Open AccessGNSS Precipitable Water Vapor Retrieval With the Aid of NWM Data for China
Author(s) -
Huang Liangke,
Peng Hua,
Liu Lilong,
Xiong Si,
Xie Shaofeng,
Chen Jun,
Li Junyu,
He Hongchang
Publication year - 2021
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2020ea001550
Subject(s) - gnss applications , radiosonde , nowcasting , precipitable water , meteorology , remote sensing , satellite system , environmental science , computer science , global positioning system , geography , water vapor , telecommunications
Global Navigation Satellite System (GNSS) has become a powerful tool for sensing precipitable water vapor (PWV), which is important in investigating long‐term climate or nowcasting. Zenith hydrostatic delay (ZHD) and weighted mean temperature ( T m ) are the two key parameters for retrieving PWV GNSS observations. ZHD is generally derived from the Saastamoinen model by using surface pressure or numerical weather model (NWM). However, the lack of meteorological observations located at or near the GNSS sites is a big challenge for the calculation of accurate ZHD, leading to inconvenience in GNSS‐derived PWV. In this study, a method for GNSS retrieval in China by using ZHD from the NWM and T m derived from IGPT2w is proposed. First, a new grid model, namely CGZHD model, is developed for the vertical adjustment of ZHD in China. Then, two types of ZHD products derived from NWM data, that is, postprocessing and forecast ones, namely ZHD and ZHD FC , respectively. Finally, the performance of GNSS PWV estimation for 18 GNSS sites is evaluated with PWVs from collocated radiosonde sites for China. Results show that the CGZHD model has excellent performance against the GPT2w model and previous studies. ZHD and ZHD FC from NWM present high precision after vertical adjustment. In terms of GNSS PWV retrieval, two modes of GNSS PWV estimation results, that is, ZHD and ZHD FC obtained from postprocessing and forecast ones, respectively, show excellent performance in China. The proposed method does not require any in situ meteorological information as input and thus has potential applications for high‐precision and real‐time GNSS PWV retrieval in China.