主办单位：中国气象局沈阳大气环境研究所
国际刊号：ISSN 1673-503X
国内刊号：CN 21-1531/P
气象与环境学报

气象与环境学报 ›› 2023, Vol. 39 ›› Issue (4): 1-8.doi: 10.3969/j.issn.1673-503X.2023.04.001

• 论文 • 上一篇    下一篇

2020年2月辽宁一次暴雪过程天气学成因与预报偏差分析

谭政华1,2(),张爱忠3,阎琦1,2,*(),杨瑞雯1,2,关雨姗4   

  1. 1. 中国气象局沈阳大气环境研究所, 辽宁沈阳 110166
    2. 辽宁省气象台, 辽宁沈阳 110166
    3. 民航东北地区空中交通管理局空管中心气象中心, 辽宁沈阳 110169
    4. 辽宁省气象装备保障中心, 辽宁沈阳 110166
  • 收稿日期:2022-03-03 出版日期:2023-08-28 发布日期:2023-09-23
  • 通讯作者: 阎琦 E-mail:ln-tanzhenghua@outlook.com;yq.mete@163.com
  • 作者简介:谭政华, 男, 1991年生, 工程师, 主要从事天气动力学和客观预报技术研究, E-mail: ln-tanzhenghua@outlook.com
  • 基金资助:
    民航东北地区空中交通管理局项目“强降雪预报综合订正技术研究”;中国气象局创新发展专项(CXFZ2023J014);中国气象局沈阳大气环境研究所和东北冷涡研究重点开放实验室联合开放基金(2020SYIAE10);中国气象局沈阳大气环境研究所和东北冷涡研究重点开放实验室联合开放基金(2021SYIAEKFZD03);中国气象局沈阳大气环境研究所和东北冷涡研究重点开放实验室联合开放基金(2021SYIAEKFMS06);环渤海区域科技协同创新基金(QYXM202104);辽宁省气象局核心攻关项目(LNCP202204);辽宁省气象局科研项目(BA202102)

Analysis of the synoptic causes and forecast deviation of a snowstorm process in Liaoning province in February 2020

Zhenghua TAN1,2(),Aizhong ZHANG3,Qi YAN1,2,*(),Ruiwen YANG1,2,Yushan GUAN4   

  1. 1. Institute of Atmospheric Environment, CMA, Shenyang 110166, China
    2. Liaoning Meteorological Observatory, Shenyang 110166, China
    3. Meteorological Center, Air Traffic Control Center, Northeast Air Traffic Administration, CAAC, Shenyang 110169, China
    4. Liaoning Meteorological Equipment Support Center, Shenyang 110116, China
  • Received:2022-03-03 Online:2023-08-28 Published:2023-09-23
  • Contact: Qi YAN E-mail:ln-tanzhenghua@outlook.com;yq.mete@163.com

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摘要：

采用NCEP FNL再分析资料，对辽宁地区2020年2月14—16日强降雪过程进行诊断，并对数值模式的预报偏差及其与影响系统的相关性进行分析。结果表明：受中国华北地区高空槽及黄海北部低压倒槽共同影响，南支锋区的偏南暖湿气流沿北支锋区的偏北风回流冷垫爬升，形成水汽辐合和抬升运动，为此次强降雪过程的发生提供了有利的动力条件。CMA-GFS模式对强降雪中心的降水强度预报偏小，但强降雪落区预报基本准确；ECMWF对降水强度预报较为准确，但强降雪落区预报偏西、范围明显偏大。ECMWF对本次强降雪过程的长时效（3.5 d）预报相对准确，时效临近时降水预报有转折性变化并出现明显预报偏差。诊断结果表明，此次强降雪过程中降水预报的转折性变化与高、低空低压系统的预报偏差显著相关，尤其是低空低压系统的预报偏差，与强降雪落区上空两条水汽通道的比湿存在显著相关，低压系统预报偏强与比湿偏大相对应，导致模式中的日本海水汽输送作用偏强，可能是造成降水预报出现明显偏差的原因。

关键词: 暴雪天气, 诊断分析, 预报偏差

Abstract:

The NCEP FNL (National Centers for Environmental Prediction Final Operational Global Analysis data) reanalysis data was used to diagnose the heavy snowfall process in the Liaoning region from February 14 to 16, 2020, and analyze the forecast deviation of the numerical model and its correlation with the affecting system.The results show that, under the influence of the upper trough in North China and the low overlying trough in the north of the Yellow Sea, the warm and humid air in the southern branch front area climbes along the cold pad of the northern wind in the northern branch front area, forming water vapor convergence and uplifting movement, which provides favorable dynamic conditions for the occurrence of the heavy snowfall process.The performance of the CMA-GFS (China Meteorological Administration Global Forecast System) model is poor in predicting the precipitation intensity of the heavy snowfall center, but the prediction of the heavy snowfall area is relatively accurate.The ECMWF (European Center for Medium-Range Weather Forecasts) model is more accurate in predicting precipitation intensity, but the forecast of heavy snowfall area is biased westward and the range looks too larger.The ECMWF model is relatively accurate in predicting the long duration (3.5 d) of this heavy snowfall process, and the precipitation forecast has a turning change and obvious forecasting biases in the short duration.The diagnostic results show that the turning change of precipitation forecast during the heavy snowfall is significantly correlated with the forecast deviation of the high and low altitude low-pressure systems.In particular, the forecast deviation of the low altitude low-pressure system is significantly correlated with the specific humidity of the two water vapor channels over the heavy snowfall area.The stronger forecast of the low-pressure system corresponds to the larger specific humidity, resulting in stronger water vapor transport over the Sea of Japan in the model.It may be the cause of the obvious deviation in the precipitation forecast.

Key words: Snowstorm, Diagnostic analysis, Forecast error analysis

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