A new zenith hydrostatic delay model for real-time retrievals of GNSS-PWV

Suqin Wu,Wang Li,Xiaoming Wang,Kefei Zhang,Qimin He,Moufeng Wan,Longjiang Li,Zhen Shen,Dantong Zhu

doi:10.5194/amt-14-6379-2021

Abstract

Abstract. The quality of the zenith hydrostatic delay (ZHD) could significantly affect the accuracy of the zenith wet delay (ZWD) of the Global Navigation Satellite System (GNSS) signal, and from the ZWD precipitable water vapor (PWV) can be obtained. The ZHD is usually obtained from a standard model – a function of surface pressure at the GNSS station. When PWV is retrieved from the GNSS stations that are not equipped with dedicated meteorological sensors for surface pressure measurements, blind models, e.g., the global pressure and temperature (GPT) models, are commonly used to determine the pressures for these GNSS stations. Due to the limited accuracies of the GPT models, the ZHD obtained from the model-derived pressure value is also of low accuracy, especially in mid- and high-latitude regions. To address this issue, a new ZHD model, named GZHD, was investigated for real-time retrieval of GNSS-PWV in this study. The ratio of the ZHD to the zenith total delay (ZTD) was first calculated using sounding data from 505 globally distributed radiosonde stations selected from the stations that had over 5000 samples. It was found that the temporal variation in the ratio was dominated by the annual and semiannual components, and the amplitude of the annual variation was dependent upon the geographical location of the station. Based on the relationship between the ZHD and ZTD, the new model, GZHD, was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable. The 20-year (2000–2019) radiosonde data at 558 global stations and the 9-year (2006–2014) COSMIC-1 (Constellation Observing System for Meteorology, Ionosphere, and Climate) data, which were also globally distributed, were used as the training samples of the new model. The GZHD model was evaluated using two sets of references: the integrated ZHD obtained from sounding data and ERA5 reanalysis data. The performance of the new model was also compared with GPT3, the latest version. Results showed the new model outperformed GPT3, especially in mid- and high-latitude regions. When radiosonde-derived ZHD was used as the reference, the accuracy, which was measured by the root mean square error (RMSE) of the samples, of the GZHD-derived ZHD was about 21 % better than the GTP3-derived ones. When ERA5-derived ZHD was used as the reference, the accuracy of the GZHD-derived ZHD was about 30 % better than GPT3-derived ZHD. In addition, the real-time PWV derived from 41 GNSS stations resulting from GZHD-derived ZHD was also evaluated, and the result indicated that the accuracy of the PWV was improved by 21 %.

Highlights

Water vapor plays an important role in both the energy budget and hydrological cycle of the earth, it only makes up ∼0.1 %–4 % of the atmosphere
When zenith hydrostatic delay (ZHD)-ERA5 was used as the reference, ZHD-GPT3 agreed well with ZHD-ERA5 and much better than the new model developed in this study since GPT3 was based on ERA-Interim data
The biases and root mean square error (RMSE) of ZHD-GZHD were smaller than those of ZHD-GPT3 at most radiosonde stations, which was similar to the results of Sect. 3.1, and Fig. 12 shows the same results as Fig. 8. These results indicate that GZHD outperformed GPT3 in 2020 during which no data were used in the construction or training of the new model, i.e., the test data were out-of-sample data

Summary

Introduction

Water vapor plays an important role in both the energy budget and hydrological cycle of the earth, it only makes up ∼0.1 %–4 % of the atmosphere. Accurate acquisition of water vapor is critical for both weather forecasting and climatology. During the last three decades, Global Navigation Satellite System (GNSS) has been used to retrieve precipitation water vapor (PWV) due to its high spatial-temporal resolution and all-weather, nearly real-time, high-accuracy, and low-cost features. The usual procedure for obtaining GNSS-derived PWV is as follows (Bevis et al, 1992): (1) estimating the zenith total delay (ZTD) of GNSS signals for each GNSS station; (2) using an empirical or standard model together with surface meteorological measurements to calculate the zenith hydrostatic delay (ZHD) for the GNSS station, subtracting it from the ZTD to obtain the zenith wet delay (ZWD) of the GNSS signals for the station; and (3) converting the ZWD into PWV by multiplying the ZWD by a conversion factor which is a function of the water-vapor-weighted mean temperature (Tm) at the station. The accuracies of the three types of models were analyzed in several works (Wang et al, 2016; Zhang et al, 2017)

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Results

Conclusion