An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate

Yang, Fei; Guo, Jiming; Meng, Xiaolin; Shi, Junbo; Zhang, Di; Zhao, Yinzhi

doi:10.1007/s10291-020-0953-9

Cited by 24 publications

(13 citation statements)

References 30 publications

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“…The Saastamoinen model is the most commonly used empirical ZWD model in GNSS meteorology (Bevis et al 1992;Askne and Nordius 1987;Ross et al 1997;Yang et al 2020a). Taking account of the influence of the surface temperature, water vapor pressure, and location of site (i.e., latitude and height) on the ZWD estimation, the Saastamoinen-ZWD model is shown as:…”

Section: Methodology and Data Descriptionmentioning

confidence: 99%

Establishment and assessment of a zenith wet delay (ZWD) augmentation model

et al. 2021

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The zenith wet delay (ZWD) accuracy is one of the most important factors affecting the convergence time and the positioning precision in precise point positioning processing. Taking into account the limitation of the empirical and blind ZWD models, the correlations between the ZWD and temperature and water vapor pressure at each point of the global 1º × 1º grid were analyzed. Based on the global grid of augmentation coefficients derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) data with a temporal resolution of 6 h from 2011 to 2014, a new ZWD augmentation model was established, which is a weighting of differences between the in situ meteorological quantities and those from the GPT3 model. The performance of the proposed model was assessed against three traditional models using different data sources in 2015, i.e., the ECMWF grids and globally distributed International GNSS Service (IGS) sites. The numerical results show that the proposed model outperforms the other three models with the largest RMSE/RRMSE improvement, reaching 1.08 cm/11.37% and 0.99 cm/9.8% when comparing the ECMWF grids and IGS sites, respectively.

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Section: Methodology and Data Descriptionmentioning

confidence: 99%

Establishment and assessment of a zenith wet delay (ZWD) augmentation model

et al. 2021

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“…Previous studies Li et al, 2020;Sun et al, 2021;Yang et al, 2020) conducted in-depth research and analysis on the temporal and spatial characteristics of Tm VLR, and the results showed that Tm VLR has seasonal changes and varies with geographic location.…”

Section: Analysis Of Tm Vlrmentioning

confidence: 99%

Development and Assessment of an ALLSSA‐Based Atmospheric Weighted Mean Temperature Model With High Time Resolution for GNSS Precipitable Water Retrieval

Chen

et al. 2022

Earth and Space Science

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The atmosphere weighted mean temperature, Tm, is an essential parameter for retrieving precipitable water from the ground‐based Global Navigation Satellite Systems (GNSS). The accuracy of high temporal resolution GNSS precipitable water vapor (PWV) estimation requires wideband Tm information and its magnitude. However, existing Tm empirical models use trigonometric functions with only fixed amplitude and low‐frequency for time fitting, which limits real‐time or near real‐time PWV retrieval from GNSS observation. Thus, an improved Tm model for China, LTCm, containing more frequency information of Tm, based on the antileakage least‐squares spectrum analysis by utilizing the ERA5 pressure‐level products during the years 2015–2019, is developed. Both Tm data from ERA5 pressure‐level products and radiosonde stations distributed in China over 2020 are selected as reference values to verify the performance of the LCTm model. The results show that the LCTm model yields significant performance against other models in Tm estimation over China, especially in marine regions and high‐altitude areas. Furthermore, the LCTm model can generally achieve a mean Bias/root mean square (RMS) of −0.33 K/2.06 K in contrast to ERA5 pressure‐level products and 0.03 K/3.47 K in comparison with radiosonde, which corresponds to a 7.2%–13.8% improvement against GPT2w, GTm‐III, and Bevis. Moreover, LCTm has σ pwv and σ pwv /PWV values of 0.27 mm and 1.25% when used to retrieve GNSS‐PWV, respectively. Consequently, the LCTm model that considers high‐frequency information of Tm can obtain more reliable Tm values. Therefore, the LCTm model can be applied to real‐time or near real‐time GNSS PWV retrieval, which is of great significance for GNSS meteorological research.

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“…With the widespread use of GPT series models, many relevant studies have emerged to improve or validate the performance of these models [11][12][13][14][15][16][17][18][19][20][21][22]. However, most validations are conducted at the surface rather than at higher altitudes.…”

Section: Saastamoinen Askne and Nordiusmentioning

confidence: 99%

Development and Assessment of Improved Global Pressure and Temperature Series Models

2021

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Global pressure and temperature (GPT) series models can provide the underlying meteorological parameters for tropospheric corrections without any other meteorological observations, which allows them to be widely used for a series of geodetic as well as meteorological and climatological purposes. Due to the height difference between the empirical model height and user location, a vertical correction of meteorological parameters is inevitable, particularly for airborne users. Unfortunately, the GPT series models have limitations on the vertical correction. We explored the temperature lapse rate for the vertical adjustment using 10 years of reanalysis data provided by the National Centers for Environmental Prediction (NCEP), and extended the GPT models to improved global pressure and temperature (IGPT) series models by introducing a new temperature lapse rate model and a new formulation of pressure reduction. An Evaluation of the IGPT model expression determines that the IGPT models have better accuracy than the GPT models, particularly under large height differences, which is attributed to their ability to consider the real behavior of temperature in the atmosphere and adiabatic effects on air pressure. The performance of the IGPT models in zenith tropospheric delay (ZTD) estimations also has been evaluated by comparison with the fifth-generation European Centers for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) data and International GNSS Service (IGS) data. The results confirm that our new models can effectively improve the accuracy of ZTDs, particularly at larger altitude differences between the target height and the corresponding four grid points of the model, not only enhancing the performance of the model in complex terrain but also extending the feasibility of IGPT models from the Earth's surface to higher altitudes.

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An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate

Cited by 24 publications

References 30 publications

Establishment and assessment of a zenith wet delay (ZWD) augmentation model

Establishment and assessment of a zenith wet delay (ZWD) augmentation model

Development and Assessment of an ALLSSA‐Based Atmospheric Weighted Mean Temperature Model With High Time Resolution for GNSS Precipitable Water Retrieval

Development and Assessment of Improved Global Pressure and Temperature Series Models

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