GPS meteorology in a low-latitude region: Remote sensing of atmospheric water vapor over the Malaysian Peninsula

Musa, Tajul Ariffin; Amir, S.; Othman, Razib M.; Ses, Sahrum; Omar, Kamaludin Mohd; Abdullah, Khairul Anuar; Lim, Samsung; Rizos, Chris

doi:10.1016/j.jastp.2011.08.014

Cited by 43 publications

(34 citation statements)

References 18 publications

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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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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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“…These GPS CORS (i.e., Pulau Pinang (USMP), Banting (BTNG), Geting (GETI) and Pekan (PEKN)) are part of the Malaysian RealTime Kinematic network (MyRTKnet) which is being maintained by the Department of Survey & Mapping Malaysia (DSMM). Only GPS data in year 2008 is available for this study and the ZPD parameter from these stations was estimated according to Musa et al, (2011). Figure 1 shows the spatial location of the radiosonde stations and the GPS CORS, while Table 2 lists the site coordinates of each radiosonde station and the GPS CORS respectively.…”

Section: Data Set: Radiosonde and Gps Stationmentioning

confidence: 99%

“…A commonly used method to estimate the T M parameter; (1) to apply a regression function from the relationship of surface air temperature (TS) as described by Musa et al, (2011) and Liou et al, (2001), (2) reanalyses the output from numerical weather prediction model such as Jin et al, (2009) and Heise et al, (2009), (3) estimate by using the Global Pressure and Temperature (GPT) model developed by Boehm et al, (2007) (Yao et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Wayan et al, (2013) The Global 1 model has been developed by using radiosonde data covering the America region over 2 year period (1990-1991). The Global 1 model has been widely utilized, especially in GPS-derived IWV such as utilized by Jin et al (2008); Musa et al, (2011);Xu et al (2012). However, the Global 1 does not represent the actual climatic condition worldwide.…”

Section: Introductionmentioning

confidence: 99%

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Water Vapour Weighted Mean Temperature Model for GPS-Derived Integrated Water Vapour in Peninsular Malaysia

Musa

Mazlan

Opaluwa

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents the development of T M model by using the radiosonde stations from Peninsular Malaysia. Two types of T M model were developed; site-specific and regional models. The result revealed that the estimation from site-specific model has small improvement compared to the regional model, indicating that the regional model is adequately to use in estimation of GPS-derived IWV over Peninsular Malaysia. Meanwhile, this study found that the diurnal cycle of T S has influenced the T M -T S relationship. The separation between daytime and nighttime observation can improve the relationship of T M -T S . However, the impact of diurnal cycle to IWV estimation is less than 1%. The T M model from Global and Tropic also been evaluated. The Tropic T M model is superior to be utilized as compared to the Global T M model.

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“…However, a large number of GNSS stations have been deployed for positioning purposes and not equipped with collocated meteorological sensors. In this case, one may use pressure derived from a global atmospheric reanalysis (Dee et al, 2011;Zhang et al, 2017) or interpolated from nearby 20 meteorological observations (Alshawaf et al, 2015;Musa et al, 2011;Wang et al, 2007) or predicted by a blind model (Böhm et al, 2015;Wang et al, 2017). For , since the temperature and humidity profiles are very difficult to obtain, particularly in a near-real-time mode, has to be calculated from a model.…”

mentioning

confidence: 99%

Constructing Precipitable Water Vapor Map from Regional GNSS Network Observations without Collocated Meteorological Data

Chen

Dai

Liu

et al. 2018

Preprint

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Abstract. Surface pressure ( ) and weighted mean temperature ( ) are two necessary variables for the accurate retrieval of precipitable water vapor (PWV) from global navigation satellite system (GNSS) data. The lack of or information is a concern for those GNSS sites that are not collocated with meteorological sensors. This paper investigates an alternative method of inferring accurate and at the GNSS station using nearby synoptic observations. and obtained at the 15 nearby synoptic sites are interpolated onto the location of GNSS station by performing both vertical and horizontal adjustments, in which the parameters involved in and calculation are estimated from ERA-Interim reanalysis profiles.In addition, we present a method of constructing high quality PWV map through vertical reduction and horizontal interpolation of the retrieved GNSS PWVs. To evaluate the performances of the and retrieval and the PWV map construction, GNSS data collected from 58 stations of the Hunan GNSS network and synoptic observations from 20 nearby 20 sites in 2015 were processed to extract the PWV so as to subsequently generate PWV map. The retrieved and and constructed PWV maps were assessed by the results derived from radiosonde and ERA-Interim reanalysis. The results show that (1) accuracies of and derived by synoptic interpolation are within the range of 1.7-3.0 hPa and 2.5-3.0 K, respectively, which are much better than the GPT2w model; (2) the constructed PWV maps have good agreements with radiosonde and ERA reanalysis data with overall accuracy better than 3 mm; and (3) PWV maps can well reveal the moisture 25 advection, transportation and convergence during heavy rainfall.

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GPS meteorology in a low-latitude region: Remote sensing of atmospheric water vapor over the Malaysian Peninsula

Cited by 43 publications

References 18 publications

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

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

Water Vapour Weighted Mean Temperature Model for GPS-Derived Integrated Water Vapour in Peninsular Malaysia

Constructing Precipitable Water Vapor Map from Regional GNSS Network Observations without Collocated Meteorological Data

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