Evaluation of the China Meteorological Administration Weather Radar Mosaic System V3.0 Hourly Precipitation Product Based on A Network of Rain Gauges: A Case Study of Guangxi Zhuang Autonomous Region
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摘要: 准确监测和预报降水对于减灾防灾、水资源规划以及应对全球变化具有重要意义。最新升级的中国气象局天气雷达拼图系统V3.0在降水业务监测中发挥着重要作用，但尚缺乏针对降水产品的精细化检验。以广西壮族自治区为例，利用国家标准气象站的雨量计观测数据，对2023年1月1日—12月31日的天气雷达拼图系统V3.0逐小时降水产品进行评估，为产品的优化升级提供科学依据，结果表明：（1）基于逐小时定量降水估测产品的年降水量分布情况与雨量计观测结果一致，但空间局部存在系统性偏差，对北部湾沿岸地区年降水量高估约500 mm，同时低估了百色市周围地区的降水。（2）逐小时定量估测降水产品对不同等级的降水事件频次的估测与雨量计观测结果较为一致；在小雨级别中表现良好，在强降水事件中表现为低估。（3）降水产品的精度在空间上呈现出一定差异，在广西东北部的桂林及其周边地区、西部百色等地的误差较高，相对偏差接近60%，而在北部湾沿岸地区的模拟表现相对更好。Abstract: Accurate precipitation monitoring and forecasting are essential for disaster mitigation, water resource management, and addressing global climate challenges. The China Meteorological Administration's upgraded Weather Radar Mosaic System V3.0 (CWRMS V3.0) plays a pivotal role in operational precipitation monitoring, yet comprehensive validation of its quantitative precipitation estimation (QPE) products remains limited. Focusing on the Guangxi Zhuang Autonomous Region, this study evaluates the system's 1-hour QPE products from 1 January to 31 December, 2023, using rain gauge observations from national meteorological stations. The results reveal three key findings: (1) The annual precipitation spatial distribution derived from QPE generally aligns with observations but exhibits distinct regional biases, including overestimation of approximately 500 mm in the coastal areas of the Beibu Gulf and an underestimation around Baise City. (2) The hourly QPE products demonstrate that the system has robust detection capabilities across various precipitation intensities, correlating well with the rain gauge records. However, while performance is satisfactory for light rains, the system exhibits a pronounced negative bias for heavy precipitation events. (3) Spatial disparities in accuracy are evident, with notably high errors (relative bias up to 60%) in northeastern Guilin and western Baise, contrasting with relatively lower errors observed in coastal regions.
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表 1 误差指标计算公式
误差指标 公式 CC $ \frac{\sum_{i=1}^{N}\left(X_i-\bar{X}\right)\left(Y_i-\bar{Y}\right)}{\sqrt{\sum_{i=1}^{N}\left(X_i-\bar{X}\right)^2}\sqrt{\sum_{i=1}^{N}\left(Y_i-\bar{Y}\right)^2}} $ (2) RMSE $ \sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(X_i-Y_i\right)^2} $ (3) RB $ \frac{\sum_{i=1}^{N}\left(X_i-Y_i\right)}{\sum_{i=1}^{N}Y_i} \times 100\% $ (4) 
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表 2 降水等级划分
等级 划分标准/(mm·h-1) 雨量计观测事件/次 0级 < 0.1 689 004 1级 0.1~2.0 51 659 2级 2.0~5.0 7 788 3级 5.0~10.0 3 448 4级 10.0~20.0 1 709 5级 > 20.0 770
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表 4 POD、FAR、CSI计算公式
指标 计算公式 POD $ \frac{\rm TP}{\rm TP + \rm FN} $ (5) FAR $ \frac{\rm FP}{\rm TP + \rm FP} $ (6) CSI $ \frac{\rm TP}{\rm TP + \rm FP + \rm FN} $ (7)
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表 5 不同降水等级下的误差指标
指标 1级 2级 3级 4级 5级 CC 0.66 0.25 0.13 0.20 0.35 RMSE/mm 2.67 1.88 4.04 10.16 18.25 RB/% 0.64 22.89 -11.36 -30.48 -32.49
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表 6 多种降水产品的统计指标
指标 1 h定量估测降水产品 GSMaP IMERG CMPAS POD 0.68 0.25~0.45 0.25~0.45 > 0.65 FAR 0.57 > 0.65 0.45~0.65 < 0.25 CSI 0.36 < 0.25 0.25~0.45 > 0.45
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