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台风“海葵”降水非对称性特征的多模式预报检验及偏差原因分析

Multi-model forecast evaluation and bias analysis of asymmetric precipitation associated with Typhoon “Haikui”

  • 摘要: 为了解不同数值模式对台风非对称性降水的预报偏差,合理使用数值模式台风降水预报结果,基于三源融合降水产品(CMPA),以2023年11号台风“海葵”为例,对中国3个高分辨率区域模式(CMA-MESO3、CMA-SH9、CMA-GD3)9月3—5日小时尺度非对称性降水精细时空特征的预报能力进行评估,并探讨台风移动方向和速度、垂直风切变(Vertical Wind Shear,VWS)等对降水预报偏差的影响。主要结论如下:(1)3个模式均能合理预报出9月3日降水高值区向台风中心移动的径向演变,4日后模式预报能力下降。而降水沿方位角的演变预报则相反:3日预报偏差较大,4日后较为准确。沿台风移动方向的降水预报偏差随着台风减弱而增大,而沿VWS方向的降水预报偏差在台风演变各阶段均较为明显。(2)3个模式对台风经过台湾岛(登陆广东、福建)期间的降水非对称指数(Precipitation Asymmetry Index, PAI)一致偏高(偏低),而台风移动特征和VWS对降水非对称分布预报偏差的影响因模式而异。CMA-SH9对VWS方向预报较好,PAI演变预报偏差小;CMA-MESO3偏差主因是台风移动方向和速度预报偏差,CMA-GD3偏差是受台风移动方向、速度和VWS预报偏差共同影响。研究结果可为台风降水业务预报提供偏差订正方向,也可为模式改进提供参考。

     

    Abstract: To clarify the forecast biases in TC precipitation and ensure the appropriate application of high-resolution regional numerical model products, this study assesses the capability of three high-resolution regional operational models in China to predict the precipitation associated with TC Haikui (2311) based on the high-resolution hourly CMPA precipitation product on 3-6 September 2023 released by the China Meteorological Administration. Specifically, we evaluate their performance in capturing the spatial asymmetry, hourly evolution characteristics such as radial, azimuthal, vertical wind shear direction, and moving direction and speed of the TC. Furthermore, the possible causes of forecast biases are discussed. Main results are as follows: (1) The three models reasonably forecasted the inward movement of the precipitation center toward the TC core on 3 September, while the forecast performance decreases from 4 September. In contrast, the TC azimuthal precipitation forecast exhibits the opposite trend: forecast skill is lower on 3 September but higher from 4 September onward. Precipitation forecast bias along the TC motion direction increases as the typhoon weakens, whereas that along VWS direction remains large throughout all stages of the TC. (2)The Precipitation Asymmetry Index (PAI) observed in the three models is higher (lower) during the TC's passage over Taiwan Island (landfall in Guangdong and Fujian). The impact of TC movement characteristics and VWS on precipitation bias varies by model. CMA-SH9 accurately forecasted the VWS direction and produced negligible bias in the evolution of PAI, whereas the moving characteristics (direction and speed) of TC has more effect in CMA-MESO3. And the asymmetric distribution in CMA-GD3 is affected both by the TC’s moving characteristics and VWS. The research findings can offer guidance on how to correct bias in operational TC precipitation forecasts and serve as a reference for model improvements.

     

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