基于两类CMA模式的西南涡暖区暴雨预报差异评估

Evaluation of Forecast Differences for Warm-Sector Heavy Rainfall Associated with Southwest Vortex Based on CMA Models

  • 摘要: 为了评估CMA系列模式在四川地区西南涡暴雨过程中的预报效果,本文利用目标对象检验法,对比分析了CMA-MESO和CMA-GFS模式预报不同类型西南涡暖区暴雨的能力差异。结果表明:(1)对于九龙涡型西南涡暖区暴雨过程,CMA-MESO模式的降水空间预报能力更稳定,参考价值优于CMA-GFS;(2)对于盆地涡型西南涡暖区暴雨过程,虽然CMA-GFS模式降水空间预报较CMA-MESO略稳定,但CMA-MESO空间检验总评分更高;(3)CMA-GFS模式在不同类型西南涡暖区暴雨过程中,对大雨以上量级降水的雨带位置、走向预报均与实况较为一致,但对九龙涡型西南涡暖区暴雨过程的降水均值、极值及面积预报均较实况偏小;(4)CMA-MESO大部分时段对大雨以上量级降水的雨带位置、均值和极值强度预报与实况基本一致,但面积预报易较实况偏小;(5)两类模式对高度场的预报效果最好,风场次之,低层温度、湿度场较差。

     

    Abstract: In order to evaluate the forecast performance of CMA models in heavy rainfall processes associated with southwest vortex in Sichuan, using the object-based verification, the capabilities of the CMA-MESO and CMA-GFS models in predicting different types of warm-sector heavy rainfall events related to southwest vortex are compared and analyzed. The results show that: (1) For the Jiulong vortex-type warm-sector heavy rainfall events, the CMA-MESO model shows more stable spatial precipitation forecasting capability, offering higher reference value than the CMA-GFS model. (2) For the basin vortex-type warm-sector heavy rainfall events, although the CMA-GFS model shows slightly more stable spatial precipitation forecasting than CMA-MESO, the overall spatial verification score of CMA-MESO is higher. (3) For different types of warm-sector heavy rainfall events associated with southwest vortex, the CMA-GFS model forecasts the location and orientation of rainbands for heavy rainfall (≥25 mm) in relatively good agreement with observations. However, it underestimates the mean precipitation, peak intensity, and coverage area for the Jiulong vortex-type events. (4) For most periods, the CMA-MESO model generally forecasts the location, mean intensity, and peak intensity of heavy rainfall (≥25 mm) in close agreement with observations, though it tends to underestimate the coverage area. (5) The two models have the best prediction performance for the height field, followed by the wind field, while their performance in forecasting low-level temperature and humidity fields is relatively poor.

     

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