Global <scp>P</scp>ositioning <scp>S</scp>ystem precipitable water vapour (<scp>GPS‐PWV</scp>) jumps before intense rain events: <scp>A</scp> potential application to nowcasting

Sapucci, Luiz Fernando; Machado, Luiz A. T.; Souza, Eniuce Menezes de; Campos, T. L.

doi:10.1002/met.1735

Cited by 45 publications

(30 citation statements)

References 50 publications

(74 reference statements)

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“…Figure 11 shows the time series of 3-min PWVs over the HKLT station and hourly precipitation at the precipitation station (co-located with the HKLT station, see Figure 1) depicted in the histogram during the four-day period of 15-18 September. Several studies have shown that precipitation occurred either when PWV reaches its peak or in the initial period after a sharp decrease [39,44,45]. However, typhoons provide abundant moisture, which is likely to cause precipitation events and accelerate the formation of saturated states of water in the region covered by typhoons.…”

Section: Relationship Between Variations In Pwv and Atmospheric Parammentioning

confidence: 99%

Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong

Zhang

et al. 2019

Remote Sensing

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Typhoons can be serious natural disasters for the sustainability and development of society. The development of a typhoon usually involves a pre-existing weather disturbance, warm tropical oceans, and a large amount of moisture. This implies that a large variation in the atmospheric water vapor over the path of a typhoon can be used to study the characteristics of the typhoon. This is the reason that the variation in precipitable water vapor (PWV) is often used to capture the signature of a typhoon in meteorology. This study investigates the usability of real-time PWV retrieved from global navigation satellite systems (GNSS) for typhoons’ characterizations, and especially, the following aspects were investigated: (1) The correlation between PWV and atmospheric parameters including pressure, temperature, precipitation, and wind speed; (2) water vapor transportation during a typhoon period; and (3) the correlation between the movement of a typhoon and the transportation of water vapor. The case study selected for this research was Super Typhoon Mangkhut that occurred in mid-September 2018 in Hong Kong. The PWV time series were obtained from a conversion of GNSS-derived zenith total delays (ZTDs) using observations at 10 stations selected from the Hong Kong GNSS continuously operating reference stations (CORS) network, which are also located along the path of the typhoon. The Bernese GNSS Software (ver. 5.2) was used to obtain the ZTDs; and the root mean square (RMS) of the differences between the GNSS-ZTDs and International GNSS Service post-processed ZTDs time series was less than 8 mm. The RMS of the differences between the GNSS-PWVs (i.e., the ZTDs converted PWVs) and radiosonde-derived PWVs (RS-PWVs) time series was less than 2 mm. The changes in PWV reflect the variation in wind speed during the typhoon period to a certain degree, and their correlation coefficient was 0.76, meaning a significant positive correlation. In addition, a new approach was proposed to estimate the direction and speed of a typhoon’s movement using the time difference of PWV arrival at different sites. The direction and speed estimated agreed well with the ones published by the China Meteorological Administration. These results suggest that GNSS-derived PWV has a great potential for the monitoring and even prediction of typhoon events, especially for near real-time warnings.

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Section: Relationship Between Variations In Pwv and Atmospheric Parammentioning

confidence: 99%

Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong

Zhang

et al. 2019

Remote Sensing

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“…This delay, known as the Zenith Total Delay, can be separated into a wet (PWV driven) and dry (non-PWV) component through the use of atmospheric models (Tralli & Lichten 1990). Although this approach is a recent advent in astronomy (Braun & Hove 2005;Dumont & Zabransky 2001;Nahmias & Zabransky 2004;Blake & Shaw 2011), the use of GPS to measure PWV has a longer history in meteorology (Bevis et al 1992) and has undergone continual improvement with a focus on minimizing the estimated uncertainty, often achieving levels 2 mm (Moore et al 2015;Shangguan et al 2015;Sapucci et al 2019).…”

Section: Precipitable Water Vapormentioning

confidence: 99%

GPS Measurements of Precipitable Water Vapor Can Improve Survey Calibration: A Demonstration from KPNO and the Mayall z-band Legacy Survey

Wood‐Vasey

Perrefort

Baker

2022

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Dual-band Global Positioning Satellite (GPS) measurements of precipitable water vapor (PWV) at the Kitt Peak National Observatory predict the overall per-image sensitivity of the Mayall z-band Legacy Survey (MzLS). The per-image variation in the brightness of individual stars is strongly correlated with the measured PWV and the color of the star. Synthetic stellar spectra through TAPAS transmission models successfully predict the observed PWV-induced photometric variation. We find that PWV absorption can be well approximated by a linear relationship with (airmass × PWV)0.6 and present an update on the traditional treatment in the literature. The MzLS zero-point sensitivity in electrons s−1 varies with a normalized-mean absolute deviation of 61 mmag. PWV variation accounts 23 mmag of this zero-point variation. The MzLS per-image absolute sensitivity decreases by 40 mmag per effective mm of PWV. The overall gray offset portion of this variation is corrected by the calibration to a reference catalog. But the relative calibration error between blue (r − z < 0.5 mag) versus red (1.2 mag < r − z) stars increases by 0.3–2 mmag per effective mm of PWV. We argue that GPS systems provide more precise PWV measurements than using differential measurements of stars of different colors and recommend that observatories install dual-band GPS as a low-maintenance, low-cost, auxiliary calibration system. We extend our results of the need for well-calibrated PWV measurements by presenting the calculations of the PWV photometric impact on three science cases of interest: stellar photometry, supernova cosmology, and quasar identification and variability.

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“…By analyzing the temporal variations in PWV time series over the Arabian Sea and the Bay of Bengal, Singh et al [45] concluded that the relatively higher PWV (around 70−90 mm) during the months of May and June is closely correlated with the onset of south-west monsoon. In addition, considerable increases in GPS-PWV (over 9.5 mm/h) before the heavy precipitation events happened in Brazil were found and regarded as "GPS-PWV jumps" by Sapucci et al [46], which are likely to be strongly linked to water vapor convergence and the continued formation of cloud droplets and raindrops. Zhang et al [47] also stated that PWV could be used to complement conventional meteorological observations for the monitoring and predictions of severe weather events.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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

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Global Positioning System precipitable water vapour (GPS‐PWV) jumps before intense rain events: A potential application to nowcasting

Cited by 45 publications

References 50 publications

Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong

Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong

GPS Measurements of Precipitable Water Vapor Can Improve Survey Calibration: A Demonstration from KPNO and the Mayall z-band Legacy Survey

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

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