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

Jiang, Peng; Ye, Shirong; Lu, Yonglin; Liu, Yanyan; Chen, Dezhong; Wu, Yue

doi:10.5194/amt-2018-67

Cited by 4 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An example is the ECMWF Re-Analysis-Interim (ERA-Interim) dataset, which is a modern reanalysis product produced by the ECMWF using the assimilation system. The majority of the data used to produce ERA-Interim are from satellites, although radiosonde data have also been assimilated into it [28,35]. However, a test at 20 GNSS sites showed that the root-mean-square error between the surface temperature calculated by ERA-Interim and the measured surface temperature reached 2.0 K, while another test at 20 radiosonde stations showed that the relative error of T m calculated from ERA-Interim compared with T m calculated by radiosonde data reached 0.5% [36].…”

Section: T M Determined By the Numerical Integration Methodsmentioning

confidence: 99%

“…Bevis et al [19] first specified the formula for calculating T m as T m = 70.2 + 0.72 • T s with 8718 radiosonde profiles of 13 stations distributed in North America. However, it was found that the coefficients of the Bevis formula vary with the geographical location and season [27][28][29], so it is necessary to estimate the coefficients through measurements in a specific region and period of time. Li et al [29] established regional T m models for the Hunan region, China, including models with one meteorological factor (T m -T s ), two mete-orological factors (T m -T s , e s ) and three meteorological factors (T m -T s , e s , P s ).…”

Section: Introductionmentioning

confidence: 99%

“…They further took the seasonal and geographic variation characteristics of the T m -T s and T m -T s , e s relations into account to develop the GTm-I model and PTm-I model, the accuracy of which were greatly improved compared with the GTm model and PTm model, respectively, on a global scale. Jiang et al [28] developed a time-varying grid global T m -T s model (TVGG) considering the significant spatiotemporal variation characteristics of the T m -T s relation. However, the relations between T m and surface meteorological elements are very complex [30] and are difficult to simulate sufficiently with a simple linear equation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

Long

Dong

et al. 2021

Atmosphere

View full text Add to dashboard Cite

The weighted mean temperature (Tm) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. Tm can be calculated via numerical integration with the atmospheric profile data measured along the zenith direction, but this method is not practical in most cases because it is not easy for general users to get real-time atmospheric profile data. An alternative method to obtain an accurate Tm value is to establish regional or global models on the basis of its relations with surface meteorological elements as well as the spatiotemporal variation characteristics of Tm. In this study, the complex relations between Tm and some of its essentially associated factors including the geographic position and terrain, surface temperature and surface water vapor pressure were considered to develop Tm models, and then a non-meteorological-factor Tm model (NMFTm), a single-meteorological-factor Tm model (SMFTm) and a multi-meteorological-factor Tm model (MMFTm) applicable to China and adjacent areas were established by adopting the artificial neural network technique. The generalization performance of new models was strengthened with the help of an ensemble learning method, and the model accuracies were compared with several representative published Tm models from different perspectives. The results show that the new models all exhibit consistently better performance than the competing models under the same application conditions tested by the data within the study area. The NMFTm model is superior to the latest non-meteorological model and has the advantages of simplicity and utility. Both the SMFTm model and MMFTm model show higher accuracy than all the published Tm models listed in this study; in particular, the MMFTm model is about 14.5% superior to the first-generation neural network-based Tm (NN-I) model, with the best accuracy so far in terms of the root-mean-square error.

show abstract

Section: T M Determined By the Numerical Integration Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

Long

Dong

et al. 2021

Atmosphere

View full text Add to dashboard Cite

show abstract

“…(2019) is adopted to calculate the

T_{m}

. The authors verified its prominent advantages over other global models, such as Bevis and GPT2w (Jiang et al., 2019). In the present study, Tm_ERA5 model , Tm_FNL model , and Tm_GFS model represent the schemes of calculating

T_{m}

from the

T_{s} - T_{m}

model using surface temperature data provided by the ERA5, FNL, and GFS, respectively.…”

Section: Resultsmentioning

confidence: 87%

“…For the second approach, the time‐varying global gridded

T_{s} - T_{m}

model developed by Jiang et al. (2019) is adopted to calculate the

T_{m}

. The authors verified its prominent advantages over other global models, such as Bevis and GPT2w (Jiang et al., 2019).…”

Section: Resultsmentioning

confidence: 92%

A Global Assessment of Precipitable Water Vapor Derived From GNSS Zenith Tropospheric Delays With ERA5, NCEP FNL, and NCEP GFS Products

Chen

Wang

et al. 2021

Earth and Space Science

View full text Add to dashboard Cite

 The potentials of and from the ERA5, NCEP FNL, and NCEP GFS products for GNSS PWV retrieval are examined on a global scale  PWV retrievals using ERA5 profiles outperform those using NCEP FNL and GFS data with overall accuracies ranging from 1.6 to 2.0 mm  PWVs derived from the NWP products perform better in low latitudes but are less accurate in winter months for both hemispheres Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

show abstract

Handling Method for Outages of IGS Real-Time Service (RTS) in GNSS Real-Time Sensing of Atmospheric Water Vapor

Li,

Kang

2023

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

View full text Add to dashboard Cite

The continuity and accuracy of real-time (RT) global satellite navigation system (GNSS) sensing of atmospheric water vapor can be seriously affected by a lack of connectivity. To mitigate this issue, a method has been developed that utilizes the International GNSS Service (IGS) ultra-rapid (IGU) satellite orbit and an established RT service (RTS) for satellite clock correction. This method ensures the accuracy of RT atmospheric water vapor estimation even when communication is interrupted. The established RTS utilizes variations in historical clock correction data from the user side to extrapolate its RT high-precision value. Through experimentation, it has been determined that using a quadratic polynomial and an eighth-order harmonic-based function along with 1-hour historical clock correction data is optimal for establishing this RTS method. The 2D and 3D accuracies of static Precise Point Positioning (PPP) can achieve centimeter to millimeter levels, and the estimated average Root Mean Square (RMS) of zenith tropospheric delay (ZTD) is equivalent to the RT archived products. When compared to the precipitable water vapor (PWV) results obtained from IGS ZTD products, the averaged RMS values of GPS, Galileo, and BDS-3 PWV reach 3.12 mm, 4.41 mm, and 6.43 mm, respectively, which can meet the accuracy requirements of general numerical weather forecasting and other applications during communication interruptions.

show abstract

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

Cited by 4 publications

References 27 publications

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

A Global Assessment of Precipitable Water Vapor Derived From GNSS Zenith Tropospheric Delays With ERA5, NCEP FNL, and NCEP GFS Products

Handling Method for Outages of IGS Real-Time Service (RTS) in GNSS Real-Time Sensing of Atmospheric Water Vapor

Contact Info

Product

Resources

About