A global grid model for the vertical correction of zenith wet delay based on the sliding window algorithm

• Main Authors: HUANG Liangke, ZHU Ge, PENG Hua, CHEN Hua, LIU Lilong, JIANG Weiping
• Format: Article
• Language: zho
• Published: Surveying and Mapping Press 2021-05-01
• Series: Acta Geodaetica et Cartographica Sinica
• Subjects: sliding window algorithm; tropospheric delay model; zenith wet delay; vertical stratification
• Online Access: http://xb.sinomaps.com/article/2020/1001-1595/2021-5-685.htm
• ISSN: 1001-1595; 1001-1595

Tropospheric delay is an important error source in Global Navigation Satellite System (GNSS) positioning. Some shortages still exist in current global zenith wet delay (ZWD) vertical stratification models, such as only single gridded data as well as monthly profiles is used for modeling. To address those of drawbacks, a new approach, the sliding window algorithm, is proposed to develop the ZWD vertical stratification model. In this work, the ZWD vertical stratification model that considering seasonal variations of ZWD height scale factor is developed, named as GZWD-H model. The ZWD layered profiles from 321 radiosonde sites in 2017 are treated as reference values, to evaluate the performance of GZWD-H model in layered vertical interpolation and its application in spatial interpolation for GGOS (global geodetic observing system) atmosphere gridded ZWD. Besides, the performance of GZWD-H model is compared to the GPT2w model. The results show that GZWD-H model shows the best performance in the ZWD layered vertical interpolation against the ZWD layered profiles from globally distributed radiosonde sites. In terms of RMS, the GZWD-H model has improved by 4% and 7% compared to the GPT2w-1 and GPT2w-5 models, respectively. Compared to GPT2w-1 and GPT2w-5 models, GZWD-H model has improved by 17% and 35% in spatial interpolation for GGOS Atmosphere gridded ZWD against surface ZWD calculated from radiosonde profiles over globe, respectively. In terms of model parameters, GZWD-H model has been significantly reduced and optimized against GPT2w-1 model, thus, the applicability of this model could be enhanced in GNSS atmospheric sounding and GNSS precise position.