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GWMT Global Atmospheric Weighted Mean Temperature Models: Development and Refinement

机译:GWMT全球大气加权平均温度模型:发展和完善

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The atmospheric weighted mean temperature T_m plays a pivotal role in remote sensing water vapor with GNSS technique. However, real-time T_m cannot be obtained at given place due to limitation of modern technology. In order to remove this barrier, a global and season-specific T_m model (GWMT-Ⅰ) based on spherical harmonics analysis was built using land radiosonde data. Combining Global Pressure and Temperature (GPT) model with the Bevis formula to provide virtual T_m observation at sea, a modified T_m model (GWMT-Ⅱ) was presented aimed to improve the accuracy of GWMT-Ⅰ in the ocean areas. Thereafter, the third generation T_m (GWMT-Ⅲ) model, derived from GGOS Atmosphere surface global grid T_m data, came into existence. But on the one hand, these three models fail to accurately describe vertical T_m variation. On the other hand, they are expanded into spherical harmonics up to degree and order nine which may impose restriction to their precision. This paper reconstructs the latest model (GWMT-Ⅳ) with 4D global NCEP/DOE 2 Reanalysis data from 2005 to 2009. Comparison of result between 2010 NCEP2 data and the GWMT-IV model shows that it achieves high accuracy all over the world and its modeling method better conforms to reality.
机译:大气加权平均温度T_m在利用GNSS技术遥感水蒸气中起着举足轻重的作用。然而,由于现代技术的限制,无法在给定位置获得实时T_m。为了消除这一障碍,利用地面无线电探空仪数据建立了基于球谐分析的全球和特定季节的T_m模型(GWMT-Ⅰ)。结合全球压力和温度(GPT)模型与Bevis公式,提供虚拟的海上T_m观测数据,提出了一种改进的T_m模型(GWMT-Ⅱ),以提高GWMT-Ⅰ在海洋地区的精度。此后,从GGOS大气表面全球网格T_m数据得出的第三代T_m(GWMT-Ⅲ)模型成立了。但是,一方面,这三个模型无法准确描述垂直T_m变化。另一方面,它们被扩展为球谐函数,其阶次为九阶,这可能会限制其精度。本文利用2005年至2009年的4D全球NCEP / DOE 2再分析数据重构了最新模型(GWMT-Ⅳ)。将2010 NCEP2数据与GWMT-IV模型的结果进行比较,表明该模型在全世界范围内均具有很高的准确性,建模方法更符合实际。

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