This study proposes two variants of the traditional conditional merging (CM) method that merges the next-generation radar (NEXRAD) ground gauge precipitation data. The first method, named CM considering simple optimal estimation (SOE), employs a novel algorithm of simultaneously considering rainfall spatial intermittency and inner variability to replace the conventional semivariogram algorithms of the CM method. The second variant, called CM-GR-SOE, employs additional Ground-Radar rainfall ratio (so called the G/R ratio) to the CM-SOE method. Model performance was evaluated using the hourly rainfall data collected between 2004 and 2007 in the regions of Houston and Dallas in Texas. The leave-one-out cross-validation was conducted, and the relative mean error (RME) and coefficient of determination (R²) were calculated for each of the methods. In areas where the rainfall intensity was low (<0.25 mm/h), NEXRAD Stage IV, and occasionally the CM method, showed lower absolute values of RME, and higher R² values than other variants. As rainfall intensity increased (greater than 7.6 mm/h), the CM-GR-SOE method showed the best performance. Further analysis revealed that spatial correlations of rainfall field is the primary source of seasonal variability of the model performance. The analysis also revealed that the correlation between the model seasonal performance and the rainfall spatial correlation depends on the density of ground gauges. For this reason, the CM-GR-SOE method performed better at the Dallas area.

这项研究提出了传统条件合并（CM）方法的两种变体，该方法合并了下一代雷达（NEXRAD）地面测量仪降水数据。第一种方法称为考虑简单最佳估计（CME）的CM，它采用了一种同时考虑降雨空间间歇性和内部可变性的新颖算法来代替CM方法的传统半变异函数算法。第二种方法称为CM-GR-SOE，它对CM-SOE方法采用了额外的地面雷达降水比（即所谓的G / R比）。使用得克萨斯州休斯敦和达拉斯地区在2004年至2007年之间收集的每小时降雨数据评估了模型性能。进行留一法交叉验证，相对平均误差（RME）和测定系数（R ²）是针对每种方法计算的。在降雨强度较低（<0.25 mm / h）的地区，NEXRAD Stage IV（有时为CM方法）的RME绝对值较低，R ²值较高。随着降雨强度的增加（大于7.6 mm / h），CM-GR-SOE方法显示出最佳性能。进一步的分析表明，降雨场的空间相关性是模型性能季节性变化的主要来源。分析还显示，模型季节性能与降雨空间相关性之间的相关性取决于地表的密度。因此，CM-GR-SOE方法在达拉斯地区表现更好。