The Concept of Operational Near Real-Time GNSS Meteorology System for Atmospheric Water Vapour Monitoring over Peninsular Malaysia

Norazmi, Muhammad Faiz; Opaluwa, Yusuf Drisu; Musa, Tajul Ariffin; Othman, Razib M.

doi:10.1007/s13369-014-1481-0

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…Elgered et al . [] adopted the following empirical model to obtain the ZHD (in millimeters), which has been widely used in many studies [ Bevis et al ., ; Bock and Doerflinger , ; Bokoye et al ., ; Kleijer , ; Musa et al ., ; Kumar et al ., ; Antonini et al ., ; Norazmi et al ., ].

normalZ normalH normalD = ((), 2.779 \pm 0.0024) P_{s} true/ f ((),, φ, H),

where P s is the total pressure (in millibar) at the station's height (in kilometers), and

f ((),, φ, H) = 1 - 0.0026 \cdot normalcos ((), 2 φ) - 0.00028 H

accounts for the variation in the gravitational acceleration at the station with the latitude φ and the height H above the ellipsoid.…”

Section: Data Set and Methodology For Pwv Determination And Analysismentioning

confidence: 99%

“…It can be obtained by subtracting the ZHD from GNSS-derived ZTD. Journal of Geophysical Research: Atmospheres 10.1002/2015JD024181 Elgered et al [1991] adopted the following empirical model to obtain the ZHD (in millimeters), which has been widely used in many studies [Bevis et al, 1992;Bock and Doerflinger, 2001;Bokoye et al, 2003;Kleijer, 2004;Musa et al, 2011;Kumar et al, 2013;Antonini et al, 2014;Norazmi et al, 2015].…”

Section: Data Set and Methodology For Pwv Determinationmentioning

confidence: 99%

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Water vapor‐weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend

et al. 2016

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Water vapor‐weighted mean temperature, Tm, is a vital parameter for retrieving precipitable water vapor (PWV) from the zenith wet delay (ZWD) of Global Navigation Satellite Systems (GNSS) signal propagation. In this study, the Tm at 368 GNSS stations for 2000–2012 were calculated using three methods: (1) temperature and humidity profiles from ERA‐Interim, (2) the Bevis Tm–Ts relationship, and (3) the Global Pressure and Temperature 2 wet model. Tm derived from the first method was used as a reference to assess the errors of the other two methods. Comparisons show that the relative errors of the Tm derived from these two methods are in the range of 1–3% across more than 95% of all the stations. The PWVs were calculated using the aforementioned three types of Tm and the GNSS‐derived ZWD at 107 stations. Again, the PWVs calculated using Tm from the first method were used as the reference of the other two PWVs. The root‐mean‐square errors of these two PWVs are both in the range of 0.1–0.7 mm. The second method is recommended in real‐time applications, since its performance is slightly better than the third method. In addition, the linear trends of the PWV time series from the first method were also used as the reference to evaluate the trends from the other two methods. Results show that 13% and 23% of the PWV trends from the respective second and third methods have a relative error of larger than 10%. For climate change studies, the first method, if available, is always recommended.

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normalZ normalH normalD = ((), 2.779 \pm 0.0024) P_{s} true/ f ((),, φ, H),

where P s is the total pressure (in millibar) at the station's height (in kilometers), and

f ((),, φ, H) = 1 - 0.0026 \cdot normalcos ((), 2 φ) - 0.00028 H

accounts for the variation in the gravitational acceleration at the station with the latitude φ and the height H above the ellipsoid.…”

Section: Data Set and Methodology For Pwv Determination And Analysismentioning

confidence: 99%

Section: Data Set and Methodology For Pwv Determinationmentioning

confidence: 99%

Water vapor‐weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend

et al. 2016

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“…These formulae have been widely used in many studies (Bevis et al, 1992;Bock and Doerflinger, 2001;Bokoye et al, 2003;Kleijer, 2004;Musa et al, 2011;Kumar et al, 2013;Norazmi et al, 2015).…”

Section: Computation Of Zhd and Iwvmentioning

confidence: 99%

Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Wang

Zhang

et al. 2017

Atmos. Meas. Tech.

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Abstract. Surface pressure is a necessary meteorological variable for the accurate determination of integrated water vapor (IWV) using Global Navigation Satellite System (GNSS). The lack of pressure observations is a big issue for the conversion of historical GNSS observations, which is a relatively new area of GNSS applications in climatology. Hence the use of the surface pressure derived from either a blind model (e.g., Global Pressure and Temperature 2 wet, GPT2w) or a global atmospheric reanalysis (e.g., ERAInterim) becomes an important alternative solution. In this study, pressure derived from these two methods is compared against the pressure observed at 108 global GNSS stations at four epochs (00:00, 06:00, 12:00 and 18:00 UTC) each day for the period 2000-2013. Results show that a good accuracy is achieved from the GPT2w-derived pressure in the latitude band between −30 and 30 • and the average value of 6 h root-mean-square errors (RMSEs) across all the stations in this region is 2.5 hPa. Correspondingly, an error of 5.8 mm and 0.9 kg m −2 in its resultant zenith hydrostatic delay (ZHD) and IWV is expected. However, for the stations located in the mid-latitude bands between −30 and −60 • and between 30 and 60 • , the mean value of the RMSEs is 7.3 hPa, and for the stations located in the high-latitude bands from −60 to −90 • and from 60 to 90 • , the mean value of the RMSEs is 9.9 hPa. The mean of the RMSEs of the ERAInterim-derived pressure across at the selected 100 stations is 0.9 hPa, which will lead to an equivalent error of 2.1 mm and 0.3 kg m −2 in the ZHD and IWV, respectively, determined from this ERA-Interim-derived pressure. Results also show that the monthly IWV determined using pressure from ERAInterim has a good accuracy − with a relative error of better than 3 % on a global scale; thus, the monthly IWV resulting from ERA-Interim-derived pressure has the potential to be used for climate studies, whilst the monthly IWV resulting from GPT2w-derived pressure has a relative error of 6.7 % in the mid-latitude regions and even reaches 20.8 % in the highlatitude regions. The comparison between GPT2w and seasonal models of pressure-ZHD derived from ERA-Interim and pressure observations indicates that GPT2w captures the seasonal variations in pressure-ZHD very well.

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“…The extensive presence of GNSS sensors in Italy and the availability of their data represent a strong basis for the usage of this technique for meteorological applications. Considering the advantages provided by GNSS post-processed data reported in the scientific literature for various meteorological analyses [32,36,[53][54][55][56][57], the transition to NRT (near real-time) GNSS meteorology operational systems [46] is crucial for providing information useful to predict rapidly evolving weather events [58][59][60]. Capturing atmospheric water vapor with high spatio-temporal resolution [59], GNSS-NRT data can be extremely helpful in the case of early warning systems related to severe weather events, in order to mitigate risk by providing timely warning of the hazard and thus giving useful information to limit damages.…”

Section: Gnss and Atmospheric Soundingmentioning

confidence: 99%

Multi-Sensor Data Analysis of an Intense Weather Event: The July 2021 Lake Como Case Study

Mascitelli

Petracca

Puca

et al. 2022

Water

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A comprehensive analysis of the July 2021 event that occurred on Lake Como (Italy), during which heavy hailstorms and floods affected the surroundings of Lake, is presented. The study provides a detailed analysis of the event using different observation sources currently available. The employed techniques include both conventional (rain gauges, radar, atmospheric sounding) and non-conventional (satellite-based Earth observation products, GNSS, and lightning detection network) observations for hydro-meteorological analysis. The study is split in three main topics: event description by satellite-based observations; long-term analysis by the ERA5 model and ASCAT soil water index; and short-term analysis by lightning data, GNSS delays and radar-VIL. The added value of the work is the near-real-time analysis of some of the datasets used, which opens up the potential for use in alerting systems, showing considerable application possibilities in NWP modeling, where it can also be useful for the implementation of early warning systems. The results highlight the validity of the different techniques and the consistency among the observations. This result, therefore, leads to the conclusion that a joint use of the innovative techniques with the operational ones can bring reliability in the description of events.

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The Concept of Operational Near Real-Time GNSS Meteorology System for Atmospheric Water Vapour Monitoring over Peninsular Malaysia

Cited by 4 publications

References 20 publications

Water vapor‐weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend

Water vapor‐weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend

Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Multi-Sensor Data Analysis of an Intense Weather Event: The July 2021 Lake Como Case Study

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