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

Wang, Xiaoming; Zhang, Kefei; Wu, Suqin; Fan, Saijun; Cheng, Yingyan

doi:10.1002/2015jd024181

Cited by 145 publications

(133 citation statements)

References 80 publications

(146 reference statements)

Supporting

Mentioning

120

Contrasting

Order By: Relevance

“…This research is part of our continuous effort to extend our research presented in Wang et al (2016a) by investigating the alternative methods for the determination of ZHD and developing of a new global long-term IWV time series, which is critical for an improved understanding of climate change using the state of the art GNSS technology. In this study, the accuracies of the ERA-Interim-derived surface pressure (calculated from the nearest grid point and interpolated from four surrounding grid points) and GPT2w-derived 6 h pressure have been investigated by comparing them against the observed pressure for 108 stations during the period 2000-2013.…”

Section: Discussionmentioning

confidence: 99%

“…More details about the computation of have been published in Wang et al (2016a), and here only a brief description of the calculation of is given. As a function of T m , can be calculated using…”

Section: Computation Of Zhd and Iwvmentioning

confidence: 99%

“…The GNSS-derived IWV over a station can then be obtained by multiplying the ZWD with a conversion factor which is a function of the water-vapor-weighted mean temperature T m over the station. The T m can be determined using one of the following three methods: (1) temperature and humidity profiles from either radiosonde observations or atmospheric reanalysis datasets (Wang et al, 2005(Wang et al, , 2016a; (2) the relationship between surface temperature T s and the water-vapor-weighted mean temperature T m (Bevis et al, 1992(Bevis et al, , 1994Ross and Rosenfeld, 1997); and (3) a blind model developed from atmospheric reanalysis products (Yao et al, 2012(Yao et al, , 2015Böhm et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by our early research (Wang et al, 2016a), it is vital to assess the performance of different methods for determining the ZHD on a global scale, which is essential in the development of a reliable global long-term IWV time series for climate studies. Although the ZHD can be accurately obtained from surface pressure observations, few GNSS stations were installed with meteorological sensors back in the 1990s since these stations were established mainly for precise positioning and navigation applications.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Wang

Zhang

et al. 2017

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Computation Of Zhd and Iwvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wang

Zhang

et al. 2017

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus the accuracy of this parameter introduces an error in the trend estimates (Nilsson and Elgered, 2008). Recent extensive studies by (Wang, 2016) have demonstrated that depending on the choice of the source of the water vapour mean temperature parameter, the IWV trend shows a relative error larger than 10 %. Thus, for consistency ZTD should be converted to IWV once it has been assimilated and we argue that this would also be the best way forward for climate models.…”

mentioning

confidence: 99%

Noise characteristics in Zenith Total Delay from homogeneously reprocessed GPS time series

Kłos

Hunegnaw

Teferle

et al. 2016

Preprint

View full text Add to dashboard Cite

Abstract. Zenith Total Delay (ZTD) time series, derived from the re-processing of Global Positioning System (GPS) data, provide valuable information for the evaluation of global atmospheric reanalysis products such as ERA-Interim. Identifying 10 the correct noise characteristics in the ZTD time series is an important step to assess the 'true' magnitude of ZTD trend uncertainties. The ZTD residual time series for 1995-2015 are generated from our homogeneously re-processed and homogenized GPS time series from over 700 globally distributed stations classified into five major climate zones. The annual peak of ZTD data ranges between 10 and 150 mm with the smallest values for the polar and Alpine zone. The amplitudes of daily curve fall between 0 and 12 mm with the greatest variations for the dry zone. The autoregressive process 15 of fourth order plus white noise model were found to be optimal for ZTD series. The tropical zone has the largest amplitude of autoregressive noise (9.59 mm) and the greatest amplitudes of white noise (13.00 mm). All climate zones have similar median coefficients of AR(1) (0.80±0.05) with a minimum for polar and Alpine, which has the highest coefficients of AR (2) (0.27±0.01) and AR(3) (0.11±0.01) and clearly different from the other zones considered. We show that 53 of 120 examined trends became insignificant, when the optimum noise model was employed, compared to 11 insignificant trends for pure 20 white noise. The uncertainty of the ZTD trends may be underestimated by a factor of 3 to 12 compared to the white noise only assumption.

show abstract

GPS PPP‐derived precipitable water vapor retrieval based on T_m/P_s from multiple sources of meteorological data sets in China

Zhang

Yuan

et al. 2017

JGR Atmospheres

View full text Add to dashboard Cite

Weighted mean temperature (Tm) and pressure (Ps) are two parameters of great relevance to precipitable water vapor (PWV) retrieval from global positioning system (GPS) data. However, information about the Tm and Ps cannot be available for those GPS stations that are not colocated with meteorological sensors. To investigate the optimal GPS‐PWV retrieval method for China, two enhanced Tm models, GM‐Tm (temperature dependent) and GH‐Tm (temperature independent), are developed. Additionally, the potentials of the Ps data from the two reanalysis data sets, the National Centers for Environmental Prediction (NCEP)‐Department of Energy (DOE) Reanalysis II (NCEP II) and ERA‐Interim, and from the empirical model GPT2w for GPS‐PWV retrieval are investigated over China. To evaluate the performances of multisources Tm and Ps data for GPS‐PWV retrieval, GPS data (2011–2013) collected from 22 stations of the Crustal Movement Observation Network of China (CMONOC) were processed by using the precise point positioning (PPP) technique, estimating the zenith tropospheric delay (ZTD) so as to be subsequently converted to GPS‐PWV. The retrieved GPS‐PWVs are compared with their counterparts derived from NCEP II and radiosonde data over China. The results show that (1) the GM‐Tm model consistently shows the highest accuracy (with root mean square error of 2.3 K), and the GH‐Tm model should be selected when temperature observations are not available, and that (2) the performances of Ps from NCEP II and ERA‐Interim differ marginally for GPS‐PWV retrieval, and significant seasonal variations are found in the agreement between the GPS‐PWVs and the PWVs derived from NCEP II and radiosonde data over China.

show abstract

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

Cited by 145 publications

References 80 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

Noise characteristics in Zenith Total Delay from homogeneously reprocessed GPS time series

GPS PPP‐derived precipitable water vapor retrieval based on T_m/P_s from multiple sources of meteorological data sets in China

Contact Info

Product

Resources

About

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

Cited by 145 publications

References 80 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

Noise characteristics in Zenith Total Delay from homogeneously reprocessed GPS time series

GPS PPP‐derived precipitable water vapor retrieval based on Tm/Ps from multiple sources of meteorological data sets in China

Contact Info

Product

Resources

About

GPS PPP‐derived precipitable water vapor retrieval based on T_m/P_s from multiple sources of meteorological data sets in China