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  • MDPI AG  (4)
  • Li, Li  (4)
  • Wang, Xiaoming  (4)
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  • MDPI AG  (4)
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  • 1
    Online Resource
    Online Resource
    A New Method for Determining an Optimal Diurnal Threshold of GNSS Precipitable Water Vapor for Precipitation Forecasting
    MDPI AG ; 2021
    In:  Remote Sensing Vol. 13, No. 7 ( 2021-04-04), p. 1390-
    Details
    In: Remote Sensing, MDPI AG, Vol. 13, No. 7 ( 2021-04-04), p. 1390-
    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.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs13071390
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2513863-7
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Weighted Mean Temperature Modelling Using Regional Radiosonde Observations for the Yangtze River Delta Region in China
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 8 ( 2022-04-15), p. 1909-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 8 ( 2022-04-15), p. 1909-
    Abstract: Precipitable water vapor can be estimated from the Global Navigation Satellite System (GNSS) signal’s zenith wet delay (ZWD) by multiplying a conversion factor, which is a function of weighted mean temperature (Tm) over the GNSS station. Obtaining Tm is an important step in GNSS precipitable water vapor (PWV) conversion. In this study, aiming at the problem that Tm is affected by space and time, observations from seven radiosonde stations in the Yangtze River Delta region of China during 2015−2016 were used to establish both linear and nonlinear multifactor regional Tm model (RTM). Compared with the Bevis model, the results showed that the bias of yearly one-factor RTM, two-factor RTM and three-factor RTM was reduced by 0.55 K, 0.68 K and 0.69 K, respectively. Meanwhile, the RMSE of yearly one-factor, two-factor and three-factor RTM was reduced by 0.56 K, 0.80 K and 0.83 K, respectively. Compared with the yearly three-factor linear RTM, the mean bias and RMSE of the linear seasonal three-factor RTMs decreased by 0.06 K and 0.10 K, respectively. The precision of nonlinear seasonal three-factor RTMs is comparable to linear seasonal three-factor RTMs, but the expressions of the linear RTMs are easier to use. Therefore, linear seasonal three-factor RTMs are more suitable for calculating Tm and are recommended to use for PWV conversion in the Yangtze River Delta region.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14081909
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 3
    Online Resource
    Online Resource
    The Impact of Different Ocean Tide Loading Models on GNSS Estimated Zenith Tropospheric Delay Using Precise Point Positioning Technique
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 18 ( 2020-09-20), p. 3080-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 18 ( 2020-09-20), p. 3080-
    Abstract: 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°.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12183080
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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  • 4
    Online Resource
    Online Resource
    Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 24 ( 2020-12-15), p. 4101-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 24 ( 2020-12-15), p. 4101-
    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.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12244101
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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    Online Resource
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