基于北京多源资料的云宏观特征判识

周青; 李柏; 张勇; 陶法; 胡树贞; 李瑞义; 杨荣康

doi:10.11898/1001-7313.20230207

基于北京多源资料的云宏观特征判识

DOI: 10.11898/1001-7313.20230207

中国气象局气象探测中心, 北京 100081

资助项目:

国家重点研发计划 2017YFC1501802

国家重点研发计划 2017YFC1501701

详细信息

通信作者:
李柏, 邮箱：libai@cma.gov.cn

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出版历程
- 收稿日期: 2022-08-04
- 修回日期: 2023-01-11
- 刊出日期: 2023-03-31

Identification on Cloud Macroscopic Physical Characteristics Based upon Multi-source Observations in Beijing

Meteorological Observation Center, China Meteorological Administration, Beijing 100081

摘要

摘要: 获取准确的云高及其变化特征，对于揭示天气系统的演变以及改进气候模式具有重要作用。由于不同设备观测云高的不确定性，将锋区要素不连续变化理论引入云高分析中，将云底部、云顶部大气的交界过渡带区域视为云锋区，研究探空、毫米波雷达、风廓线雷达等不同类型设备观测要素在云锋区及云外环境大气的变化特征。对流云和层状云个例研究表明：在云锋区，温湿度及雷达反射率因子随高度的一阶、二阶导数均呈不连续现象（即一阶、二阶导数值在云内外和云锋区表现为不相等），风廓线雷达信噪比垂直梯度也出现突变，因此不同设备观测云高具有较好空间一致性，并得到云底和云顶高度的合理范围和相应判据；相对于层状云，对流云内外温度梯度差异以及云体内反射率因子二阶导数的脉动变化幅度均偏大，因此可作为区分二者的参考指标。
- 云锋区;
- 二阶导数;
- 不连续变化;
- 判识方法
Abstract: The knowledge of accurate cloud heights (including cloud base height and cloud top height) information and its variation is of great importance to elucidating synoptic variation and improving climate model and prediction precision. Utilizing the theory of variation continuity and first-order discontinuity of meteorological element in frontal zone, cloud front zone is defined as transitional zone between the cloud cluster and its adjacent area in vertical direction in order to solve the problem of cloud heights uncertainties observed by different equipments. According to the humidity, scattering and turbulence properties of cloud, using observation from L-band sounding, Ka-band millimeter wave cloud radar (MMCR) and the wind profiler, the variation characteristics of temperature, humidity, radar reflectivity and signal noise ratio (SNR) as well as their differences from the ambient atmosphere are studied. In addition, the differences between convective clouds and stratified clouds are studied in terms of the characteristics of element gradient variation inside and outside clouds. Finally, the identification for cloud front zone is verified by case study and the reasonable scope and identification criterion for cloud base height and cloud top height are concluded. The results show that the first-order and second-order derivative of temperature, humidity, and radar reflectivity are discontinuous in cloud front zone (they are not equal inside and outside the cloud front region), and the vertical gradient of SNR retrieved by wind profiler is also instable, which shows that the cloud boundary range with better spatial consistency can be obtained by different devices, based on the frontal theory. In addition, there are two indicators that can be utilized to distinguish the stratiform clouds from convective clouds. The first is the difference between the vertical gradient of temperature and humidity in clouds and that in ambient atmosphere, which is larger in convective clouds than that in stratiform clouds. The second is the fluctuation amplitude of the second-order derivative of reflectivity in clouds, which is also larger in convective clouds than that in stratiform clouds. The concept of cloud front zone can be used to comprehensively identify the common range of cloud height detected by different devices, indicating that there are consistent variation characteristics in a certain area near the cloud front zone for different devices. The similarity of cloud vertical structures retrieved by multi-source equipment observation are elucidated through the characteristics of cloud front zone, which is worth applying for collaborative observation of different devices.
- cloud front zone;
- second-order derivative;
- discontinuous variation;
- identification method

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图 1 2017年8月28日00:00—23:59(a)以及18:00—21:00(b)毫米波雷达反射率因子时序

Fig. 1 MMCR-observed radar reflectivity from 0000 BT to 2359 BT(a) and from 1800 BT to 2100 BT(b) on 28 Aug 2017

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图 2 2017年8月28日19:40毫米波雷达反射率因子廓线(a)及其一阶导数(b)和二阶导数(c)廓线

Fig. 2 Profiles of MMCR-observed reflectivity(a) and its first-order derivative(b), second-order derivative(c) at 1940 BT 28 Aug 2017

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图 3 2017年8月28日20：00探空观测温度、相对湿度、相对冰面相对湿度廓线、判云的相对湿度阈值廓线(a)以及温度、相对湿度一阶导数(b)和二阶导数(c)廓线

Fig. 3 Profiles of radiosonde-observed temperature, relative humidity, relative humidity below 0℃, judgment entry cloud relative humidity threshold(a) and temperature, relative humidity first-order derivative(b), second-order derivative(c) at 2000 BT 28 Aug 2017

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图 4 2017年8月28日00：00—23：59风廓线雷达信噪比(a)、信噪比一阶导数(b)、18：00—21：00信噪比(c)以及信噪比一阶导数(d)时序

Fig. 4 SNR(a) and its first-order derivative(b) from 0000 BT to 2359 BT, with SNR(c) and its first-order derivative(d) from 1800 BT to 2100 BT observed by WPR on 28 Aug 2017

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图 5 2017年8月28日19:40风廓线雷达信噪比廓线(a)及其一阶导数廓线(b)

Fig. 5 Profiles of WPR-observed SNR(a) and its first-order derivative(b) at 1940 BT 28 Aug 2017

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图 6 同图 1，但为2018年6月30日

Fig. 6 The same as in Fig. 1,but on 30 Jun 2018

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图 7 同图 2，但为2018年6月30日19:30

Fig. 7 The same as in Fig. 2,but at 1930 BT 30 Jun 2018

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图 8 同图 3，但为2018年6月30日20：00

Fig. 8 The same as in Fig. 3,but at 2000 BT 30 Jun 2018

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图 9 同图 4，但为2018年6月30日

Fig. 9 The same as in Fig. 4,but on 30 Jun 2018

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表 1 2017年8月28日(个例1)与7月27日(个例2)层状云云锋区范围及判识条件

Table 1 Identification criterion for cloud frontal zone on 28 Aug 2017 and 27 Jul 2017

	探测设备	云锋区范围	判识条件
云底高度	毫米波雷达	7.08~7.32 km(个例1) 5.76~6.00 km(个例2)	取极大值，从极大值迅速降为极小值的区域
	探空	7.25~7.34 km(个例1) 5.80~6.00 km(个例2)	取极大值，且R_H达饱和阈值，且
	风廓线雷达	7.00~7.47 km(个例1) 4.83~5.07 km(个例2)	信噪比的一阶导数取极大值
云顶高度	毫米波雷达	11.28~11.40 km(个例1) 10.32~11.16 km(个例2)	取极小值，从极小值迅速增为极大值的区域
云顶高度	探空	11.18~12.78 km(个例1) 11.05~11.77 km(个例2)	R_H接近未饱和阈值，且≤0，≥0

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表 2 2018年6月30日(个例3) 与2017年8月18日(个例4) 对流云云锋区范围及判识条件

Table 2 Identification criterion for cloud frontal zone on 30 Jun 2018 and 18 Aug 2017

	探测设备	云锋区范围	判识条件
云底高度	毫米波雷达	4.20~4.32 km(个例3) 5.04~5.16 km(个例4)	取极大值，或从极大值迅速降为极小值的区域
云底高度	探空	4.28~5.03 km(个例3) 4.93~5.12 km(个例4)	R_H达饱和阈值，且≤0，≥0
云顶高度	毫米波雷达	12.60~12.72 km(个例3) 9.12~9.84 km(个例4)	取极小值，或从极小值迅速增为极大值的区域
云顶高度	探空	12.50~13.50 km(个例3) 9.70~10.21km(个例4)	R_H低于饱和阈值，且≤0，≥0

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基于北京多源资料的云宏观特征判识

DOI: 10.11898/1001-7313.20230207

通信作者:
李柏, 邮箱：libai@cma.gov.cn

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Identification on Cloud Macroscopic Physical Characteristics Based upon Multi-source Observations in Beijing

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留言板

基于北京多源资料的云宏观特征判识

DOI: 10.11898/1001-7313.20230207

通信作者: 李柏, 邮箱：libai@cma.gov.cn

计量

出版历程

Identification on Cloud Macroscopic Physical Characteristics Based upon Multi-source Observations in Beijing

计量

出版历程

目录

通信作者:
李柏, 邮箱：libai@cma.gov.cn