干旱气象 ›› 2021, Vol. 39 ›› Issue (2): 333-344.DOI: 10.11755/j.issn.1006-7639(2021)-02-0333

• 业务技术应用 • 上一篇    下一篇

GRAPES_Meso模式及其云分析系统在中国西北地区降水预报中的应用评估

任绪伟1,陈晓燕2,蔡迪花3,李兰倩1,邵爱梅1   

  1. 1.兰州大学大气科学学院,甘肃 兰州 730000;
    2.兰州中心气象台,甘肃 兰州 730020;
    3.中国气象局兰州干旱气象研究所,甘肃 兰州 730020
  • 出版日期:2021-04-30 发布日期:2021-05-07
  • 通讯作者: 邵爱梅(1976— ),女,教授,主要研究方向为资料同化与数值模拟. E-mail: sam@lzu.edu.cn。
  • 作者简介:任绪伟(1995— ),男,硕士生,从事资料同化和数值模拟方面的研究.
  • 基金资助:
    兰州中心气象台气象雷达资料数值预报应用系统建设(一期)《西北干旱半干旱区快速更新循环预报子系统》项目和科技基础资源调查专项“中国南北过渡带综合科学考察”(2017FY100900)共同资助

Evaluation of Precipitation Forecast Based on GRAPES_Meso Model and Its Cloud Analysis System in Northwest China

REN Xuwei1, CHEN Xiaoyan2, CAI Dihua3, LI Lanqian1, SHAO Aimei1   

  1. 1.College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China;
    2. Lanzhou Central Meteorological Observatory, Lanzhou 730020, China;
    3. Institude of Arid Meteorology, CMA, Lanzhou 730020, China
  • Online:2021-04-30 Published:2021-05-07

摘要: 应用GRAPES_Meso模式(3 km)及其云分析系统,对中国西北地区2018年夏季13次强降水天气过程进行数值预报试验,并就7月天气开展了批量试验,检验评估该模式系统在西北地区的降水预报效果。结果表明:(1)该模式对西北地区降水有着良好且稳定的预报能力,小雨及以上量级降水TS评分在13个强降水个例中平均为0.5~0.6,而批量试验平均为0.4~0.5,可为短临天气预报业务提供支撑;(2)云分析系统能够对模式中水凝物含量进行合理调整,提高了各量级降水预报质量,但云分析系统的循环同化预报结果表现不稳定;(3)预报的雷达回波范围和观测较一致,但强度较观测偏高。

关键词: GRAPES_Meso模式, 云分析系统, 西北地区, 降水预报

Abstract: The precipitation forecast performance of GRAPES_Meso model with 3 km spatial resolution and its cloud analysis system was investigated and compared through numerical experiments for 13 heavy rainfall cases in summer and batch forecast in July 2018 in Northwest China. The results are as follows: (1) GRAPES_Meso model with 3 km spatial resolution had a good forecasting skill and stable performance for precipitation forecast in Northwest China, it could provide favourable outputs for short-term forecasting and nowcasting business. The average threat score of light rain and above for 13 heavy rainfall cases was between 0.5 and 0.6, while the threat score of batch experiments in July was slightly worse than that of 13 heavy rainfall cases. (2) By introducing observed data of radar reflectivity, satellite TBB and CTA, the cloud analysis system could reasonably adjust cloud water, rain water and other hydrometeor content, consequently reduce spin-up time in the mesoscale model and improve the forecast ability of precipitation with different magnitudes. However, the forecast results of cyclic assimilations in cloud analysis system weren’t stable. (3) The predicted coverage of radar echo by GRAESP_Meso model and cloud analysis system was consistent with observations, while the intensity of composite reflectivity was higher than the observations.

Key words: GRAPES_Meso model, cloud analysis system, Northwest China, precipitation forecast

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