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我国北方一次典型强沙尘暴过程的延伸期数值预报能力分析

Numerical prediction ability analysis of extended period for a typical severe sandstorm process in northern China
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摘要 目前利用数值模式对延伸期以上时间尺度的沙尘天气进行客观定量预报还在尝试阶段,本文利用耦合了沙尘模块的区域气候模式RegCM-dust对我国北方一次典型强沙尘暴过程进行延伸期数值预报能力分析,并与NCEP再分析资料等分析结果进行对比。结果表明,模式模拟的起沙量大值区主要位于新疆南部、蒙古国和内蒙西部;模式对10 m风速具有一定的预报能力,但模拟风速比再分析资料风速偏小;模式模拟的沙尘柱含量和总沉降量变化能够反映沙尘暴天气过程特征;模拟的整层沙尘混合比与城市污染指数有一定对应关系,说明模式对沙尘引起的污染天气具有一定预报能力。 Sandstorm is a serious natural disaster in north China.It is of great significance to carry out relevant research to improve the forecast level of this kind of catastrophic weather.Based on the RegCM-dust model,an extended period numerical prediction analysis of a typical severe sandstorm process in north China is conducted,and the results are compared with NCEP reanalysis data and other analysis results.The results show that the regions with high sediment discharge simulated by the model are mainly located in southern Xinjiang,Mongolia and western Inner Mongolia.The model has a certain forecasting ability for 10 m wind speed,but the simulated wind speed is smaller than the reanalysis data.The changes of dust column content and total sedimentation simulated by the model can reflect the characteristics of the dust storm weather process.The simulated sand-dust mixing ratio has a certain correspondence with the urban pollution index,which indicates that the model has certain forecasting ability for the pollution weather caused by sand-dust.
作者 李丹华 张强 卢国阳 刘丽伟 任余龙 白冰 杨扬 段伯隆 黄鹏程 LI Danhua;ZHANG Qiang;LU Guoyang;LIU Liwei;REN Yulong;BAI Bing;YANG Yang;DUAN Bolong;HUANG Pengcheng(Lanzhou Regional Climate Center,Lanzhou 730020,China;Institute of Arid Meteorology,CMA,Lanzhou 730020,China;Lanzhou Central Meteorological Observatory,Lanzhou 730020,China)
出处 《干旱气象》 2023年第6期944-951,共8页 Journal of Arid Meteorology
基金 国家自然科学基金重点资助项目(42230611) 干旱气象科学研究基金项目(IAM202014、IAM202110) 甘肃省气象局项目(Ms2021-16、Ms2022-17) 甘肃省自然基金青年科技基金计划项目(1208RJYA027、21JR7RA709) 预报员复盘总结专项(FPZJ2023-138)共同资助。
关键词 北方地区 强沙尘暴 延伸期 数值预报 northern China strong sandstorms extended period numerical forecasting
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