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The precise point positioning (PPP) service via the B2b signal (PPP-B2b) on the BeiDou Navigation Satellite System (BDS) provides high-accuracy orbit and clock data for global navigation satellite systems (GNSSs), enabling real-time atmospheric data acquisition without internet access. In this study, we assessed the quality of orbit, clock, and differential code bias (DCB) products from the PPP-B2b service, comparing them to post-processed products from various analysis centres. The zenith tropospheric delay (ZTD) and precipitable water vapour (PWV) were computed at 32 stations using the PPP technique with PPP-B2b corrections. These results were compared with post-processed ZTD with final orbit/clock products and ZTD/PWV values derived from the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) and radiosonde data. For stations between 30° N and 48° N, the mean root mean square error (RMSE) of ZTD for the PPP-B2b solution was approximately 15 mm compared to ZTD from the International GNSS Service (IGS). However, accuracy declined at stations between 30° N and 38° S, with a mean RMSE of about 25 mm, performing worse than ZTD estimates using Centre National d'Études Spatiales (CNES) products. The mean RMSEs of PWV derived from PPP-B2b were 3.7 mm and 4.4 mm when compared to PWV from 11 co-located radiosonde stations and ERA5 reanalysis, respectively, and underperformed relative to CNES solutions. Seasonal variability in GNSS-derived PWV was also noted. This reduction in accuracy limits the global applicability of PPP-B2b. Despite these shortcomings, satellite-based PPP services like PPP-B2b remain viable alternatives for real-time positioning and atmospheric applications without requiring internet connectivity. [ABSTRACT FROM AUTHOR]

In recent years, tropospheric products obtained from ground-based global navigation satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and precipitable water vapor (PWV) estimates, have advanced their usages in meteorological applications such as the detection of precipitation events. Generally, a cumulative anomaly (CA) time series of any atmospheric variable, which represents the long-term departure of the variable from its “normal” cycle, is widely used for quantitatively estimating the variable’s variations in response to a weather event. In this study, a new cumulative anomaly-based model (NCAM) containing 14 variables, including not only PWV and ZTD values but also their respective six types of derivatives, for detecting heavy precipitation was developed. The 6-h CA time series of the variables were calculated based on the data of hourly precipitation records and time series of ZTD and PWV collected at the co-located HKSC–King’s Park (KP) stations over the eight-year period 2010–2017. The model was evaluated using the 14 variables’ CA time series to detect heavy precipitation events happened in the summer months over the period 2018–2019, and precipitation records in the same period were used as the reference. Results demonstrated that 99.1% of heavy precipitation was correctly detected by the NCAM with a lead time of 2.87 h, and the false alarm ratio (FAR) score resulting from the model was reduced to 22.4%. In addition, two case studies were also conducted to verify the effectiveness of the NCAM. These results all provide a promising direction for the application of using the CA time series of GNSS tropospheric products to the detection of heavy precipitation events. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Global navigation satellite systems (GNSSs) have become an important tool to derive atmospheric products, such as the total zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) for weather and climate studies. The ocean tide loading (OTL) effect is one of the primary errors that affects the accuracy of GNSS-derived ZTD/PWV, which means the study and choice of the OTL model is an important issue for high-accuracy ZTD estimation. In this study, GNSS data from 1 January 2019 to 31 January 2019 are processed using precise point positioning (PPP) at globally distributed stations. The performance of seven widely used global OTL models is assessed and their impact on the GNSS-derived ZTD is investigated by comparing them against the ZTD calculated from co-located radiosonde observations. The results indicate that the inclusion or exclusion of the OTL effect will lead to a difference in ZTD of up to 3–15 mm for island stations, and up to 1–2 mm for inland stations. The difference of the ZTD determined with different OTL models is quite small, with a root-mean-square (RMS) value below 1.5 mm at most stations. The comparison between the GNSS-derived ZTD and the radiosonde-derived ZTD indicates that the adoption of OTL models can improve the accuracy of GNSS-derived ZTD. The results also indicate that the adoption of a smaller cutoff elevation, e.g., 3° or 7°, can significantly reduce the difference between the ZTDs determined by GNSS and radiosonde, when compared against a 15° cutoff elevation. Compared to the radiosonde-derived ZTD, the RMS error of GNSS-derived ZTD is approximately 25–35 mm at a cutoff elevation of 15°, and 15–25 mm when the cutoff elevation is set to 3°. [ABSTRACT FROM AUTHOR]

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. • Nowadays, GNSS-derived atmospheric products have heralded a new era of meteorological applications. • The anomaly series of a variable is widely used to investigate the deviations of its raw series from a certain normal cycle. • A new model for detecting heavy rainfall using anomaly-based percentile thresholds of predictors was first established. • These predictors can effectively reflect the complete picture of the variations in the PWV series prior to heavy rainfall. • Results showed that 97.6% of heavy rainfalls were correctly detected and the seasonal false alarm rate was 13.4 %. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a growing interest in using GNSS observations to detect heavy precipitation. In this study, a neural network-based (NN-based) approach taking seven meteorological variables as input data was developed based on the back propagation (BP) algorithm for detecting heavy precipitation. Apart from the surface meteorological variables of temperature, pressure and relative humidity, the model has also adopted other information such as day-of-year, hour-of-day and GNSS-derived zenith total delay and precipitable water vapor (PWV) as input variables. The feasibility of using these variables for developing the BP-NN-based model was elaborated by conducting the feature analysis of the seven input variables. In addition, the criterion for selecting a proper size of training sample was also briefly investigated by studying the impact and sensibility of the sample lengths in the model. The proposed model was developed using a sample size of an 8-year (2010–2017) period in the summer at a pair of co-located GNSS/weather stations−HKSC-KP in Hong Kong. The use of a long-term data is to "reliably" capture the characteristics of the selected variables. The detection results for the summer months in 2018 and 2019 were then compared against corresponding precipitation records to valid the effectiveness of the newly proposed model. Results of the correct detection and false alarm rates were 94.5 % and 20.8 %, respectively, which were significant improvements compared with the existing models. [Display omitted] • Recent years have witnessed a growing interest in using GNSS observations to detect heavy precipitation. • A neural network-based approach taking seven meteorological variables as input data was developed based on the back propagation algorithm for detecting heavy precipitation. • The rationality of using those variables for developing the new model was elaborated by conducting a preliminary feature analysis. • The criterion for selecting a proper size of training sample was briefly investigated by studying the impact and sensibility of the sample lengths in the new model. • Results of the correct detection and false alarms were 94.5 % and 20.8 %, respectively, which were significant improvements compared with the existing models. [ABSTRACT FROM AUTHOR]