Atmospheric Profiles Derived From GPS Occulation

Over the past 100 years, Taiwan was hit by 3 to 4 typhoons per year. Typhoons that produce heavy precipitation and strong winds may cause severe damage to agriculture and industry, and serious loss of human life. The heavy rainfall associated with Mei-Yu fronts also pose a serious threat to Taiwan, but with somewhat less degree than that associated with typhoons. While it is still impossible to divert or diminish a typhoon or heavy rainfall events associated with Mei-Yu fronts for the purpose of reducing the damage, it is possible to forecast to some degree the associated track, intensity, and precipitation through numerical weather prediction (NWP) models. Many issues must be properly handled for improving NWP models. For example, it is required to incorporate mesoscale observations into NWP models through advanced data assimilation techniques in order to improve typhoons and Mei-Yu fronts forecasting. However, mesoscale observations of PW that plays a crucial role in atmospheric dynamics are generally insufficient.

In this paper, we present a newly developed scheme for sensing precipitable water (PW). It is to utilize ground based Global Positioning System (GPS) receivers, and surface meteorological measurements of temperature and pressure. Spatial and temporal revolutions of PW associated with severe weather systems are monitored by ground based GPS receivers deployed at the weather stations of Central Weather Bureau of Taiwan. The severe weather systems of concern include Mei-Yu fronts in May-June, and Typhoons Zeb and Babs in late October 1998. GPS-observed PW is compared with that measured by radiosondes at the Taipei Weather Station and that predicted by the Penn State-NCAR non-hydrostatic mesoscale model version 5 (MM5). Consistency in the PW time series among GPS and radiosonde observations, and MM5 predictions is observed. In addition, it is found that the GPS scheme captures the extremes of the PW amount better than the MM5. These results suggest that the sensed PW from the GPS approach is reliable, and can be assimilated into NWP models for further analysis in improving forecasts of severe weather systems.