GPS-based precipitable water vapour retrieval and variability using measured and global reanalysis data in the coastal regions of China

doi:10.1080/01431161.2019.1624861

	GPS-based precipitable water vapour retrieval and variability using measured and global reanalysis data in the coastal regions of China
	Wang, Zhaoyang 1; Xing, Zhe 1; Zhou, Xinghua 2; Tang, Qiuhua 2; Zhou, Dongxu 2; Sun, Weikang 3
刊名	INTERNATIONAL JOURNAL OF REMOTE SENSING
	2019-06-05
ISSN号	0143-1161
DOI	10.1080/01431161.2019.1624861
英文摘要	The atmospheric water vapour is an important indicator of the climate state and evolution, and also the key parameter affecting the hydrological cycle, atmospheric convection, and the weather. This study validated the accuracy of surface pressure and temperature data interpolated from European Centre for Medium-Range Weather Forecasts (ECMWF) Interim ReAnalysis (ERA-Interim) and ECMWF 5th ReAnalysis (ERA5) datasets, and evaluated the performance of the interpolation meteorological data for precipitable water vapour (PWV) retrieval using the measured meteorological data and Global Positioning System (GPS) data of the year 2014 obtained from 25 stations of the Chinese Coastal GPS Observation Network. We then analysed the temporal and spatial distribution of water vapour in the coastal regions of China using a 3-year GPS PWV time series calculated with the interpolated reanalysis meteorological data at 25 stations from 2014 to 2016. Evaluation of the reanalysis meteorological data results showed that the accuracies of the interpolated pressure and temperature data from ERA5 were slightly superior than those from ERA-Interim. The root-mean-square errors (RMSEs) of the surface temperature and pressure interpolated from two reanalysis datasets were less than 2.4 K and 1.6 hPa, respectively. Data based on GPS PWV products that used interpolated parameters were very close to those of meteorological observations, with biases within +/- 0.4 mm and RMSEs below 0.5 mm in most areas; these data also strongly agreed with radiosonde observations. However, the interpolated meteorological data from reanalysis datasets could not reflect the true change during typhoon events, which could not be used in GPS PWV retrieval. The analysis of the temporal and spatial distribution showed that the distribution of water vapour was mainly affected by latitude, land-sea distribution, and water vapour advection. The variations in water vapour were mainly seasonal with the highest PWV occurring in the summer and the lowest always occurring in the winter. Significant diurnal variations of water vapour varied with season and latitude, with an amplitude of about 0.9-3.5 mm; the peak value occurred in the afternoon or early morning, and was affected by surface evaporation, large-scale land-sea breeze circulation, and valley-wind circulation.
资助项目	Polar Environment Comprehensive Inspection Assessment and Management[2018-4 [811142031]]
WOS关键词	METEOROLOGY ; CYCLE ; MODEL
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000472378700001
内容类型	期刊论文
源URL	[http://ir.fio.com.cn:8080/handle/2SI8HI0U/24584]
专题	自然资源部第一海洋研究所
通讯作者	Wang, Zhaoyang
作者单位	1.Natl Marine Data & Informat Serv, Marine Environm Geog Ctr, Tianjin, Peoples R China 2.Minist Nat Resources, Inst Oceanog 1, Marine Engn Environm & Geomat Ctr, Qingdao, Shandong, Peoples R China 3.Shandong Univ Sci & Technol, Geomat Coll, Qingdao, Shandong, Peoples R China
推荐引用方式 GB/T 7714	Wang, Zhaoyang,Xing, Zhe,Zhou, Xinghua,et al. GPS-based precipitable water vapour retrieval and variability using measured and global reanalysis data in the coastal regions of China[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2019.
APA	Wang, Zhaoyang,Xing, Zhe,Zhou, Xinghua,Tang, Qiuhua,Zhou, Dongxu,&Sun, Weikang.(2019).GPS-based precipitable water vapour retrieval and variability using measured and global reanalysis data in the coastal regions of China.INTERNATIONAL JOURNAL OF REMOTE SENSING.
MLA	Wang, Zhaoyang,et al."GPS-based precipitable water vapour retrieval and variability using measured and global reanalysis data in the coastal regions of China".INTERNATIONAL JOURNAL OF REMOTE SENSING (2019).