Application evaluation of multi-source remote sensing data in spring dust weather monitoring in Beijing

doi:10.11755/j.issn.1006-7639(2023)-02-0318

Abstract

Abstract:

Based on the detection data of three remote sensing detection devices (lidar, laser cloud altimeter and cloud radar), the visibility observation of four national weather stations in Beijing and the PM₁₀ monitoring data of Beijing Ecological Environment Monitoring Center during three dust (storm) weather processes (March 15, March 28 and April 15) affecting Beijing in spring 2021, the characteristics and advantages of three kinds of remote sensing detection equipment for dust monitoring are analyzed comprehensively in order to improve dust weather forecasting and early warning technology. The results show that the lidar can accurately distinguish dust aerosol and atmospheric pollutants under the combination of extinction coefficient and depolarization ratio. The observation of dust in the boundary layer and the middle and lower layer of the troposphere shows high extinction coefficient (more than 0.30 km^-1) and high depolarization ratio (more than 10%). The near-ground backscatter coefficient and cloud bottom height of the cloud altimeter have a good corresponding relationship with PM₁₀ mass concentration and visibility changes, and we can also judge the dust settlement through the sharp increase of near-ground backscatter coefficient and the decrease of cloud bottom height. The cloud radar can detect dust in the boundary layer (0-2 km) and the dust cloud in the upper and middle troposphere (6-10 km), and it also can detect the vertical velocity. In general, lidar is the only one among the three kinds of remote sensing detection equipment that can distinguish dust and air pollutants. Cloud altimeter has good monitoring effect on sand and dust near surface layer, and it can judge the duration of sand dust. Cloud radar has the longest detection range and can detect vertical velocity.

Key words: dust, lidar, cloud altimeter, cloud radar

摘要：

利用2021年春季影响北京的3次沙尘（暴）天气过程（3月15日、28日和4月15日）中激光雷达、激光云高仪和云雷达3种遥感探测设备的探测数据，北京4个国家气象站能见度观测及北京市生态环境监测中心PM₁₀监测数据，综合分析3种遥感探测设备对沙尘监测的特点及优劣，以期提高沙尘天气预报预警技术。结果表明：激光雷达的消光系数和退偏振比可以较准确地区分沙尘气溶胶和大气污染物，在边界层和对流层中下层沙尘表现为高消光系数（>0.30 km^-1）和高退偏振比（>10%）；云高仪的近地面后向散射系数、云底高度与PM₁₀质量浓度、能见度变化有较好的对应关系，也能通过近地面后向散射系数的陡增和云底高度的下降来判断沙尘沉降；云雷达能探测到边界层（0~2 km）内的沙尘和对流层中上层（6~10 km）的沙尘云，且能探测垂直速度。综合来看，激光雷达是3类遥感探测设备中唯一能够区分沙尘和大气污染物的设备；云高仪在近地面层对沙尘的监测效果较好，且能够判断沙尘的持续时间；云雷达的探测距离最远且能探测垂直速度。

关键词: 沙尘, 激光雷达, 云高仪, 云雷达

CLC Number:

P481

XU Luyang, ZHAI liang, WANG Yuanyuan, LEI Lei, YU Bo, HAO Cui, QIN Qingchang. Application evaluation of multi-source remote sensing data in spring dust weather monitoring in Beijing[J]. Journal of Arid Meteorology, 2023, 41(2): 318-327.

徐路扬, 翟亮, 王媛媛, 雷蕾, 于波, 郝翠, 秦庆昌. 多源遥感数据在北京春季沙尘天气监测中的应用评估[J]. 干旱气象, 2023, 41(2): 318-327.

Figures/Tables 10

Fig.1 Hourly variation of visibility and PM10 mass concentration at Daxing （a）， Haidian （b）， Huairou （c） and Yanqing （d） station in Beijing on 15 March 2021

Fig.2 Hourly variation of visibility and PM10 mass concentration at Daxing （a）， Haidian （b）， Huairou （c） and Yanqing （d） station in Beijing on 28 March 2021

Fig.3 Hourly variation of visibility and PM10 mass concentration at Daxing （a）， Haidian （b）， Huairou （c） and Yanqing （d） station in Beijing on 15 April 2021

Fig.4 The mie scattering extinction coefficient （a， c）（Unit： km-1） and depolarization ratio （b， d）（Unit：％） detected by the lidar at Huairou station in Beijing from 12：01 on 14 to 11：59 on 19 March （a， b） and from 00：00 to 23：59 on 15 April （c， d） 2021

Tab.1 The maximum extinction coefficient of the lidar near the ground layer （less than100 m） at Huairou station in Beijing before， during and after the dust weather processes on March 15 and April 15， 2021

过程时间	沙尘发生前	沙尘过程中	沙尘过程后
3月15日	0.10~0.60	0.90~1.60	0.10~0.60
4月15日	0.10~0.50	0.80~1.60	0.10~0.50

Fig.5 The hourly variation of backscatter coefficient （the color shaded， Unit： 10-9m-1·srad-1） and cloud-base height （the black solid line） from the laser cloud altimeter at Haidian station in Beijing on 15 （a）， 28 （b） March， and 15 April （c） 2021

Tab.2 Maximum backscatter coefficient of the laser cloud altimeter at Haidian station in Beijing before， during and after the three dust weather processes on March 15， March 28 and April 15， 2021

过程时间	沙尘发生前	沙尘过程中	沙尘过程后
3月15日	16 432~50 581	1 048 575	1 048 575
3月28日	9 125~26 012	1 048 575	1 048 575
4月15日	688~3 234	1 048 575	1 048 575

Fig.6 Height-time cross section of reflectivity factor of the cloud radar on 15 March 2021 at four meteorological stations in Beijing（Unit： dBZ）

Fig.7 Height-time cross section of radial velocity from the cloud radar on 15 March 2021 at four meteorological stations in Beijing （Unit： m·s-1）

Tab.3 The reflectivity factor from the cloud radar at four meteorological stations in Beijing before， during and after dust weather process on 15 March 2021

站点	发生前	沙尘过程中	过程后
海淀	-41~-27	-10~5	-41~-10
延庆	-41~-25	-13~4	-41~-8
观象台	-41~-26	-8~7	-41~-9
霞云岭	-41~-27	0~11	-41~-9

References 36

[1]	蔡嘉仪, 苗世光, 李炬, 等, 2020. 基于激光云高仪反演全天边界层高度的两步曲线拟合法[J]. 气象学报, 78(5): 864-876.
[2]	曹贤洁, 张镭, 周碧, 等, 2009. 利用激光雷达观测兰州沙尘气溶胶辐射特性[J]. 高原气象, 28(5): 1 115-1 120.
[3]	陈广庭, 2001. 近50年北京的沙尘天气及治理对策[J]. 中国沙漠, 21(4): 402-407.
[4]	陈羿辰, 金永利, 丁德平, 等, 2018. 毫米波测云雷达在降雪观测中的应用初步分析[J]. 大气科学, 42(1): 134-149.
[5]	董旭辉, 祁辉, 任立军, 等, 2007. 偏振激光雷达在沙尘暴观测中的数据解析[J]. 环境科学研究, 20(2): 106-111.
[6]	董旭辉, 杉本伸夫, 白雪椿, 等, 2006. 激光雷达在沙尘观测中的应用——2004年春季北京和呼和浩特沙尘天气的解析[J]. 中国沙漠, 26(6): 942-947.
[7]	段伯隆, 刘新伟, 郭润霞, 等, 2021. “3·15”北方强沙尘暴天气成因分析[J]. 干旱气象, 39(4): 541-553.
[8]	高庆先, 苏福庆, 任阵海, 等, 2002. 北京地区沙尘天气及其影响[J]. 中国环境科学, 22(5): 468-471.
[9]	郭伟, 卜令兵, 贾小芳, 等, 2016. 基于激光云高仪的北京沙尘气溶胶特征分析[J]. 气象, 42(12): 1 540-1 546.
[10]	黄浩杰, 王鹤婷, 许敏, 2021. 京津冀一次沙尘天气的多源资料分析[J]. 农业灾害研究, 11(6): 32-36+151.
[11]	刘慧, 井宇, 黄少妮, 等, 2021. 陕西关中地区一次霾转沙尘过程的气象条件分析[J]. 气象与环境科学, 44(3): 8-15.
[12]	刘黎平, 2021. 毫米波云雷达观测和反演云降水微物理及动力参数方法研究进展[J]. 暴雨灾害, 40(3): 231-242.
[13]	邱金桓, 郑斯平, 黄其荣, 等, 2003. 北京地区对流层中上部云和气溶胶的激光雷达探测[J]. 大气科学, 27(1): 1-7.
[14]	宋嘉尧, 张文煜, 张宇, 等, 2013. 基于微脉冲激光雷达观测资料的半干旱区沙尘天气消光效应分析[J]. 干旱气象, 31(4): 672-676. DOI
[15]	谭金华, 2018. 沙尘环境下交通流跟驰模型及仿真[J]. 交通运输系统工程与信息, 18(3): 63-67.
[16]	王金玉, 李盛, 王式功, 等, 2013. 沙尘污染对暴露人群呼吸系统健康的影响[J]. 中国沙漠, 33(3): 826-831. DOI
[17]	王孟, 黄伟宁, 2020. 沙尘暴对Ka频段信号影响的研究[J]. 中国无线电, (7): 44-46.
[18]	王小兰, 闫世明, 王雁, 等, 2021. 太原市冬季一次霾和沙尘“混合型污染”过程天气成因分析[J]. 气象与环境科学, 44(4):43-52.
[19]	魏凤英, 1999. 现代气候统计诊断与预测技术[M]. 北京: 气象出版社.
[20]	熊亚军, 唐宜西, 寇星霞, 等, 2017. 北京春季一次霾和沙尘混合污染天气过程分析[J]. 干旱气象, 35(1): 100-107.
[21]	于杰, 车慧正, 陈权亮, 等, 2016. 2010—2012年我国西北地区沙尘个例气溶胶特征分析[J]. 气象与环境科学, 39(2): 33-40.
[22]	袁静, 潘哲, 李伶俐, 等, 2019. 基于激光云高仪的雾和霾监测研究[J]. 气象水文海洋仪器, 36(2): 8-14.
[23]	尹青, 何金海, 张华, 2009. 激光雷达在气象和大气环境监测中的应用[J]. 气象与环境学报, 25(5): 48-56.
[24]	张怀清, 胡红玲, 鞠洪波, 等, 2009. 激光雷达沙尘参数提取技术研究[J]. 林业科学研究, 22(2): 161-165.
[25]	张晋茹, 陈玉宝, 卜令兵, 2018. 基于气溶胶和大气风场激光雷达对北京一次沙尘过程分析[J]. 激光与光电子学进展, 55(8), DOI: 10.3788/LOP55.080102. DOI
[26]	张墅, 2021. 激光云高仪和毫米波云雷达协同测量云底高技术的融合算法研究[J]. 高技术通讯, 31(2): 163-169.
[27]	张迎新, 李林, 曹晓冲, 等, 2021. 京津冀地区一次持续时间较长浮尘天气的分析[J]. 沙漠与绿洲气象, 15(4): 123-129.
[28]	赵静, 曹晓钟, 代桃高, 等, 2017. 毫米波云雷达与探空测云数据对比分析[J]. 气象, 43(1): 101-107.
[29]	朱君, 曹晓钟, 李晓兰, 2017. 激光云高仪与可见光测云仪观测试验对比[J]. 气象科技, 45(4): 611-615.
[30]	祝廷成, 梁存柱, 陈敏, 等, 2004. 沙尘暴的生态效益[J]. 干旱区资源与环境, 18(增刊1): 33-37.
[31]	CHEN S, ZHAO C, QIAN Y, et al, 2014. Regional modeling of dust mass balance and radiative forcing over East Asia using WRF-Chem[J]. Aeolian Research, 15: 15-30. DOI URL
[32]	HUANG J, MINNIS P, LIN B, et al, 2006. Possible influences of Asian dust aerosols on cloud properties and radiative forcing observed from MODIS and CERES[J]. Geophysical Research Letters, 33, DOI: 10.1029/2005GL024724. DOI
[33]	LUE Y L, LIU L Y, HU X, et al, 2010. Characteristics and provenance of dustfall during an unusual floating dust event[J]. Atmospheric Environment, 44(29):3477-3 484. DOI URL
[34]	RIPOLL J, NTZIACHRISTOS V, 2005. The characteristics of atmospheric aerosol at Aksu, an Asian dust-source region of North-West China: a summary of observations over the three years from March 2001 to April 2004[J]. Journal of the Meteorological Society of Japan, 83A: 45-72. https://doi.org/10.2151/jmsj.83A.45.
[35]	WANG W, SAMAT A, ABUDUWAILI J, et al, 2022. Temporal characterization of sand and dust storm activity and its climatic and terrestrial drivers in the Aral Sea region[J]. Atmospheric Research, 275, https://doi.org/10.1016/j.atmosres.2022.106242.
[36]	YASUNORI K, MASAO M, 2005. Regional difference in the characteristic of dust event in East Asia: relationship among dust outbreak, surface wind, and land surface condition[J]. Journal of the Meteorological Society of Japan, 83A: 1-18. https://doi.org/10.2151/jmsj.83A.1.