scholarly journals An ERA5‐Based Model for Estimating Tropospheric Delay and Weighted Mean Temperature Over China With Improved Spatiotemporal Resolutions

2019 ◽  
Vol 6 (10) ◽  
pp. 1926-1941 ◽  
Author(s):  
Zhangyu Sun ◽  
Bao Zhang ◽  
Yibin Yao
Keyword(s):  
Tropospheric Delay ◽  
Weighted Mean ◽  
Mean Temperature ◽  
Weighted Mean Temperature

scholarly journals A new voxel-based model for the determination of atmospheric weighted mean temperature in GPS atmospheric sounding

2017 ◽  
Vol 10 (6) ◽  
pp. 2045-2060 ◽  
Author(s):  
Changyong He ◽  
Suqin Wu ◽  
Xiaoming Wang ◽  
Andong Hu ◽  
Qianxin Wang ◽  
...  
Keyword(s):  
Precipitable Water ◽  
The United States ◽  
Reanalysis Data ◽  
Tropospheric Delay ◽  
Weighted Mean ◽  
Precipitable Water Vapour ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Environmental Prediction ◽  
D Model

Abstract. The Global Positioning System (GPS) is a powerful atmospheric observing system for determining precipitable water vapour (PWV). In the detection of PWV using GPS, the atmospheric weighted mean temperature (Tm) is a crucial parameter for the conversion of zenith tropospheric delay (ZTD) to PWV since the quality of PWV is affected by the accuracy of Tm. In this study, an improved voxel-based Tm model, named GWMT-D, was developed using global reanalysis data over a 4-year period from 2010 to 2013 provided by the United States National Centers for Environmental Prediction (NCEP). The performance of GWMT-D was assessed against three existing empirical Tm models – GTm-III, GWMT-IV, and GTm_N – using different data sources in 2014 – the NCEP reanalysis data, surface Tm data provided by Global Geodetic Observing System and radiosonde measurements. The results show that the new GWMT-D model outperforms all the other three models with a root-mean-square error of less than 5.0 K at different altitudes over the globe. The new GWMT-D model can provide a practical alternative Tm determination method in real-time GPS-PWV remote sensing systems.

scholarly journals A Regional Model for Predicting Tropospheric Delay and Weighted Mean Temperature in China Based on GRAPES_MESO Forecasting Products

2021 ◽  
Vol 13 (13) ◽  
pp. 2644
Author(s):  
Liying Cao ◽  
Bao Zhang ◽  
Junyu Li ◽  
Yibin Yao ◽  
Lilong Liu ◽  
...  
Keyword(s):  
Empirical Models ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
In Situ Observation ◽  
Weighted Mean ◽  
Gnss Positioning ◽  
Gnss Meteorology ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Gpt2w Model

Accurate tropospheric delay (TD) and weighted mean temperature (Tm) are important for Global Navigation Satellite System (GNSS) positioning and GNSS meteorology. For this purpose, plenty of empirical models have been built to provide estimates of TD and Tm. However, these models cannot resolve TD and Tm variations at synoptic timescales since they only model the average annual, semi-annual, and/or daily variations. As a result, the existed empirical models cannot perform well under extreme weather conditions. To address this limitation, we propose to estimate Zenith Hydrostatic Delay (ZHD), Zenith Wet Delay (ZWD), and Tm directly from the stratified numerical weather forecasting products of the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_MESO) of China. The GRAPES_MESO forecasting data has a temporal resolution of 3 h, which provides the opportunity to resolve the synoptic variation. However, it is found that the estimated ZWD and Tm exhibit apparent systematic deviation from in situ observation-based estimates, which is due to the inherent biases in the GRAPES_MESO data. To solve this problem, we propose to correct these biases using a linear model and a spherical cap harmonic model. The estimates after correction are termed as the “CTropGrid” products. When validated by the radiosonde data, the CTropGrid product has biases of 1.5 mm, −0.7 mm, and −0.1 K, and Root Mean Square (RMS) error of 8.9 mm, 20.2 mm, and 1.5 K for ZHD, ZWD, and Tm. Compared to the widely used GPT2w model, the CTropGrid products have improved the accuracies of ZHD, ZWD, and Tm by 11.9%, 55.6%, and 60.5% in terms of RMS. When validating the Zenith Tropospheric Delay (ZTD) products (the sum of ZHD and ZWD) using the IGS ZTD data, the CTropGrid ZTD has a bias of −0.7 mm and an RMS of 35.8 mm, which is 22.7% better than the GPT2w model in terms of RMS. Besides the accuracy improvements, the CTropGrid products well model the synoptic-scale variations of ZHD, ZWD, and Tm. Compared to the existing empirical models that only capture the tidal (seasonal and/or diurnal) variations, the CTropGrid products capture well the non-tidal variations of ZHD, ZWD, and Tm, which enhances the tropospheric delay corrections and GNSS water vapor monitoring at synoptic timescales. Therefore, the CTropGrid product is an important progress in GNSS positioning and GNSS meteorology.

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.

scholarly journals A new voxel-based model for the determination of atmospheric-weighted-mean temperature in GPS atmospheric sounding

2016 ◽  
Author(s):  
Changyong He ◽  
Suqin Wu ◽  
Xiaoming Wang ◽  
Andong Hu ◽  
Kefei Zhang
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Tropospheric Delay ◽  
Weighted Mean ◽  
Precipitable Water Vapour ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
D Model

Abstract. The Global Positioning System (GPS) has been regarded as a powerful atmospheric observing system for determining precipitable water vapour (PWV) nowadays. One of the most critical variables in PWV remote sensing using GPS technique is the zenith tropospheric delay (ZTD). The conversion from ZTD to PWV requires a good knowledge of the atmospheric-weighted-mean temperature (Tm) over the station. Thus the quality of PWV is affected by the accuracy of both ZTD and Tm. In this study, an improved voxel-based Tm model, named GWMT−D, was developed and validated using global reanalysis data from 2010 to 2014 provided by NCEP-DOE Reanalysis 2 data (NCEP2). The performance of GWMT−D, along with other three selected empirical Tm models, GTm−III, GWMT−IV and GTm_N, was assessed with reference Tm derived from different sources – the NCEP2, Global Geodetic Observing System (GGOS) data and radiosonde measurements. The results showed that the new GWMT−D model outperformed all the other three models with a root-mean-square error of less than 5.0 K at different altitudes over the globe. The new GWMT−D model can provide an alternative Tm determination method in real-time/near real-time GPS-PWV remote sensing system.

scholarly journals Improving the Estimation of Weighted Mean Temperature in China Using Machine Learning Methods

2021 ◽  
Vol 13 (5) ◽  
pp. 1016
Author(s):  
Zhangyu Sun ◽  
Bao Zhang ◽  
Yibin Yao
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Vapor ◽  
Water Vapor Pressure ◽  
Tropospheric Delay ◽  
Weighted Mean ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Mean Temperature ◽  
Weighted Mean Temperature

As a crucial parameter in estimating precipitable water vapor from tropospheric delay, the weighted mean temperature (Tm) plays an important role in Global Navigation Satellite System (GNSS)-based water vapor monitoring techniques. However, the rigorous calculation of Tm requires vertical profiles of temperature and water vapor pressure that are difficult to acquire in practice. As a result, empirical models are widely used but have limited accuracy. In this study, we use three machine learning methods, i.e., random forest (RF), backpropagation neural network (BPNN), and generalized regression neural network (GRNN), to improve the estimation of empirical Tm in China. The basic idea is to use the high-quality radiosonde observations estimated Tm to calibrate and optimize the empirical Tm through machine learning methods. Validating results show that the three machine learning methods improve the Tm accuracy by 37.2%, 32.6%, and 34.9% compared with the global pressure and temperature model 3 (GPT3). In addition to the overall accuracy improvement, the proposed methods also mitigate the accuracy variations in space and time, guaranteeing evenly high accuracy. This study provides a new idea to estimate Tm, which could potentially contribute to the GNSS meteorology.

Improved atmospheric weighted mean temperature modeling using sparse kernel learning

GPS Solutions ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Liu Yang ◽  
Guobin Chang ◽  
Nijia Qian ◽  
Jingxiang Gao
Keyword(s):  
Kernel Learning ◽  
Weighted Mean ◽  
Temperature Modeling ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Sparse Kernel

A new global grid model for the determination of atmospheric weighted mean temperature in GPS precipitable water vapor

2018 ◽  
Vol 93 (2) ◽  
pp. 159-176 ◽  
Author(s):  
Liangke Huang ◽  
Weiping Jiang ◽  
Lilong Liu ◽  
Hua Chen ◽  
Shirong Ye
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Weighted Mean ◽  
Grid Model ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Global Grid

scholarly journals An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

2020 ◽  
Vol 12 (7) ◽  
pp. 1098
Author(s):  
Pedro Mateus ◽  
João Catalão ◽  
Virgílio B. Mendes ◽  
Giovanni Nico
Keyword(s):  
Water Vapor ◽  
Air Temperature ◽  
Surface Pressure ◽  
Surface Air Temperature ◽  
Precipitable Water ◽  
Weighted Mean ◽  
Zenith Wet Delay ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Zenith Hydrostatic Delay

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

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