The performance of a numerical weather prediction model using convective-scale ensemble data assimilation with ground-based global navigation satellite systems-zenith total delay (ZTD) and radar data is investigated on a heavy rainfall event that occurred in Taiwan on 10 June 2012. The assimilation of ZTD and/or radar data is performed using the framework of the WRF local ensemble transform Kalman filter with a model grid spacing of 2 km. Assimilating radar data is beneficial for predicting the rainfall intensity of this local event but produces overprediction in southern Taiwan and underprediction in central Taiwan during the first 3 h. Both errors are largely overcome by assimilating ZTD data to improve mesoconvective-scale moisture analyses. Consequently, assimilating both the ZTD and radar data show advantages in terms of the location and intensity of the heavy rainfall. Sensitivity experiments involving this event indicate that the impact of ZTD data is improved by using a broader horizontal localization scale than the convective scale used for radar data assimilation. This optimization is necessary in order to consider more fully the network density of the ZTD observations and the horizontal scale of the moisture transport by the southwesterly flow in this case.

We present a comparison of atmospheric precipitable water vapor (PWV) derived from ground-based global positioning system (GPS) receiver with traditional radiosonde measurement and very long baseline interferometry (VLBI) technique for a five-year period (2008–2012) using Australian GPS stations. These stations were selectively chosen to provide a representative regional distribution of sites while ensuring conventional meteorological observations were available. Good agreement of PWV estimates was found between GPS and VLBI comparison with a mean difference of less than 1 mm and standard deviation of 3.5 mm and a mean difference and standard deviation of 0.1 mm and 4.0 mm, respectively, between GPS and radiosonde measurements. Systematic errors have also been discovered during the course of this study, which highlights the benefit of using GPS as a supplementary atmospheric PWV sensor and calibration system. The selected eight GPS sites sample different climates across Australia covering an area of approximately 30° NS/EW. It has also shown that the magnitude and variation of PWV estimates depend on the amount of moisture in the atmosphere, which is a function of season, topography, and other regional climate conditions.

Unusual decreases of water levels were consistently observed in 78% (= 42/54) of wells in the Choshuichi Alluvial Fan of central Taiwan about 250 days before the Chi-Chi earthquake (M = 7.6 on 20 September 1999) while possible factors of barometric pressure, earth tides, precipitation as well as artificial pumping were removed. Variations in groundwater levels measured on anomalous wells from 1 August 1997 to 19 September 1999, which covers the 250 day unusual decreases, were transferred into the frequency domain to unveil frequency characteristics associated with the Chi-Chi earthquake. Analytical results show that amplitudes at the frequency band between 0.02 day⁻¹ and 0.04 day⁻¹ generally maintained at the low stage and were apparently enhanced a few weeks before the Chi-Chi earthquake. Variations of amplitude at this particular frequency band were further examined along with other Taiwan earthquakes (M > 6) from 1 August 1997 to 31 December 2009. Features of the enhanced amplitudes at the frequency band are consistently observed prior to the other two earthquakes (the Rei-Li and Ming-Jian earthquakes) during the 12.5 yr study period. In addition, surface displacements recorded from GPS, which provides insights into understanding stress status in subsurface during the Chi-Chi earthquake, are also inspected. The result confirms that abnormal rise and fall changes in groundwater level yield an agreement with forward and backward surface displacements around the epicentre prior to the Chi-Chi earthquake.