Improving the Detection Performance of Extreme Precipitation Observations Using a Radar–Gauge Merging Algorithm
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摘要: 高时空分辨率、高精度的降水产品对于极端降水的监测以及防灾减灾具有重要意义。地面雨量计提供点尺度降水精确观测，但无法精细化捕捉对流性强降水的空间分布。雷达观测可以精细地刻画降水的空间分布特征，但雷达定量估计降水（QPE，quantitative precipitation estimation）产品估测精度易受雷达观测偏差和Z–R（雷达反射率—降水率）关系等因素影响。因此，本文开展高时空分辨率的雷达—雨量计降水融合算法研究，集成雨量计观测和雷达定量估计降水产品各自的优点。该算法主要步骤包括：雨量站观测数据格点化、局地雨量计订正雷达QPE和雷达—雨量计降水融合三个部分。首先利用克里金插值方法，对雨量站观测的降水进行插值，得到格点降水信息；再通过局地雨量计订正方法系统性地订正雷达QPE产品，以提高雷达QPE产品精度；最后，结合降水类型，通过雷达—雨量计降水融合算法，产生高时空分辨率、高精度的雷达—雨量计降水融合产品。通过郑州“21·7”暴雨、台风“烟花”和2021年8月随州暴雨三个典型的极端降水个例，对雷达—雨量计降水融合算法产生的雷达—雨量计降水融合产品进行了系统地评估和分析。结果表明，在不同的极端降水个例和不同的降水时段，雷达—雨量计降水融合产品精度上优于雷达QPE产品，且在降水的空间分布上较雨量站观测格点插值产品更能精细地刻画降水的结构特征。表明算法得到的雷达—雨量计降水融合产品的准确性较高，对极端降水有较好地捕捉和监测能力。Abstract: Precipitation products with high spatial and temporal resolution and high accuracy are crucial for monitoring extreme precipitation events as well as preventing and mitigating disasters. Gauge station observations provide accurate point-scale precipitation but are insufficient for finely capturing spatial information on heavy precipitation induced by severe convection. Radar scanning can provide accurate precipitation information with high spatial and temporal resolution, but the accuracy of radar QPE (quantitative precipitation estimation) is vulnerable to various factors, such as observation accuracy and Z–R (radar reflectivity factor Z and rainfall rate R) relationship. Therefore, a radar–gauge merging algorithm is proposed in this paper to combine the advantages of gauge station observations and radar QPE. The algorithm includes three steps: Kriging interpolations of precipitation, LGC (local gauge-corrected) radar QPE, and radar–gauge merging QPE. First, the precipitation interpolation fields are obtained by the Kriging method based on the regional station observations. Subsequently, based on the LGC method, the accuracy of the radar QPE is improved by making systematic corrections. Finally, combined with the precipitation type, the radar–gauge merging QPE with high spatial and temporal resolution and high accuracy is produced by the radar–gauge merging algorithm. Three extreme precipitation events, namely, the 21·7 extreme precipitation in Zhengzhou, typhoon In-Fa, and the extreme precipitation in Suizhou in August 2021, are used to evaluate the performance of the radar–gauge merging algorithm. Results show that the new radar–gauge merging QPE outperforms the radar QPE product in terms of accuracy and characterizes the precipitation structure more finely than the Kriging interpolations of the gauge station observations in terms of the spatial distribution and time periods of the different extreme precipitation events. These results indicate the high accuracy and stability of the new radar–gauge merging algorithm and its ability to capture the distribution of extreme precipitation events.
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图 2 2021年7月20日09时（协调世界时，下同）河南地区（a）区域观测站降水量（单位：mm）分布，（b）基于区域观测站的克里金插值降水量（单位：mm）分布，（c）插值场与地面国家站观测1 h降水量散点图。图a、b中的圆点和星状点分别为国家站（86个）和区域站（1179个）测量的1 h降水量分布。红色方框区域为国家站观测和区域站观测插值场相差较大的区域之一
Figure 2. Precipitation (units: mm) distributions of (a) the regional gauge station observations, (b) Kriging interpolations based on the regional gauge station observations, and (c) scatterplots of 1-h precipitation (units: mm) from Kriging interpolations and the national gauge station observations in Henan area at 0900 UTC 20 July 2021. In Figs. a, b, dots and stars represent the 1-h precipitation (units: mm) from 86 national gauge stations and 1179 regional gauge stations, respectively. The red box area is one of the areas with large differences between Kriging interpolations based on the regional gauge station observations and the national gauge station observations
图 3 2021年7月20日09时河南南部（图2中红色方框区域）（a）基于区域地面观测站的克里金插值场降水量分布和（b）原始雷达QPE产品降水量（单位：mm）分布。图a、b中的圆点和星状点分别为国家站和区域站测量的1 h降水量（单位：mm）分布
Figure 3. Precipitation (units: mm) distributions of (a) Kriging interpolations based on the regional gauge station observations and (b) radar QPE (quantitative precipitation estimation) in southern Henan area (the red box in Fig. 2) at 0900 UTC 20 July 2021. In Figs. a, b, dots and stars represent the 1-h precipitation (units: mm) from national gauge stations and regional gauge stations, respectively
图 4 2021年7月20日09时河南地区雷达QPE产品降水量订正（a）前、（b）后分布，雷达QPE产品1 h降水量订正（c）前、（d）后与国家站1 h降水量散点图。图a、b中圆点为国家站测量的1 h降水量（单位：mm）分布
Figure 4. Precipitation distributions of radar QPE (a) before and (b) after LGC (local gauge-corrected), scatterplots of 1-h precipitation from radar QPE (c) before and (d) after LGC and the national gauge station observations in Henan area at 0900 UTC 20 July 2021. In Figs. a, b, dots represent 1-h precipitation (units: mm) from the national gauge stations
图 5 2021年7月20日09时河南地区（a）雷达—雨量计降水融合产品的降水量（单位：mm）分布，（b）雷达—雨量计降水融合产品的1 h降水量与国家站观测的1 h降水量的散点图。图a中圆点为国家站观测的1 h降水量分布
Figure 5. (a) Precipitation (units: mm) distribution of the radar–gauge merging QPE and (b) scatterplots of 1-h precipitation from the radar–gauge merging algorithm and the national gauge station in Henan area at 0900 UTC 20 July 2021. In Fig. a, dots represent 1-h precipitation (units: mm) from the national gauge stations
图 6 2021年7月20日09时郑州地区（a）雷达QPE、（b）区域站克里金插值场、（c）区域站订正后雷达QPE、（d）雷达—雨量计降水融合产品的降水量（单位：mm）分布。圆点和星状点分别为国家站和区域站1 h降水量（单位：mm）分布。白色框区域和黑色框区域分别表示为空间变异性大值区和奇异雨量计观测值区域
Figure 6. Precipitation (units: mm) distributions from (a) radar QPE, (b) Kriging interpolations based on the regional gauge station observations, (c) radar QPE after LGC, and (d) radar–gauge merging QPE in Zhengzhou at 0900 UTC 20 July 2021. Dots and stars represent 1-h precipitation (units: mm) from the national and regional gauge stations, respectively. The white boxed area and the black boxed area indicate the area of large spatial variability and the area of odd rain gauge observations, respectively
图 7 郑州“21·7”暴雨时间段（2021年7月20日00时至7月21日00时）（a）雷达QPE产品1 h降水量、（b）雷达—雨量计降水融合产品1 h降水量与地面国家站观测1 h降水量的散点图
Figure 7. Scatterplots of 1-h precipitation from (a) radar QPE, (b) radar–gauge merging QPE and the national gauge station observations in Zhengzhou from 0000 UTC 20 July to 0000 UTC 21 July 2021
图 8 2021年7月20日00时至22时郑州“21·7”暴雨评分指标时间序列：（a）均方根误差（RMSE）；（b）相对误差（RMAE）；（c）相对偏差（RMB）；（d）相关系数（CC）。蓝色线为雷达QPE产品，红色线为融合产品。图a中叠加了个例1研究区域（30.5°～37.5°N，109.5°～117.5°E）内所有国家站1 h降水量均值（AHP，灰色柱）
Figure 8. Time series of (a) root mean square error (RMSE), (b) root mean absolute error (RMAE), (c) relative mean bias (RMB), and (d) correlation coefficient (CC) for the precipitation event in Zhengzhou from 0000 UTC to 2200 UTC 20 July 2021. Blue lines show the radar QPE, and the red lines show the radar–gauge merging QPE. In Fig. a, gray bars represent average hourly precipitation (AHP) of all national gauge stations in the study area (30.5°–37.5°N, 109.5°–117.5°E) of the first case
图 9 2021年7月20日21时郑州地区不同降水产品的降水量（单位：mm）分布：（a）区域站克里金插值场；（b）原始雷达QPE产品；（c）区域站订正后雷达QPE产品；（d）雷达—雨量计降水融合产品。圆点和星状点分别为国家站和区域站1 h降水量（单位：mm）分布，黑色框为融合产品改善较小区域
Figure 9. Precipitation (units: mm) distributions in Zhengzhou at 2100 UTC 20 July 2021: (a) Radar QPE; (b) radar QPE after LGC; (c) Kriging interpolations based on the regional gauge station observations; (d) radar–gauge merging QPE. Dots and stars represent 1-h precipitation (units: mm) from the national and regional gauge stations, respectively. The black boxed area shows the small improvement area of radar–gauge merging QPE
图 10 台风登陆过程中（2021年7月25日21时）（a）雷达QPE产品、（b）雷达—雨量计降水融合产品的降水量（单位：mm）分布及（c、d）其1 h降水量与国家站观测1 h降水量散点图。图a、b中圆点为国家站降水量分布
Figure 10. Precipitation (units: mm) distributions of (a) radar QPE and (b) radar–gauge merging QPE and scatterplots of 1-h precipitation from (c) radar QPE, (d) radar–gauge merging QPE and the national gauge station observations during the typhoon In-Fa landfall (at 2100 UTC 25 July 2021). In Figs. a, b, dots represent the precipitation from the national gauge stations
图 11 台风“烟花”过程中（2021年7月24日07时）（a）雷达—雨量计降水融合产品降水量（单位：mm）分布及（b）其1 h降水量与国家站1 h降水量的散点图。图a中圆点为国家站1 h降水量分布
Figure 11. (a) Precipitation (units: mm) distribution of radar–gauge merging QPE and (b) scatterplots of 1-h precipitation from radar–gauge merging QPE and the national gauge station observations during the typhoon In-Fa landfall (at 0700 UTC 24 July 2021). In Fig. a, the dots represent 1-h precipitation (units: mm) from the national gauge stations
图 16 2021年8月（a、b）11日07时、（c、d）11日18时、（e、f）11日23时、（g、h）13日00时随州暴雨雷达QPE产品（左）、雷达—雨量计降水融合产品（右）的降水量（单位：mm）分布。圆点为国家站测量的1 h降水量（单位：mm）分布
Figure 16. Precipitation (units: mm) distributions of radar QPE (left) and radar–gauge merging QPE (right) for the precipitation event in Suizhou at (a, b) 0700 UTC 11, (c, d) 1800 UTC 11, (e, f) 2300 UTC 11 and (g, h) 0000 UTC 13 August 2021. The dots represent 1-h precipitation (units: mm) from the national gauge stations
表 1 极端降水个例信息
Table 1. Extreme precipitation events summary
个例 名称 时间 区域范围 特点 1 郑州“21·7”暴雨 2021年7月20日00时
（协调世界时，下同）
至21日00时（30.5°～37.5°N，109.5°～117.5°E） “列车效应”导致多个对流云团反复在郑州附近发展移动，导致降水强度大、维持时间长，引起局地极端降水天气；郑州2021年7月21日09时的小时降雨量达到了201.9 mm。 2 台风“烟花” 2021年7月22日01时
至28日09时（28.0°～34.0°N，117.0°～124.0°E） 2021年7月25日04:30前后第一次登陆，2021年7月26日01:50前后第二次登陆，最强风力为42 m s−1（14级） 。 3 2021年8月随州暴雨 2021年8月10日01时
至13日15时（30.0°～32.5°N，110.0°～115.0°E） 强降水过程总雨量大、极端性强、突发性强。降雨主要集中在随州市南部，累计雨量超过200 mm。 
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