CMA-MESO与ZJ3KM模式对浙江省短时强降水预报性能检验分析

Refined performance of CMA-MESO and ZJ3KM in forecasting short-term heavy precipitation over Zhejiang Province

摘要

为深入评估中国气象局中尺度天气数值预报系统 (China Meteorological Administration Mesoscale Model，CMA-MESO) 与浙江快速更新同化预报系统 (Zhejiang WRF-ADAS Rapid Refresh System，ZJ3KM) 对浙江省短时强降水的精细化预报性能，基于自动气象站观测降水和模式预报资料，针对 2023 年 6—9 月五类天气背景下短时强降水 (冷切型、暖切型、高空槽型、热带气旋型、副高控制型)，综合运用过程检验、邻域空间检验 FSS (Fractions Skill Score) 评分、MODE (Method for Object-Based Diagnostic Evaluation) 检验以及 TS 评分等传统检验方法，从降水强度落区、频率、日变化特征、空间结构等多方面系统评估两模式的预报性能。结果表明：(1) 落区上，CMA-MESO 对暖切型和热带气旋型短时强降水的落区与频率预报效果更优，ZJ3KM 在冷切型和副高控制型的降水总量、频率及强度空间分布上更具优势，两模式对高空槽型短时强降水的预报均存在较大偏差。(2) 日变化特征方面，CMA-MESO 整体接近实况但低估副高控制型降水，ZJ3KM 普遍高估降水频率且峰值时间偏早。(3) 传统检验显示，CMA-MESO 在热带气旋型的 TS 评分更高、空报率更低，ZJ3KM 在副高控制型表现更优且多数类型下命中率更高、范围偏差更小。(4) 空间检验表明，CMA-MESO 在热带气旋型占优，ZJ3KM 在其余类型降水强度指示性更好但落区存在偏差。两种模式均存在强降水对象识别不足的问题，CMA-MESO 在冷、暖切型的空间匹配度更好，ZJ3KM 在其他三类中表现更优；位置偏差方面，除冷切型外 CMA-MESO 多偏东偏南，ZJ3KM 多偏东偏北。该研究结果可为 CMA-MESO 和 ZJ3KM 模式更好地应用在浙江地区短时强降水预报业务提供参考。

Abstract

To better understand the forecasting performance of the China Meteorological Administration Mesoscale Model (CMA-MESO) and the Zhejiang WRF-ADAS Rapid Refresh System (ZJ3KM) for short-term heavy precipitation in Zhejiang Province. The analysis is based on short-term heavy precipitation events associated with five typical synoptic patterns—cold shear, warm shear, upper-level trough, tropical cyclone, and subtropical high—during June–September 2023. This study systematically assesses the two models using precipitation process verification, traditional verification methods, as well as FSS (Fractions Skill Score) and MODE (Method for Object-Based Diagnostic Evaluation). Model performance is assessed in terms of rainfall intensity, spatial distribution, diurnal cycle, and structural characteristics.The results are as follows: (1) CMA-MESO performs better in capturing the rainfall coverage and frequency for warm-shear and tropical cyclone types, whereas ZJ3KM exhibits superior skill in simulating the total precipitation, frequency, and intensity distributions for cold-shear and subtropical high types. Both models show pronounced biases for upper-level trough cases. (2) Regarding diurnal variation, CMA-MESO generally matches observations but underestimates precipitation in the subtropical high type, whereas ZJ3KM tends to overestimate precipitation frequency and advance the timing of peak rainfall. (3) CMA-MESO achieves a higher Threat Score (TS) and lower false alarm rate for the tropical cyclone type, whereas ZJ3KM performs better for the subtropical high type and achieves higher hit rates and smaller domain biases for most precipitation types. (4) Spatial verification indicates that CMA-MESO performs better for tropical cyclone, while ZJ3KM provides superior precipitation intensity indication for other types, albeit with location biases. Both models show insufficient identification of heavy precipitation objects. Regarding spatial matching, CMA-MESO demonstrates better performance for cold- and warm-shear cases, whereas ZJ3KM excels in the remaining three categories. For position bias, except in cold shear, CMA-MESO tends to have eastward and southward deviations, while ZJ3KM shows eastward and northward biases. Overall, optimal operational application should integrate the strengths of both models--leveraging of trend evolution information CMA-MESO, and intensity of ZJ3KM and local small-scale details, while applying targeted corrections to mitigate their respective location biases.

关键词

数值预报模式 / 短时强降水 / 精细化检验 / FSS / MODE

Key words

numerical weather prediction systems / short-term heavy precipitation / refined verification analysis / FSS / MODE

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基金资助

浙江省“尖兵”“领雁”研发攻关计划项目(2024C03256)

浙江省自然科学基金联合基金项目(LZJMY23D050004，LZJMZ23D050003，LZJMY24D050007)

国家自然科学基金青年项目(42305010)

中国气象局复盘总结专项(FPZJ2024-053)