A Global Model for Estimating Tropospheric Delay and Weighted Mean Temperature Developed with Atmospheric Reanalysis Data from 1979 to 2017

Sun, Zhangyu; Bao, Zhenan; Yao, Yibin

doi:10.3390/rs11161893

Cited by 66 publications

(45 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As we can see, on a global scale, GGNTm outperformed all the other two models, especially at high pressure levels. The GTrop has been proved to be considerably better than GPT3 (Sun et al, 2019), owing to its use of the T m lapse rate, although its T m results still had large errors at high pressure levels, which is most likely to result from neglecting the nonlinear vertical variation in T m . The large bias and RMSE of the GWMT_D results were possibly because its modeling was based on NCEP reanalysis data, and there may exist differences between the reanalysis P. Sun et al: A new global grid-based weighted mean temperature model Figure 6.…”

Section: Comparison With Era5 Hourly Datamentioning

confidence: 99%

“…In recent studies, some researchers used a T m lapse rate, the rate of change in T m with altitude, to correct the effect of the height element on T m , e.g., IGPT2w (Huang et al, 2019b), GTm_R (Li et al, 2020), and GPT2wh (Yang et al, 2020). The GTrop model (Sun et al, 2019), developed for predicting both ZTD and T m , also took into account the T m lapse rate, and it outperforms GPT2w obviously at altitudes under 10 km.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A new global grid-based weighted mean temperature model considering vertical nonlinear variation

Sun

Zhang

et al. 2021

Atmos. Meas. Tech.

View full text Add to dashboard Cite

Abstract. Global navigation satellite systems (GNSS) have been proved to be an excellent technology for retrieving precipitable water vapor (PWV). In GNSS meteorology, PWV at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals received at the station using a conversion factor which is a function of weighted mean temperature (Tm) along the vertical direction in the atmosphere over the site. Thus, the accuracy of Tm directly affects the quality of the GNSS-derived PWV. Currently, the Tm value at a target height level is commonly modeled using the Tm value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in Tm is neglected. This may result in large errors in the Tm result for the target height level, as the variation trend in the vertical direction of Tm may not be linear. In this research, a new global grid-based Tm empirical model with a horizontal resolution of 1∘ × 1∘ , named GGNTm, was constructed using ECMWF ERA5 monthly mean reanalysis data over the 10-year period from 2008 to 2017. A three-order polynomial function was utilized to fit the vertical nonlinear variation in Tm at the grid points, and the temporal variation in each of the four coefficients in the Tm fitting function was also modeled with the variables of the mean, annual, and semi-annual amplitudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted Tm values in 2018 to compare with the following two references in the same year: (1) Tm from ERA5 hourly reanalysis with the horizontal resolution of 5∘ × 5∘; (2) Tm from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model-predicted Tm values over all global grid points at the 950 and 500 hPa pressure levels were 3.35 and 3.94 K, respectively. Compared to the second reference, the mean bias and mean RMSE of the model-predicted Tm values over the 428 radiosonde stations at the surface level were 0.34 and 3.89 K, respectively; the mean bias and mean RMSE of the model's Tm values over all pressure levels in the height range from the surface to 10 km altitude were −0.16 and 4.20 K, respectively. The new model results were also compared with that of the GTrop and GWMT_D models in which different height correction methods were also applied. Results indicated that significant improvements made by the new model were at high-altitude pressure levels; in all five height ranges, GGNTm results were generally unbiased, and their accuracy varied little with height. The improvement in PWV brought by GGNTm was also evaluated. These results suggest that considering the vertical nonlinear variation in Tm and the temporal variation in the coefficients of the Tm model can significantly improve the accuracy of model-predicted Tm for a GNSS receiver that is located anywhere below the tropopause (assumed to be 10 km), which has significance for applications requiring real-time or near real-time PWV converted from GNSS signals.

show abstract

Section: Comparison With Era5 Hourly Datamentioning

confidence: 99%

mentioning

confidence: 99%

A new global grid-based weighted mean temperature model considering vertical nonlinear variation

Sun

Zhang

et al. 2021

Atmos. Meas. Tech.

View full text Add to dashboard Cite

show abstract

“…The reanalysis data generated by NWMs are usually used as the data source in the development of NMTm models, and one of the most representative NMTm models is the Global Pressure and Temperature 2 wet (GPT2w) model, which is an empirical model providing empirical values of various meteorological elements near the Earth's surface including T m [17]. In recent years, the research focus of NMTm models has shifted to the study on the impact of height differences on the model accuracy, and a series of NMTm models such as the GTm_R model [18], IGPT2w model [19,20], GWMT-D model [11] and GTrop-Tm model [21] have been developed. The application scope of NMTm models has even been extended from the ground to the tropopause [22].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

et al. 2021

View full text Add to dashboard Cite

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

show abstract

“…(Huang et al, 2019b), GTm_R (Li et al, 2020) and GPT2wh (Yang et al, 2020). The GTrop model (Sun et al, 2019), developed for predicting both ZTD and , also took into account the lapse-rate, and it outperforms GPT2w obviously at altitudes under 10 km.…”

Section: Introductionmentioning

confidence: 99%

A new global grid-based weighted mean temperature model considering vertical nonlinear variation

Sun¹,

Wu²,

Zhang³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Global Navigation Satellite Systems (GNSS) have been proved to be an excellent technology for retrieving precipitable water vapor (PWV). In GNSS meteorology, PWV at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals received at the station using a conversion factor which is a function of weighted mean temperature (Tm) along the vertical direction in the atmosphere over the site. Thus, the accuracy of Tm directly affects the quality of the GNSS-derived PWV. Currently, the Tm value at a target height level is commonly modelled using the Tm value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in Tm is neglected. This may result in large errors in the Tm result for the target height level, as the variation trend in the vertical direction of Tm may not be linear. In this research, a new global grid-based Tm empirical model with a horizontal resolution of 1°×1°, named GGNTm, was constructed using ECMWF ERA5 monthly mean reanalysis data over the 10-year period from 2008 to 2017. A three-order polynomial function was utilized to fit the vertical nonlinear variation in Tm at the grid points, and the temporal variation in each of the four coefficients in the Tm fitting function was also modelled with the variables of the mean, annual and semi-annual amplitudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted Tm values in 2018 to compare with the following two references in the same year 1) Tm from ERA5 hourly reanalysis with the horizontal resolution of 5°×5°; 2) Tm from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model predicted Tm values over all global grid points at the 950 hPa and 500 hPa pressure levels were 3.35 K and 3.94 K respectively. Compared to the second reference, the mean bias and mean RMSE of the model predicted Tm values over the 428 radiosonde stations at the surface level were 0.34 K and 3.89 K respectively; the mean bias and mean RMSE of the model’s Tm values at all pressure levels in the height range from the surface to 10 km altitude were −0.16 K and 4.20 K respectively. The new model results were also compared with that of the GPT3, GTrop and GWMT_D models in which different height correction methods were also applied. Results indicated that significant improvements made by the new model were at high-altitude pressure levels; in all five height ranges, GGNTm results were generally unbiased, and their accuracy varied little with height. The impact of Tm on GNSS-PWV was evaluated in terms of relative error, and significant improvement was found compared to the widely used GPT3 model. These results suggest that considering the vertical nonlinear variation in Tm and the temporal variation in the coefficients of the Tm model can significantly improve the accuracy of model-predicted Tm for a GNSS receiver that is located in anywhere below the tropopause (assumed to be 10 km), which has significance for applications needing real-time or near real-time PWV converted from GNSS signals.

show abstract

A Global Model for Estimating Tropospheric Delay and Weighted Mean Temperature Developed with Atmospheric Reanalysis Data from 1979 to 2017

Cited by 66 publications

References 37 publications

A new global grid-based weighted mean temperature model considering vertical nonlinear variation

A new global grid-based weighted mean temperature model considering vertical nonlinear variation

Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

A new global grid-based weighted mean temperature model considering vertical nonlinear variation

Contact Info

Product

Resources

About