Abstract:Based on precipitable water vapor received from ground-based GPS, Doppler radar, ground automatically stations observation, two stages of a heavy rainfall in Hebei province is comparatively studied. The results show that: (1) the first stage of convective precipitation is in the evening of 23 June, whose precipitation intensity is stronger but lasting time is shorter; the main affecting system is high-altitude cold vortex, the southeast quadrant of which is the corresponding precipitation area. The main radar echo classification is convective, moving from northwest to southeast, and the echo intensity is more than 50 dBz; the value of VIL and ET is higher than the indicators of convective weather in Hebei province; heavy rainfall is corresponding with the peak of GPS PWV, while the other peak is usually occurred 6 h before precipitation. It experienced a rapid rise before the rainfall, and a fall phases after the rainfall; it shows a clear clockwise rotation in the GPS spatial distribution, and the center of a large value is corresponding with the heavy precipitation area. (2) The second stage on 24 June is mostly stratiform precipitation, whose lasting time is longer; the major affecting system is low-altitude shear line and easterly winds. The stratiform rain echoes move along the steering flow of 700 hPa, and the height of east wind is from the ground to 2 km. In this stage, the value of GPS precipitable water maintained at high-value level, especially in the west of Hebei province; the contours of GPS precipitable water vapor move from north to south in the spatial distribution, which is related with easterly winds and the Taihang Mountain terrain. (3) In contrast to the liquid and gaseous water, the emergence of peak of VIL and GPS PWV is generally the same, both of which can reflect the time and nature of precipitation; the time of VIL and precipitation is just fit, and the evolution trend of GPS PWV can indicate the starting, continuing and ending time in the precipitation, which can provide a reference for forecasting.