Merging radar and rain gauge data by using spatial–temporal local weighted linear regression kriging for quantitative precipitation estimation
Merging radar and rain gauge data by using spatial–temporal local weighted linear regression kriging for quantitative precipitation estimation
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DOI：
10.1016/j.jhydrol.2021.126612
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发表时间：
2021-10
影响因子：
6.4
通讯作者：
Zhang Guofeng;Tian Guanghui;Cai Daxin;Bai Rui;Jinhe Tong
中科院分区：
文献类型：
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作者：
Zhang Guofeng;Tian Guanghui;Cai Daxin;Bai Rui;Jinhe Tong
雷达与雨量计数据融合是获取高精度、高时空分辨率降水产品的重要手段。然而，由于雨滴尺寸分布本身的变化以及雷达观测数据的不确定性，A和Z-R（雷达反射率因子（Z）与降雨率（R））关系是空间和时间的复杂函数是不争的事实，这对雷达雨量计定量降水估算（QPE）的精度影响很大。在本研究中，为了进一步提高雷达雨量计QPE的精度，提出了时空局部加权线性回归（STLWLR）及其相应的回归克里金法（STLWLRK）来合并雷达和雨量计数据进行QPE，其中时空邻域中Z-R对的最大数量N、时空距离变换参数μ和指定阈值Cof |e/σ|是三个关键参数。具体而言，利用海南岛发生的案例和交叉验证模式对三个关键参数进行校准，并以相对平均绝对误差（RMAE）和偏差作为评价指标对两种方法进行评价。结果表明，与实时调整Z关系（AZR）方案和雷达QPE两步标定技术（AID方案）相比，参数优化的STLWLR和STLWLRK可以进一步提高雷达雨量计QPE的精度。对2018年5月1日至10月31日海南岛发生的病例以及1小时、3小时、6小时、12小时和24小时时间尺度的10倍交叉验证，与AID相比，STLWLRK的RMAE分别下降了6.3%、8.4%、8.4%、8.2%和7.9%计划。结果还表明，增加时间尺度可以减少不同方法的雷达雨量计 QPE 的误差。由于STLWLR以扎实的地理学、数学和雷达气象学为基础，并经过大量实例的检验，STLWLR及其优化参数应具有一定的普适性。总体而言，与传统方法相比，STLWLRK 是一种更稳健、更容易实现、更准确且计算成本更低的方法。
Merging radar and rain gauge data is an important means of obtaining precipitation products with high accuracy and high spatial–temporal resolution. However, due to the change of the raindrop size distribution itself and the uncertainty of the radar observational data, it is an indisputable fact thatAandbin theZ–R(radar reflectivity factor (Z) to rainfall rate (R)) relationship are complex functions of space and time, which has a great influence on the accuracy of radar–rain gauge quantitative precipitation estimation (QPE). In this study, in an effort to further improve the accuracy of radar–rain gauge QPE, spatial–temporal local weighted linear regression (STLWLR) and its corresponding regression kriging (STLWLRK) are proposed for merging radar and rain gauge data for QPE, for which the maximum number ofZ–Rpairs in the spatial–temporal neighborhoodN, the spatial–temporal distance transformation parameterμ, and the specified thresholdCof |e/σ| are three key parameters. Specifically, cases that occurred on Hainan Island and the cross-validation mode were used to calibrate the three key parameters and to evaluate the two methods with the relative mean absolute error (RMAE) and bias as evaluation indicators. The results show that compared with the real-time-adjustedZ–Rrelationship (AZR) scheme and the two-step calibration technique of radar QPE (the AID scheme), STLWLR and STLWLRK with optimization parameters could further improve the accuracy of radar–rain gauge QPE. For 10-fold cross-validation of the cases occurring on Hainan Island from 1 May to 31 October 2018 and for 1-hr, 3-hr, 6-hr, 12-hr, and 24-hr time scales, the RMAE of STLWLRK decreased by 6.3%, 8.4%, 8.4%, 8.2%, and 7.9%, respectively, compared with that of the AID scheme. The results also show that increasing time scale could reduce the error of radar-rain gauge QPE for different methods. As STLWLR is based on solid geography, mathematics, and radar meteorology and has been tested by a large number of cases, STLWLR and its optimization parameters should have certain universality. Overall, compared with traditional methods, STLWLRK is a more robust, easier to implement, more accurate, and less computationally expensive method.