Development of an improved empirical model for slant delays in the troposphere (GPT2w)

Boehm, J.; Möller, Gregor; Schindelegger, Michael; Pain, Gregory; Weber, Robert L.

doi:10.1007/s10291-014-0403-7

Cited by 416 publications

(294 citation statements)

References 16 publications

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“…Lagler et al, (2013) 15 significantly improved the GPT model, especially for its spatial and temporal variability, and named this new version as GPT2. An extension version called GPT2w was developed by Böhm et al, (2015) with improved capability to determine ZWD in blind mode. Besides the pressure and temperature, the refined GPT2w model also provides various parameters such as water vapor pressure, weighted mean temperature and temperature lapse rate.…”

Section: Gpt2w Modelmentioning

confidence: 99%

“…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.…”

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confidence: 99%

“…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. An empirical model dependent on surface temperature ( ) (Bevis et al, 1994) or a blind model developed from atmospheric reanalysis products (Böhm et al, 2015;Yao et al, 2013;Zhang et al, 2017) is often employed. 25…”

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confidence: 99%

“…The use of blind models is a very convenient means, however, most blind models 30 (e.g. Global Pressure and Temperature 2 wet, GPT2w; (Böhm et al, 2015)) are developed at a global scale and not likely to capture regional small-scale variations. More accurate and could be achieved by interpolation from nearby meteorological observations if they can be accessed simultaneously.…”

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confidence: 99%

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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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Section: Gpt2w Modelmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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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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“…(2) Several global empirical models of Tm are established based on analyses of Tm time series from NWP datasets or other sources (Chen et al, 2014;Yao et al, 2012;Bohm et al, 2015). Tm at any time and any location can be estimated from these models independent of real meteorological observations.…”

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confidence: 99%

Development of time-varying global gridded Ts-Tm model for precise GPS-PWV retrieval

Jiang¹,

Ye²,

Lu³

et al. 2018

Preprint

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Water-vapor-weighted mean temperature, Tm, is the key variable to estimate mapping factor between GPS zenith wet delay (ZWD) and precipitable water vapor (PWV). In near real-time GPS-PWV retrieving, estimating Tm from surface air 10 temperature Ts is a widely used method because of its high temporal resolution and a fair degree of accuracy. Based on the Tm estimates and the extracted Ts parameters at each reanalysis grid node, analyses of the relationship between Tm and Ts were performed without smoothing of data which will produce superior results than other similar studies. Analyses demonstrate that

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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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Development of an improved empirical model for slant delays in the troposphere (GPT2w)

Cited by 416 publications

References 16 publications

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

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

Development of time-varying global gridded Ts-Tm model for precise GPS-PWV retrieval

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

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