Investigating the spatiotemporal variations of GNSS precipitable water and characteristics during drought period based on the empirical Tm model in Yunnan, China

Abstract

Precipitable water vapor (PWV) as an important parameter of atmospheric water vapor can be obtained through global navigation satellite system (GNSS), which offers high-precision observations in all-weather conditions. It is difficult for the current meteorological observing systems to measure PWV under extreme weather conditions. GNSS-derived PWV has high temporal and spatial resolutions, being a reliable and stable data source for meteorology. In this study, we establish an empirical model for the atmospheric weighted mean temperature ($T_m$) in Yunnan, China based on radiosonde data collected from 2020 to 2022. The model is verified by assessing its accuracy using measurements of four radiosondes in 2023. The results show that the empirical $T_m$ model is more accurate compared to other widely used models such as GPT2w model, Li model, and Bevis model. The root mean square (RMS) of $T_m$ obtained from the empirical models are 3.5 K, 2.3 K, 2.1 K and 2.1 K with average bias of −0.10 K, 0.22 K, 0.04 K, and 0.32 K, respectively. We use GNSS to obtain PWV and validate the GNSS-derived PWV using data from the radiosonde station in Kunming, Yunnan. The GNSS PWV is consistent with the PWV measured by the radiosondes. The RMS of the GNSS-PWV of the years 2020, 2021, and 2022 are 3 mm, 2.2 mm, and 2.9 mm, respectively, and the GNSS-PWV is confirmed to have a higher accuracy compared to others. Additionally, this study analyzes the spatiotemporal variations of GNSS PWV over Yunnan in 2021. The result shows that GNSS-derived PWV varies with geographic location and season and is significantly related to precipitation. Through the analysis of PWV during drought periods in 2021 and comparison with the same period in 2020 and 2022, we find that PWV is significantly lower in 2021 than in the same period of other years. Understanding the spatiotemporal distribution of PWV is crucial for meteorological monitoring and weather forecasting.