Hourly PWV Dataset Derived from GNSS Observations in China.

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ID: 78327
2019
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Abstract
The rapid variation of atmospheric water vapor is important for a regional hydrologic cycle and climate change. However, it is rarely investigated in China, due to the lack of a precipitable water vapor (PWV) dataset with high temporal resolution. Therefore, this study focuses on the generation of an hourly PWV dataset using Global Navigation Satellite System (GNSS) observations derived from the Crustal Movement Observation Network of China. The zenith total delay parameters estimated by GAMIT/GLOBK software are used and validated with an average root mean square (RMS) error of 4-5 mm. The pressure (P) and temperature (T) parameters used to calculate the zenith hydrostatic delay (ZHD) and weighted average temperature of atmospheric water vapor () are derived from the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting (ECMWF ERA5) products. The values of P and T at the GNSS stations are obtained by interpolation in the horizontal and vertical directions using empirical formulas. is calculated at the GNSS stations using the improved global pressure and temperature 2 wet (IGPT2w) model in China with an RMS of 3.32 K. The interpolated P and T are validated by interpolating the grid-based ERA5 data into radiosonde stations. The average RMS and bias of P and T in China are 2.71/-1.11 hPa and 1.88/-0.51 K, respectively. Therefore, the error in PWV with a theoretical RMS of 1.85 mm over the period of 2011-2017 in China can be obtained. Finally, the hourly PWV dataset in China is generated and the practical accuracy of the generated PWV dataset is validated using the corresponding AERONET and radiosonde data at specific stations. Numerical results reveal that the average RMS values of the PWV dataset in the four geographical regions of China are less than 3 mm. A case analysis of the PWV diurnal variations as a response to the EI Niño event of 2015-2016 is performed. Results indicate the capability of the hourly PWV dataset of monitoring the rapid water vapor changes in China.
Reference Key
zhao2019hourlysensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhao, Qingzhi;Yang, Pengfei;Yao, Wanqiang;Yao, Yibin;
Journal sensors
Year 2019
DOI
E231
URL
Keywords
era5 gnss pwv diurnal variation antioxidant safety tio2 sunscreen oxisol spf booster molecular combination

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