Abstract

The latest reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF), ERA5, can provide atmospheric data for calculating Zenith Tropospheric Delay (ZTD) with hourly temporal resolution, which is a key factor in Global Navigation Satellite System (GNSS) high-precision application. This paper is aimed at evaluating the performance of ZTD derived from ERA5 reanalysis data over China using 219 GNSS stations of the Crustal Movement Observation Network of China (CMONOC) covering the period from 2015 to 2016. The site-specific hourly ZTD at these stations is obtained by integration method and Saastamoinen model method on ERA5 pressure-level and surface-level reanalysis data with the temporal resolution of 1 h and the spatial resolution of 0.25° × 0.25°. Firstly, the atmospheric temperature and pressure that derived from ERA5 are compared with temperature and pressure obtained from meteorological sensors available at 193 GNSS stations. The biases are 2.31 °C and 1.26 mbar implying the accuracy and feasibility of ERA5 pressure and temperature for calculating ZTD over China. Secondly, the performance of ERA5 ZTD is systematically evaluated using ZTD from 219 GNSS sites. The average bias and Root Mean Square (RMS) of ERA5 pressure-level ZTD at all test stations in integration method are approximately 2.97 mm and 11.49 mm respectively, and those of ERA5 surface-level ZTD in model method are 7.97 mm, 39.25 mm, which indicates that ERA5 pressure-level ZTD has a higher accuracy over China. Further analysis indicates that the accuracies of ZTD derived from ERA5 pressure-level and surface-level data are approximately 13.8% and 10.9% higher than those from of ERA-Interim pressure-level and surface-level data. Moreover, ERA5 is able to accurately capture the short-term (hourly) variation of ZTD, which further indicates the better performance of ERA5. Thirdly, the temporal and spatial variation characteristics of ERA5 ZTD accuracy are further analyzed over China. The results show that the ZTD in the southern region has the lower accuracy compared with that in the northern region over China due to the influence of latitude and altitude. Furthermore, it is found that the ERA5 ZTD over China has obvious seasonality, with higher accuracy in winter and lower accuracy in summer.

Highlights

  • Tropospheric effect is a major error source in precise global navigation satellite system (GNSS) positioning [1]

  • The products of Numerical Weather Model (NWM) have been used to build global or regional temperature, pressure and Zenith Tropospheric Delay (ZTD) models by scholars, such as Global Pressure and Temperature (GPT), GPT2, Global Pressure and Temperature 2 wet (GPT2w), Improved Tropospheric Grid (ITG), etc. [11,12,13,14,15], which implies that NMW data play a vital role in the field of geodesy and GNSS meteorology

  • The main data used in this paper are the ERA5 reanalysis data from European Center for Medium-Range Weather Forecasts (ECMWF) and GNSS observations collected from the Crustal Movement Observation Network of China (CMONOC) during the two-year period from 2015 to 2016

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Summary

Introduction

Tropospheric effect is a major error source in precise global navigation satellite system (GNSS) positioning [1]. High-precision tropospheric delay product is critical for GNSS to improve the accuracy of positioning and water vapor retrieving [5,6]. [11,12,13,14,15], which implies that NMW data play a vital role in the field of geodesy and GNSS meteorology These further applications are based on the high-accuracy parameters derived from NMW data such as pressure, temperature and ZTD. In this paper the goals we want to achieve are: (1) to assess the accuracy of atmospheric temperature and pressure provided by ERA5 by comparing with those from GNSS meteorological data;(2) to evaluate the performance of ERA5 ZTD by comparing with GNSS ZTD; and (3) to analyze the temporal and spatial variation characteristics of ERA5 ZTD over China.

Data Description
Reanalysis Products
GNSS Observations
Calculating ZTD Using ERA5 Meteorological Data at GNSS Stations
Deriving ZTD from GNSS
Temperature and Pressure Evaluation
Evaluation of ERA5ZTD
Conclusions
Full Text
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