Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China

Abstract

GPS has become a very effective tool to remotely sense precipitable water vapor (PWV) information, which is important for weather forecasting and nowcasting. The number of geodetic GNSS stations set up in China has substantially increased over the last few decades. However, GPS PWV derivation requires surface pressure to calculate the precise zenith hydrostatic delay and weighted mean temperature to map the zenith wet delay to precipitable water vapor. GPS stations without collocated meteorological sensors can retrieve water vapor using standard atmosphere parameters, which lead to a decrease in accuracy. In this paper, a method of interpolating NWP reanalysis data to site locations for generating corresponding meteorological elements is explored over China. The NCEP FNL dataset provided by the NCEP (National Centers for Environmental Prediction) and over 600 observed stations from different sources was selected to assess the quality of the results. A one-year experiment was performed in our study. The types of stations selected include meteorological sites, GPS stations, radio sounding stations, and a sun photometer station. Compared with real surface measurements, the accuracy of the interpolated surface pressure and air temperature both meet the requirements of GPS PWV derivation in most areas; however, the interpolated surface air temperature exhibits lower precision than the interpolated surface pressure. At more than 96% of selected stations, PWV differences caused by the differences between the interpolation results and real measurements were less than 1.0 mm. Our study also indicates that relief amplitude exerts great influence on the accuracy of the interpolation approach. Unsatisfactory interpolation results always occurred in areas of strong relief. GPS PWV data generated from interpolated meteorological parameters are consistent with other PWV products (radio soundings, the NWP reanalysis dataset, and sun photometer PWV data). The differences between them were approximately 1~3 mm at most at our selected stations, and GPS data processing is the main factor influencing the agreement of the GPS PWV results with those of other methods.

Preliminary Evaluation of the Atmospheric Infrared Sounder Water Vapor Over China Against High‐Resolution Radiosonde Measurements

“…Moreover, the mean MB and RMSE values of the four stations are 1.77 and 3.97 mm, respectively. With the exception of the MB value of the Changchun station (−0.83 mm), the MB values of all stations indicate that the GPS PWV is slightly higher than the radiosonde PWV because of the difference in the length of the data used, the available PWV data at each station, and the different detection techniques adopted (Jiang et al, ).…”

GPS PPP‐derived precipitable water vapor retrieval based on Tm/Ps from multiple sources of meteorological data sets in China

“…Many efforts have been made to retrieve GPS-PWV by using T m and P s data from various sources [Bock et al, 2007;Bordi et al, 2015;Liou et al, 2001;Liu et al, 2005;Means and Cayan, 2013;Jiang et al, 2016]. Quinn and Herring [1996] examined the possibility of using the P s data obtained from National Centers for Environmental Prediction (NCEP) Reanalysis I to retrieve GPS-PWV data at a few International GPS Service (IGS) stations.…”

A Data-Driven Approach for Accurate Rainfall Prediction

“…There are different empirical models that can be used to derive ZHD, such as the Saastamonien equation, VMF1 model or Static model. The Saastamonien equation uses pressure values to calculate the hydrostatic delays [19]. The actual pressure and temperature values are not readily available for all the GPS stations.…”