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

2021 ◽  
Vol 14 (3) ◽  
pp. 2529-2542
Author(s):  
Peng Sun ◽  
Suqin Wu ◽  
Kefei Zhang ◽  
Moufeng Wan ◽  
Ren Wang
Keyword(s):  
Vertical Direction ◽  
Horizontal Resolution ◽  
Target Height ◽  
Weighted Mean ◽  
New Model ◽  
Nonlinear Variation ◽  
Weighted Mean Temperature ◽  
The Mean ◽  
Grid Points ◽  
Global Grid

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.

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

2020 ◽  
Author(s):  
Peng Sun ◽  
Suqin Wu ◽  
Kefei Zhang ◽  
Moufeng Wan ◽  
Ren Wang
Keyword(s):  
Vertical Direction ◽  
Horizontal Resolution ◽  
Target Height ◽  
Weighted Mean ◽  
New Model ◽  
Nonlinear Variation ◽  
Weighted Mean Temperature ◽  
The Mean ◽  
Grid Points ◽  
Global Grid

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.

A comparative study of self-propelled and towed wakes in a stratified fluid

2010 ◽  
Vol 652 ◽  
pp. 373-404 ◽  
Author(s):  
KYLE A. BRUCKER ◽  
SUTANU SARKAR
Keyword(s):  
Energy Transfer ◽  
Vertical Direction ◽  
Stratified Fluid ◽  
The Self ◽  
Turbulence Energy ◽  
Mean Velocity ◽  
Dominant Term ◽  
Turbulent Dissipation ◽  
The Mean ◽  
Grid Points

Direct numerical simulations (DNS) of axisymmetric wakes with canonical towed and self-propelled velocity profiles are performed atRe= 50 000 on a grid with approximately 2 billion grid points. The present study focuses on a comparison between towed and self-propelled wakes and on the elucidation of buoyancy effects. The development of the wake is characterized by the evolution of maxima, area integrals and spatial distributions of mean and turbulence statistics. Transport equations for mean and turbulent energies are utilized to help understand the observations. The mean velocity in the self-propelled wake decays more rapidly than the towed case due to higher shear and consequently a faster rate of energy transfer to turbulence. Buoyancy allows a wake to survive longer in a stratified fluid by reducing the 〈u1′u3′〉 correlation responsible for the mean-to-turbulence energy transfer in the vertical direction. This buoyancy effect is especially important in the self-propelled case because it allows regions of positive and negative momentum to become decoupled in the vertical direction and decay with different rates. The vertical wake thickness is found to be larger in self-propelled wakes. The role of internal waves in the energetics is determined and it is found that, later in the evolution, they can become a dominant term in the balance of turbulent kinetic energy. The non-equilibrium stage, known to exist for towed wakes, is also shown to exist for self-propelled wakes. Both the towed and self-propelled wakes, atRe= 50000, are found to exhibit a time span when, although the turbulence is strongly stratified as indicated by small Froude number, the turbulent dissipation rate decays according to inertial scaling.

scholarly journals ATMOSPHERIC WEIGHTED MEAN TEMPERATURE MODEL IN GUILIN

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1155-1160
Author(s):  
Z. X. Mo ◽  
L. K. Huang ◽  
H. Peng ◽  
L. L. Liu ◽  
C. L. Kang
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Weighted Mean ◽  
New Model ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Radiosonde Station ◽  
Variation Characteristics ◽  
Tm Model ◽  
The Impact

Abstract. Atmospheric water vapor is an important part of the earth's atmosphere, and it has a great relationship with the formation of precipitation and climate change. In CNSS-derived precipitable water vapor (PWV), atmospheric weighted mean temperature, Tm, is the key factor in the progress of retrieving PWV. In this study, using the profiles of Guilin radiosonde station in 2017, the spatiotemporal variation characteristics and relationships between Tm and surface temperature (Ts) are analyzed in Guilin, an empirical Tm model suitable for Guilin is constructed by regression analysis. Comparing the Tm values calculated from Bevis model, Li Jianguo model and new model, it is found that the root mean square error (RMSE) of new model is 2.349 K, which are decreased by 14% and 19%, respectively. Investigating the impact of different Tm models on GNSS-PWV, the Tm-induced error from new model has a smaller impact on PWV than other two models. The results show that the new Tm model in Guilin has a relatively good performance and it can improve the reliability of the regional GNSS water vapor retrieval to some extent.

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

2019 ◽  
Vol 11 (16) ◽  
pp. 1893 ◽  
Author(s):  
Zhangyu Sun ◽  
Bao Zhang ◽  
Yibin Yao
Keyword(s):  
Grid Point ◽  
Satellite System ◽  
Temporal Variations ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
Weighted Mean ◽  
New Model ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Gpt2w Model

Precise modeling of tropospheric delay and weighted mean temperature (Tm) is critical for Global Navigation Satellite System (GNSS) positioning and meteorology. However, the model data in previous models cover a limited time span, which limits the accuracy of these models. Besides, the vertical variations of tropospheric delay and Tm are not perfectly modeled in previous studies, which affects the performance of height corrections. In this study, we used the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis from 1979 to 2017 to build a new empirical model. We first carefully modeled the lapse rates of tropospheric delay and Tm. Then we considered the temporal variations by linear trends, annual, and semi-annual variations and the spatial variations by grids. This new model can provide zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and Tm worldwide with a spatial resolution of 1° × 1°. We used the ECMWF ERA-Interim data and the radiosonde data in 2018 to validate this new model in comparison with the canonical GPT2w model. The results show that the new model has higher accuracies than the GPT2w model in all parameters. Particularly, this new model largely improves the accuracy in estimating ZHD and Tm at high-altitude (relative to the grid point height) regions.

scholarly journals A Weighted Mean Temperature Model with Nonlinear Elevation Correction Using China as an Example

2021 ◽  
Vol 13 (19) ◽  
pp. 3887
Author(s):  
Hai Zhu ◽  
Kejie Chen ◽  
Guanwen Huang
Keyword(s):  
Vertical Direction ◽  
Reference Value ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
Weighted Mean ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Elevation Correction

The weighted mean temperature (Tm) is a crucial parameter for determining the tropospheric delay in transforming precipitable water vapor. We used the reanalysis data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the distribution characteristics of Tm in the vertical direction in China. To address the problem that the precision of the traditional linear function model is limited in fitting the Tm profile, a scheme using the linear and Fourier functions to fit the Tm profile was constructed. Based on the least squares principle (LSQ) to fit the change in its coefficients over time, a Tm model for China with nonlinear elevation correction (CTm-h) was constructed. The experimental results show that, using ECMWF and radiosonde data to evaluate the precision of the CTm-h model, the RMS is 3.43 K and 4.64 K, respectively. Compared to GPT2w, the precision of the CTm-h model in China is increased by about 26.8%. The CTm-h model provides a significant improvement in the correction effect of Tm in the vertical direction, and the Tm profile calculated by the model is closer to the reference value.

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