基于2014～2017年Ka毫米波雷达数据分析北京地区云宏观分布特征

Cloud Macro-Physical Characteristics in Beijing Based on Ka Radar Data during 2014-2017

 

中文摘要:

本文利用2014年1月至2017年12月Ka毫米波雷达数据对北京地区云宏观特征进行统计分析。云出现率方面，4年平均值约36.3%；冬季最低，夏季最大；月出现率值9月最大，12月最小；出现率日变化有季节差异，春夏两季呈现中午（11:00，北京时间，下同）开始逐步升高至下午17:00后逐步下降的特点，增高幅度大于15%；冬、秋两季日变化特征不显著。高度方面，4年平均云底高约4.9 km，平均云顶高约7.2 km；云顶高和云底高的月变化特征明显，从年初1月开始逐步上升，在6月达到峰值，而后下降到12月达到低值；3～10月，高云（云底高>5 km)占约一半左右比例；厚度小于1 km的云在各月中所占比例最高；厚度1～4 km的云，厚度越大所占比例越低；特别地，厚度大于4 km的云所占比例在4～9月中仅次于厚度小于1 km云的比例。4年期间，北京地区单层云居多约占66.7%，两层云占比约25.2%，两层以上云占8.1%；冬季约80%的云为单层云，而6～9月云层分布变化最多，其中9月单层云比例最低约为40%。本文基于4年高时空分辨率雷达数据对北京地区云分布特征，特别是云垂直分布特征在数值上准确刻画，该项工作在已有云气候研究中尚未见开展，所获得的知识将对了解地区气候特征、区域模式云参数化选择提供参考。

 

Abstract:       In this paper, Ka band radar data from January 2014 to December 2017 are used to statistically analyze the macro-physical characteristics of cloud in Beijing. The average cloud occurrence frequency during the four years is 36.3%. The maximum monthly averaged cloud occurrence frequency occurs in September, and the minimum is in December. Cloud occurrence frequency has significant daily variation in spring and summer, increasing by up to 15% from 1100 LST to 1700 LST and then decreasing gradually. The mean cloud base height (CBH) is 4.9 km, and cloud top height (CTH) is 7.2 km. The CBH and CTH rise from January gradually, reach the peak in June, and fall to minimum in December. From March to October, high-level clouds (CBH > 5 km) account for 50% of all clouds. Clouds with cloud thickness (CT) < 1 km are the majority in each month; from April to September, clouds with CT > 4 km account for the second-top proportion. Statistics show that single-layer clouds account for 66.7%, double-layers clouds account for 25.2%, and 8.1% are multiple-layers clouds. About 80% of clouds are single-layer in winter. The climatological characteristics, especially the vertical distribution of clouds in Beijing, are characterized numerically based on radar data in high temporal and spatial resolution. Results from this work will further clarify regional cloud climatic characteristics as well as cloud parameterization in climate models.

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