FY-4A GIIRS数据与ERA5再分析资料融合的中国区域大气加权平均温度模型

doi:10.11947/j.AGCS.2023.20210717

测绘学报 ›› 2023, Vol. 52 ›› Issue (6): 904-916.doi: 10.11947/j.AGCS.2023.20210717

FY-4A GIIRS数据与ERA5再分析资料融合的中国区域大气加权平均温度模型

王新志^1,2,3, 陈发源¹

1. 南京信息工程大学遥感与测绘工程学院, 江苏南京 210044;
2. 南京信息工程大学无锡研究院, 江苏无锡 214100;
3. 南京信息工程大学大气与环境实验教学中心, 江苏南京 210044

收稿日期:2021-12-30 修回日期:2022-10-17 发布日期:2023-07-08
通讯作者: 陈发源 E-mail:631074939@qq.com
作者简介:王新志(1981-),男,博士,副教授,研究方向为GNSS气象学、工程测量与大地测量。E-mail:48984755@qq.com
基金资助:
江苏省重点研发计划(BE2021622);江苏省自然科学基金(BK20211037);江苏省高等教育教改项目(2021JSJG219);江苏省研究生实践创新计划(SJCX21_0373);无锡市科技发展资金项目(N20201011)

Using FY-4A GIIRS data and ERA5 reanalysis data to build a regional atmospheric weighted mean temperature model in China

WANG Xinzhi^1,2,3, CHEN Fayuan¹

1. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. Wuxi Institute, Nanjing University of Information Science and Technology, Wuxi 214100, China;
3. Experimental Teaching Center for Meteorology and Environment, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received:2021-12-30 Revised:2022-10-17 Published:2023-07-08
Supported by:
The Key Research and Development Program of Jiangsu Province (No. BE2021622); The Natural Science Foundation of Jiangsu Province(No. BK20211037);Higher Education Reform Project of Jiangsu Province(No. 2021JSJG219);Postgraduate Practice Innovation Program of Jiangsu Province(No. SJCX21_0373);Science and Technology Development Fund Project of Wuxi city(No. N20201011)

摘要/Abstract

摘要： 大气加权平均温度(T_m)的精度直接影响全球导航卫星系统(GNSS)水汽反演的结果。针对现有T_m模型的参数、建模数据源有待优化及模型构建时仅依赖于单个探空站点或单一格网点数据等问题,本文提出融合FY-4A GIIRS数据与ERA5再分析资料,在此基础上引入滑动窗口算法对融合数据进行处理同时顾及经度、纬度和高程因子构建空间分辨率为0.5°×0.5°的T_m经验模型(FY-ET_m模型)。采用偏差(Bias)和均方根误差(RMS)作为精度评定指标,联合未参与建模的2020年探空数据、ERA5再分析资料及天顶对流层延迟产品,对FY-ET_m模型及其反演的大气可降水量进行精度评定。结果表明:以探空数据为参考值,FY-ET_m模型的年均Bias、RMS分别为-0.02、5.79 K,相比较于Bevis和GPT3模型分别提高了3.62(Bias)、0.8(RMS)和2.54(Bias)、0.63 K(RMS);以ERA5再分析资料为参考值,FY-ET_m模型的年均Bias、RMS分别为0.01、3.32 K,相比较于Bevis和GPT3模型分别提高了0.97(Bias)、0.13(RMS)和2.94(Bias)、1.71 K(RMS),同精度优异的GPT3模型相比,FY-ET_m模型在中国西部和北部地区也表现出了明显的精度改善;以GNSS站点得到的PWV为参考值,FY-ET_m模型反演的PWV与GNSS站得到的PWV值精度相当,Bias变化范围为-0.5~0.5 mm。FY-ET_m模型准确度高稳定性良好,只需输入位置和时间信息就能获取目标点的T_m,能够在GNSS水汽反演中发挥重要的作用。

关键词: FY-4A GIIRS, ERA5, 大气加权平均温度, GPT3

Abstract: The accuracy of atmospheric weighted mean temperature (T_m) directly affects the results of Global Navigation Satellite System (GNSS) precipitable water vapor inversion.For the existing T_m model parameters, modeling data sources to be optimized and the model construction only relies on a single sounding site or single grid point data. It is proposed to fuse FY-4A GIIRS data with ERA5 reanalysis data. A sliding window algorithm is introduced to process the fused data while taking into account the longitude, latitude and elevation factors to construct an empirical T_m model (FY-ET_m model) with a spatial resolution of 0.5°×0.5°. Bias and root mean square error (RMS) are used as accuracy metrics, combined with the 2020 sounding data, ERA5 reanalysis data, and zenith tropospheric delay (ZTD) products that are not involved in the modeling. Subsequently, the accuracy of the FY-ET_m model and its inverse precipitable water vapor (PWV) are evaluated. The results show that the average annual Bias and RMS of FY-ET_m model are -0.02、 5.79 K, respectively, which are 3.62(Bias)、 0.8(RMS)、 2.54(Bias) and 0.63 K (RMS) higher than those of Bevis and GPT3 models. With the reanalysis data of ERA5 as the reference value, the average annual Bias and RMS of FY-ET_m model are 0.01、 3.32 K, respectively, which are 0.97(Bias)、 0.13(RMS)、 2.94(Bias) and 1.71 K(RMS) higher than those of Bevis and GPT3 models. Compared with GPT3 model with excellent accuracy, FY-ET_m model also shows obvious accuracy improvement in western and northern China. With the PWV obtained from GNSS station as the reference value, the accuracy of PWV inversion from FY-ET_m model is similar to that from GNSS station, Bias ranges from -0.5 mm to 0.5 mm.The FY-ET_m model has high accuracy and good stability. It can obtain the T_mof the target point only by inputting the position and time information, and it can play an important role in GNSS precipitable water vapor inversion.

Key words: FY-4A GIIRS, ERA5, atmospheric weighted mean temperature, GPT3

中图分类号:

P228

王新志, 陈发源. FY-4A GIIRS数据与ERA5再分析资料融合的中国区域大气加权平均温度模型[J]. 测绘学报, 2023, 52(6): 904-916.

WANG Xinzhi, CHEN Fayuan. Using FY-4A GIIRS data and ERA5 reanalysis data to build a regional atmospheric weighted mean temperature model in China[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(6): 904-916.

参考文献

[1] 姚宜斌, 张顺, 孔建. GNSS空间环境学研究进展和展望[J]. 测绘学报, 2017, 46(10): 1408-1420. DOI: 10.11947/j.AGCS.2017.20170333. YAO Yibin, ZHANG Shun, KONG Jian. Research progress and prospect of GNSS space environment science[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1408-1420. DOI: 10.11947/j.AGCS.2017.20170333.
[2] 张京朋. 中国区域大气水汽变化的观测、模拟及其归因研究[D]. 兰州: 兰州大学, 2019. ZHANG Jingpeng. Observation, simulation and attribution of atmospheric water vapor change over China[D]. Lanzhou: Lanzhou University, 2019.
[3] 丁金才. GPS气象学及其应用[M]. 北京: 气象出版社, 2009. DING Jincai. GPS meteorology and its application[M]. Beijing: China Meteorological Press, 2009.
[4] 李昊睿, 丁伟钰, 薛纪善, 等. 广东省GPS/PWV资料的质量控制及其对前汛期降水预报影响的初步研究[J]. 热带气象学报, 2014, 30(3): 455-462. LI Haorui, DING Weiyu, XUE Jishan, et al. A preliminary study on the quality control method for Guangdong GPS/PWV data and its effects on precipitation forecasts in the annually first raining season of Guangdong[J]. Journal of Tropical Meteorology, 2014, 30(3): 455-462.
[5] 朱玉香, 陈永贵, 安春华. 台风“利奇马”在山东期间的GPS-PWV动态特征[J]. 导航定位学报, 2020, 8(6): 103-108. ZHU Yuxiang, CHEN Yonggui, AN Chunhua. Dynamic characteristics of GPS-PWV during typhoon Lekima in Shandong Province[J]. Journal of Navigation and Positioning, 2020, 8(6): 103-108.
[6] LUO Yu, GAO Wenjuan, LUO Linyan, et al. Analysis on the characteristics of GPS-PWV during heavy rainfall in Huaihua region[J]. Meteorological and Environmental Research, 2020, 11(3): 1-6, 11.
[7] BEVIS M, BUSINGER S, HERRING T A, et al. GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system[J]. Journal of Geophysical Research, 1992, 97(D14): 15787.
[8] 黄良珂,李琛,谢劭峰,等.顾及垂直递减率的中国区域大气加权平均温度格点产品空间插值研究[J].武汉大学学报(信息科学版), 2023, 48(2): 295-300. HUANG Liangke, LI Chen, XIE Shaofeng, et al. Spatial interpolation of atmospheric weighted mean temperature grid products in China with consideration of vertical lapse rate[J]. Geomatics and Information Science of Wuhan University, 2023, 48(2): 295-300.
[9] 姚宜斌, 孙章宇, 许超钤. Bevis公式在不同高度面的适用性以及基于近地大气温度的全球加权平均温度模型[J]. 测绘学报, 2019, 48(3): 276-285. DOI: 10.11947/j.AGCS.2019.20180160. YAO Yibin, SUN Zhangyu, XU Chaoqian. Applicability of Bevis formula at different height level and global weighted mean temperature model based on near-earth atmospheric temperature[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(3): 276-285. DOI: 10.11947/j.AGCS.2019.20180160.
[10] 莫智翔, 黎杏, 黄良珂, 等. 顾及多因子影响的中国西部地区大气加权平均温度模型精化研究[J]. 大地测量与地球动力学, 2021, 41(2): 145-151. MO Zhixiang, LI Xing, HUANG Liangke, et al. Refinement of atmospheric weighted mean temperature model considering the effects of multiple factors for Western China[J]. Journal of Geodesy and Geodynamics, 2021, 41(2): 145-151.
[11] 李媛, 李黎, 张振, 等. 长三角地区分季节多因子本地化T_m模型研究[J]. 大地测量与地球动力学, 2020, 40(2): 140-145. LI Yuan, LI Li, ZHANG Zhen, et al. Research on seasonal and multifactor T_m model of weighted average temperature in Yangtze River Delta[J]. Journal of Geodesy and Geodynamics, 2020, 40(2): 140-145.
[12] 范士杰, 刘兆健, 陈岩, 等. 青岛地区大气加权平均温度模型优化[J]. 地理空间信息, 2021, 19(9): 136-138, 146, 8. FAN Shijie, LIU Zhaojian, CHEN Yan, et al. Optimization of atmospheric weighted mean temperature model in Qingdao[J]. Geospatial Information, 2021, 19(9): 136-138, 146, 8.
[13] 徐铭泽, 郭秋英, 侯建辉, 等. 济南地区加权平均温度模型建立及精度分析[J]. 导航定位学报, 2021, 9(5): 142-151. XU Mingze, GUO Qiuying, HOU Jianhui, et al. Modeling and accuracy analysis of weighted mean temperature in Jinan region[J]. Journal of Navigation and Positioning, 2021, 9(5): 142-151.
[14] 刘立龙, 万庆同, 周威, 等. 基于傅里叶级数的中国沿海地区T_m模型精化研究[J]. 大地测量与地球动力学, 2019, 39(11): 1137-1141. LIU Lilong, WAN Qingtong, ZHOU Wei, et al. Research on refinement of T_m model in China's coastal area based on Fourier series[J]. Journal of Geodesy and Geodynamics, 2019, 39(11): 1137-1141.
[15] LAGLER K, SCHINDELEGGER M, BÖHM J, et al. GPT2: Empirical slant delay model for radio space geodetic techniques[J]. Geophysical Research Letters, 2013, 40(6): 1069-1073.
[16] BÖHM J, MÖLLER G, SCHINDELEGGER M, et al. Development of an improved empirical model for slant delays in the troposphere (GPT2w)[J]. GPS Solutions, 2015, 19(3): 433-441.
[17] LANDSKRON D, BÖHM J. VMF3/GPT3: refined discrete and empirical troposphere mapping functions[J]. Journal of Geodesy, 2018, 92(4): 349-360.
[18] 黄良珂, 彭华, 刘立龙, 等. 顾及垂直递减率函数的中国区域大气加权平均温度模型[J]. 测绘学报, 2020, 49(4): 432-442. DOI: 10.11947/j.AGCS.2020.20190168. HUANG Liangke, PENG Hua, LIU Lilong, et al. An empirical atmospheric weighted mean temperature model considering the lapse rate function for China[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(4): 432-442. DOI: 10.11947/j.AGCS.2020.20190168.
[19] 姚宜斌, 孙章宇, 许超钤, 等. 顾及非线性高程归算的全球加权平均温度模型[J]. 武汉大学学报(信息科学版), 2019, 44(1): 106-111. YAO Yibin, SUN Zhangyu, XU Chaoqian, et al. Global weighted mean temperature model considering nonlinear vertical reduction[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 106-111.
[20] 黄良珂, 陈华, 刘立龙, 等. 一种新的高精度全球对流层天顶延迟模型[J]. 地球物理学报, 2021, 64(3): 782-795. HUANG Liangke, CHEN Hua, LIU Lilong, et al. A new high-precision global model for calculating zenith tropospheric delay[J]. Chinese Journal of Geophysics, 2021, 64(3): 782-795.
[21] 杨雨晗, 尹球, 束炯. FY-4A大气垂直探测仪(GIIRS)温度探测通道优选[J]. 红外与毫米波学报, 2018, 37(5): 545-552. YANG Yuhan, YIN Qiu, SHU Jiong. Channel selection of atmosphere vertical sounder (GIIRS) onboard the FY-4A geostationary satellite[J]. Journal of Infrared and Millimeter Waves, 2018, 37(5): 545-552.
[22] 王帅民. 基于GNSS和再分析资料的ZTD/PWV精度评定与模型构建方法研究[D]. 济南: 山东大学, 2021. WANG Shuaimin. Research on accuracy evaluation and model establishment of ZTD/PWV based on GNSS and reanalysis[D]. Jinan: Shandong University, 2021.
[23] 王明华, 曹云昌, 梁宏, 等. 中国区域性大气加权平均温度线性模型精度评估[J]. 南京信息工程大学学报(自然科学版), 2021, 13(2): 161-169. WANG Minghua, CAO Yunchang, LIANG Hong, et al. On the accuracy of regional weighted mean temperature linear models over China[J]. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), 2021, 13(2): 161-169.
[24] 中国气象局. 常规高空气象观测业务规范[M].北京:气象出版社,2010. China Meteorological Administration. Operational norms for routine upper-air meteorological observation[M]. Beijing: Meteorological Publishing House,2010.
[25] 龙凤阳, 胡伍生, 杨雪晴. 基于误差补偿的中国区域加权平均温度模型研究[J]. 测绘工程, 2021, 30(5): 1-6. LONG Fengyang, HU Wusheng, YANG Xueqing. Research on the model of weighted mean temperature in China based on error compensation[J]. Engineering of Surveying and Mapping, 2021, 30(5): 1-6.
[26] HUANG Liangke, LIU Lilong, CHEN Hua, et al. An improved atmospheric weighted mean temperature model and its impact on GNSS precipitable water vapor estimates for China[J]. GPS Solutions, 2019, 23(2): 51.
[27] SAASTAMOINEN J. Contributions to the theory of atmospheric refraction[J]. Bulletin Géodésique, 1973, 107(1): 13-34.
[28] DAVIS J L, HERRING T A, SHAPIRO I I, et al. Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length[J]. Radio Science, 1985, 20(6): 1593-1607.
[29] 王勇,刘严萍. 地基GPS气象学原理与应用研究[M]. 北京:测绘出版社,2012. WANG Yong, LIU Yanping. Theory and application of ground-based GPS meteorology[M]. Beijing: Surveying & Mapping Press, 2012.

FY-4A GIIRS数据与ERA5再分析资料融合的中国区域大气加权平均温度模型

Using FY-4A GIIRS data and ERA5 reanalysis data to build a regional atmospheric weighted mean temperature model in China

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价

[1]	谢劭峰, 王义杰, 黄良珂, 彭华, 黎峻宇, 刘立龙. 中国区域大气加权平均温度垂直递减率格网模型[J]. 测绘学报, 2023, 52(2): 206-217.
[2]	周要宗, 楼益栋, 张卫星, 梁宏, 施闯, 吴迪, 曹云昌. 基于CRA40产品的对流层延迟计算及对比分析[J]. 测绘学报, 2023, 52(1): 22-31.
[3]	徐天河, 李耸, 王帅民, 江楠. 顾及气象数据的中国区域对流层延迟RBF神经网络优化模型[J]. 测绘学报, 2022, 51(8): 1690-1707.
[4]	杨飞, 郭际明, 陈明, 章迪. GNSS大气加权平均温度经验模型精化方法的建立和分析[J]. 测绘学报, 2022, 51(11): 2339-2345.