varying characteristics of other atmospheric variables, was first introduced to an application of this type.…”

“…The retrieval of ZTD time series over the 14-year study period at the selected ten stations was conducted by using Bernese GNSS Software Version 5.2 [46]. The detailed data processing strategies were generally the same as those adopted in our previous studies [35], including the double-difference approach, the elevation angle cutoff of 3 • , the Vienna Mapping Function 1, and the operational International GNSS Service Finals clocks and orbits [47,48]. Regarding temporal issues, a 27-hour observation window was selected, and the temporal resolution of ZTD estimates as well as estimation spacing were both set to 5 min [49,50].…”

“…With the adoption of these strategies, the accuracy of GNSS-derived atmospheric products was fully validated in different study areas over the past few years [52][53][54]. Specific to the Hong Kong region, in our previous studies [33,35], the accuracy of ZTD and PWV were tested and assured using sounding profiles as the reference, effectively meeting the requirements for operational use in the meteorological community.…”

“…Since the GNSS sites are dispersedly distributed in the study region, to conduct a more reasonable analysis and to effectively guarantee the adaptability of the ZTD time series, as well as to avoid introducing errors on account of spatial distances, the above experiments only adopted the ZTD values obtained from the nearest GNSS stations to each of the three weather stations. This practice is similar to those adopted in our previous studies [33,35]. By calculating and analyzing the distances between each pair of GNSS and weather stations, three GNSS stations were selected for their corresponding "co-located" weather stations, i.e., three pairs of GNSS/weather stations were adopted (HKA-HKSL, KP-HKSC, and ST-HKST).…”

Investigating the Optimal Spatial Resolution for Assimilating GNSS PWV into an NWP System to Improve the Accuracy of Humidity Field

“…2) GNSS PWV: It is noteworthy that while ZTD has been shown to exhibit a close relationship with atmospheric humidity [17], [53], which can also circumvent the introduction of error sources associated with converting ZTD to PWV, it cannot be directly considered as a representative measure of water vapor content in the atmosphere. Additionally, numerous previous studies have unequivocally demonstrated the benefits of assimilating PWV into NWP models [33], [54], [55].…”