Abstract

In the Greater Beijing metropolitan area of North China (Beijing-Tianjin-Hebei areas), a mixed single- and dual-frequency ground-based GPS water vapor monitoring network is established to support operational weather forecasting and scientific research. In this paper we briefly summarize the progr... Show moreIn the Greater Beijing metropolitan area of North China (Beijing-Tianjin-Hebei areas), a mixed single- and dual-frequency ground-based GPS water vapor monitoring network is established to support operational weather forecasting and scientific research. In this paper we briefly summarize the progress of this collaborative operational GPS-Met network on network design, retrieval methods for precipitable water vapor content, slant path water vapor content, and 3D-tomography. In particular, we discuss the results of the pilot experiment using the mixed single- and dual-frequency GPS network over the area sensitive to sever weather events in the vicinity of Beijing, and the impact of retrieved atmospheric water vapor on precipitation forecasts and short-rang numerical weather prediction. Results show that the mixed network with a station separation of 5-10 km can provide accurate retrieval of single-frequency GPS observations, meeting the practical requirement after correcting the ionosphere-delay error with a high-resolution ionosphere-delay error correction model. Moreover, with specific purpose to assess the impact of GPS water vapor monitoring on precipitation prediction, typical severe rainfall weather events are analyzed in detail using GPS-Met observations, and numerical forecasting experiments with MM5/WRF modeling and data assimilation system. Analyses indicated that precipitation episode and hourly rainfall intensity are associated with a sharp increase of PW followed by a sharp decline in GPS/PWV, and the prediction of the timing, location, and intensity of two heavy rainfall events are significantly improved with the assimilation of the ground-based GPS precipitable water vapor data at the initial time. Show less