贵州局地Tm模型的建立及时空变化特征分析
Establishment of the Local Tm Model in Guizhou and Its Spatio-Temporal Variation
DOI: 10.12677/AAM.2022.111001, PDF,    国家自然科学基金支持
作者: 苏 雷, 张显云*, 袁 炜:贵州大学矿业学院,贵州 贵阳
关键词: 贵州加权平均温度ERA5精度时空变化Guizhou Weighted Average Temperature ERA5 Precision Spatio-Temporal Variation
摘要: 加权平均温度(Tm)作为GNSS反演大气可降水量(PWV)的关键参量,具有较强的区域差异性和时域周期性。针对贵州地形起伏大和气候多变的特点,为提升贵州地区GNSS PWV的探测精度,本文基于欧洲中期天气预报中心发布的ERA5气象数据集,在揭示贵州局域Tm时域周期性的基础上,构建了一种适宜于贵州的高时空分辨率局域Tm格网模型(GZTm),并基于无线电探空站气象资料计算的Tm,对GZTm的精度进行了评价。结果表明:相比于Bevis模型和GPT2w模型,GZTm在贵阳站的精度分别提高了24.8%和1.0%,在威宁站则分别提高了10.4%和8.1%。此外,基于选定的15个ERA5格网点,本文还对贵州Tm的时空变化特征进行了初步分析,结果显示贵州Tm的季节差异明显,且与站点高程间密切相关,呈现出较强的区域差异性。
Abstract: As a key parameter for GNSS inversion of precipitable water vapour (PWV), the weighted mean temperature (Tm) has strong regional differences and time-domain periodicity. In view of the characteristics of large topographic fluctuation and changeable climate in Guizhou, and in order to improve the precision of GNSS PWV within the Guizhou province, based on the ERA5 meteorological data set released by the European Centre for Medium-Range Weather Forecasts, a high spatio-temporal resolution Tm grid model (GZTm) suitable for Guizhou province was established on the basis of the revealing of the time domain periodicity for Guizhou Tm. Also, the precision of GZTm was evaluated by the Tm computed by the radiosonde dataset. Compared with the Bevis model and the GPT2w model, the results show that the precision of GZTm at Guiyang Station was increased by 24.8% and 1%, and 10.4% and 8.1% for Weining station, respectively. In addition, based on the selected 15 ERA5 grid points, this paper also conducts a preliminary analysis of the temporal and spatial characteristics of Guizhou Tm. The results show that Guizhou Tm owns obvious seasonal differences and is closely related to site elevation, showing strong regional differences.
文章引用:苏雷, 张显云, 袁炜. 贵州局地Tm模型的建立及时空变化特征分析[J]. 应用数学进展, 2022, 11(1): 1-8. https://doi.org/10.12677/AAM.2022.111001

参考文献

[1] 李宏达, 张显云, 王晓红, 等. 贵州局地加权平均温度模型的建立与精度分析[J]. 大地测量与地球动力学, 2020, 40(5): 496-501.
[2] Bevis, M., Businger, S., Herring, T.A., et al. (1992) GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System. Journal of Geophysical Research Atmospheres, 97, 15787-15801. [Google Scholar] [CrossRef
[3] 龚绍琦. 中国区域加权平均温度的时空变化及模型[J]. 应用气象学报, 2013, 24(3): 332-341.
[4] Chen, P., Yao, W. and Zhu, X. (2014) Realization of Global Empirical Model for Mapping Zenith Wet Delays onto Precipitable Water Using NCEP Re-Analysis Data. Geophysical Journal International, 198, 1748-1757. [Google Scholar] [CrossRef
[5] 罗桢, 吴良才. 南昌地区地基GPS水汽反演中区域加权平均温度模型[J]. 南昌大学学报(理科版), 2016, 40(3): 264-268.
[6] 谢劭峰, 黎峻宇, 刘立龙, 等. 新疆地区GGOS Atmosphere加权平均温度的精化[J]. 大地测量与地球动力学, 2017, 37(5): 472-477.
[7] 李黎, 樊奕茜, 王亮, 等. 湖南地区加权平均温度的影响因素分析及建模[J]. 大地测量与地球动力学, 2018, 38(1): 48-52.
[8] 朱海, 黄观文, 张菊清. 顾及气候差异的区域加权平均温度模型——以中国陕西为例[J]. 测绘学报, 2021, 50(3): 356-367.
[9] Xia, P., Ye, S., Chen, B., et al. (2019) Improving the Weighted Mean Temperature Model: A Case Study Using Nine Year (2007-2015 Radiosonde and COSMIC Data in Hong Kong. Meteorological Applications, 27, 1-13. [Google Scholar] [CrossRef
[10] Albergel, C., Dutra, E., Munier, S., et al. (2018) ERA-5 and ERA-Interim Driven ISBA Land Surface Model Simulations: Which One Performs Better?. Hydrology and Earth System Sciences, 22, 3515-3532. [Google Scholar] [CrossRef
[11] Bevis, M., Businger, S. and Chiswell, S. (1994) GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water. Journal of Applied Meteorology, 33, 379-386. [Google Scholar] [CrossRef
[12] Yao, Y., Xu, C., Zhang, B., et al. (2014) GTm-III: A New Global Empirical Model for Mapping Zenith Wet Delays onto Precipitable Water Vapour. Geophysical Journal International, 197, 202-212. [Google Scholar] [CrossRef
[13] Böhm, J., Möller, G., Schindelegger, M., et al. (2015) Development of an Improved Empirical Model for Slant Delays in the Troposphere (GPT2w). GPS Solutions, 19, 433-441. [Google Scholar] [CrossRef
[14] Chen, P. and Yao, W. (2015) GTm_X: A New Version Global Weighted Mean Temperature Model. Lecture Notes in Electrical Engineering, 341, 605-611. [Google Scholar] [CrossRef