| 广州“5.7”暴雨预报的模式不确定性研究 |
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| 引用本文： | 陈黛雅,沈学顺,霍振华.广州“5.7”暴雨预报的模式不确定性研究[J].气象学报,2023,81(1):58-78. |
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| 作者姓名： | 陈黛雅 沈学顺 霍振华 |
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| 作者单位： | 1.中国气象科学研究院，北京，100081 |
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| 基金项目： | 国家重点研发计划“高精度可扩展数值天气预报模式研究”项目（2017YFC1501904） |
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| 摘 要： | 2017年5月7日，在弱天气尺度强迫下，广州发生了暖区特大暴雨，局地发展迅速，降水强度极端，多家业务模式出现了漏报情况。为了探究此次降水过程模式预报的不确定性，采用条件非线性最优参数扰动（Conditional Nonlinear Optimal Perturbation related to Parameters,CNOP-P）方法筛选出最能体现中小尺度系统非线性误差增长特征的关键物理参数，以此构造CNOP-P-RP模式扰动方案，并基于CMA-Meso模式进行对流尺度集合预报试验，最后探究了CNOP-P关键参数影响局地对流发生、发展不同阶段的物理机理。结果显示，不同降水阶段的CNOP-P敏感参数主要与垂直扩散、云雨自动转换或其他水成物向雨滴的转换有关。与业务上常用的随机物理倾向扰动（Stochastically Perturbed Parameterization Tendencies,SPPT）方案相比，在本次降水过程中，基于CNOP-P-RP方案构造的集合预报试验具有更高的降水和地面要素的概率预报技巧，集合预报系统可靠性也占优。进一步分析发现，垂直扩散不确定性导致的山前温度梯度和...
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| 关 键 词： | 条件非线性最优参数扰动 模式不确定性 CMA模式 对流尺度集合预报 |
| 收稿时间： | 2022-03-17 |
| 修稿时间： | 2022-07-15 |
A research on the model uncertainty in forecast of the 7 May 2017 heavy rainfall in Guangzhou |
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| Institution: | 1.Chinese Academy of Meteorological Sciences，Beijing 100081，China2.CMA Earth System Modeling and Prediction Centre（CEMC），Beijing 100081，China3.State Key Laboratory of Severe Weather，Chinese Academy of Meteorology Sciences，Beijing 100081，China |
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| Abstract: | A warm-sector torrential rain under weak synoptic scale forcing occurred in Guangzhou on 7 May 2017. The precipitation process developed rapidly and locally, and the precipitation intensity is extremely high. Many operational numerical weather prediction models failed to forecast this storm. To study the model uncertainty in the forecast of this precipitation process, the Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP-P) is adopted to select key physical parameters which can best represent the nonlinear error growth characteristics of the meso-micro scale system. A new model perturbation scheme CNOP-P-RP is constructed based on these key parameters. Convective-permitting ensemble prediction experiment is carried out based on the CMA-Meso model. Finally, the physical mechanism behind the influences of key parameters selected by CNOP-P on local convection in different stages is investigated. The result shows that the key parameters selected by CNOP-P are mainly related to vertical diffusion, auto-conversion from cloud to rain and conversion from other hydrometeors to raindrops. Compared with Stochastic Perturbed Parameterization Tendencies (SPPT) scheme which is widely utilized in operational ensemble prediction systems, the ensemble prediction experiment based on the CNOP-P-RP scheme is more skillful and reliable for probability forecast of precipitation and surface elements in this process. Further analysis shows the variation of piedmont temperature gradient and surface cold pool caused by the uncertainty of vertical diffusion plays an important role in convective triggering and rainstorm development. From 00:00 BT to 04:00 BT 7 May, the enhancement of vertical diffusion near the center of heavy precipitation in Huadu strengthened the vertical transport of heat, momentum and water vapor. The melting of snow and graupel particles is the main reason for the increase of precipitation, indicating that although the formation of raindrops is mainly caused by condensation near the top of boundary layer and collision of cloud water, the effect of ice particles cannot be ignored. From 04:00 BT to 08:00 BT 7 May, with the strengthening of water vapor transport and upward movement, a more active warm rain process dominated the increase of precipitation in the heavy precipitation center at Zengcheng. This study preliminarily proves the feasibility of the CNOP-P-RP scheme in describing the uncertainties in convection-permitting ensemble prediction systems, and provides some references for the improvement of warm-sector torrential rain forecast in South China. |
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