Accurate and reliable measurement and prediction of the spatial and temporal distribution of rain field over a wide range of scales are important topics in hydrologic investigations. In this study, a geostatistical approach was adopted. To estimate the rainfall intensity over a study domain with the sample values and the spatial structure from the radar data, the cumulative distribution functions (CDFs) at all unsampled locations were estimated. Indicator kriging (IK) was used to estimate the exceedance probabilities for different preselected threshold levels, and a procedure was implemented for interpolating CDF values between the thresholds that were derived from the IK. Different probability distribution functions of the CDF were tested and their influences on the performance were also investigated. The performance measures and visual comparison between the observed rain field and the IK-based estimation suggested that the proposed method can provide good results of the estimation of indicator variables and is capable of producing a realistic image.

在广泛的尺度上，准确，可靠地测量和预测雨场的时空分布是水文调查的重要课题。在这项研究中，采用了地统计方法。为了用雷达数据的样本值和空间结构估算研究范围内的降雨强度，估算了所有未采样位置的累积分布函数（CDF）。指标克里金法（IK）用于估计不同的预选阈值水平的超出概率，并实施了在IK衍生的阈值之间插值CDF值的过程。测试了CDF的不同概率分布函数，并研究了它们对性能的影响。