Atmospheric Weighted Mean Temperature Model in Guilin

Mo, Zhixiang; Huang, Liang Ke; Peng, Hua; Liu, L. L.; Kang, Chuanli

doi:10.5194/isprs-archives-xlii-3-w10-1155-2020

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…This model considers the large topographic fluctuations and lapse rate function of T m in China and can provide high-precision and real-time T m only by inputting time and station location information. In terms of the performances of these empirical T m models, they do not require any input of meteorological parameters and considers the temporal and spatial variation characteristics of T m , hence making this model very useful for those users who cannot obtain surface temperature and demand relatively high accuracy [29]. The CT m model takes into account the vertical lapse rate change of T m and shows a significant advantage in China, especially in the Qinghai-Tibet Plateau region [30].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis and Validation of the Atmospheric Weighted Mean Temperature Models in China

Zhao

et al. 2022

Remote Sensing

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Atmospheric weighted mean temperature (Tm) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical Tm models using meteorological or non-meteorological parameters have been proposed to calculate PWV, but their accuracy and reliability cannot be guaranteed in some regions. To validate and determine the optimal Tm model for PWV retrieval in China, this paper analyzes and evaluates some typical Tm models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the Tm model for China (CTm), the Global Weighted Mean Temperature-H (GTm-H) and the Global Tropospheric (GTrop) models. The Tm values of these models are first obtained at corresponding radiosonde (RS) stations in China over the period of 2011 to 2020. The corresponding Tm values of 87 RS stations in China are also calculated using the layered meteorological data and regarded as the reference. Comparison results show that the accuracy of these five Tm models in China has an obvious geographical distribution and decreases along with increasing altitude and latitude, respectively. The average root mean square (RMS) and Bias for the Linear, GPT3, CTm, GTm-H and GTrop models are 4.2/3.7/3.4/3.6/3.3 K and 0.7/−1.0/0.7/−0.1/0.3 K, respectively. Among these models, Linear and GPT3 models have lower accuracy in high-altitude regions, whereas CTm, GTm-H and GTrop models show better accuracy and stability throughout the whole China. These models generally have higher accuracy in regions with low latitude and lower accuracy in regions with middle and high latitudes. In addition, Linear and GPT3 models have poor accuracy in general, whereas GTm-H and CTm models are obviously less accurate and stable than GTrop model in regions with high latitude. These models show different accuracies across the four geographical regions of China, with GTrop model demonstrating the relatively better accuracy and stability. Therefore, the GTrop model is recommended to obtain Tm for calculating PWV in China.

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Section: Introductionmentioning

confidence: 99%