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本文中观测使用的是青岛镭测创芯科技有限公司的WindMast PBL型边界层风廓线测风激光雷达(以下简称“雷达”),其布设于内蒙古自治区锡林郭勒盟毛登牧场(44°8′31.70″N, 116°18′41.70″E),如图 1所示。激光波束俯仰角度为71.38°,起测高度为28m,径向距离分辨率为15m,高度分辨率为14m(径向距离分辨率15m×sin 71.38°),最大径向探测距离为3000m。本文中所用雷达数据为10min平均数据,数据产品包括水平风速、水平风向、垂直速度和消光系数等。表 1中为相关参数指标。
Table 1. Performance specifications of coherent Doppler wind LiDAR (WindMast PBL)
parameters value detection system coherent detection of pulsed laser wavelength 1550nm detection range 30m~3000m wind speed range 0m/s~75m/s wind velocity error ≤0.5m/s wind direction range 0°~360° wind direction error <5° vertical resolution 15m/30m period detection 1s/1min/2min/5min/10min scanning model velocity azimuth display/Doppler beam swinging data products horizontal wind speed and direction, air vertical velocity, horizontal turbulence intensity, signal of noise rate, aerosol back scatter coefficient and extinction coefficient, standard deviation of the air vertical velocity, the state of radar, surface air temperature, humidity, pressure, etc. -
本文中选用内蒙古自治区锡林郭勒盟锡林浩特市2019-10-27~2019-10-28(年月日时间均为北京时间)地面观测站(站号:54102,43°57′0.36″N, 116°7′11.64″E)逐小时整点时刻的瞬时温度、地面气压、相对湿度、瞬时风速、瞬时风向、1h降水量和能见度数据,以及空气质量监测站(43°55′46.99″N, 116°6′14.08″E)的每小时颗粒物(particulate matter,PM)粒径不大于2.5μm(PM2.5)和10μm(PM10)的质量浓度和空气质量指数(air quality index, AQI)。
地面观测站和环保监测站是距离雷达最近的地面观测站点,均位于雷达西南方的锡林浩特市区,分别与雷达相距24.4km和27.5km, 如图 2所示。考虑到同处于草原中,周围地势均为平整,气象观测站和市环保监测站所观测到的数据资料,可以近似表示测风激光雷达观测点处的地面气象要素和空气颗粒物浓度状况。同时需要注意的是,城市下垫面和测风激光雷达所处的草地下垫面仍有不同,在分析过程中要根据具体情况来处理。
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MODIS(moderate resolution imaging spectroradiometer)是搭载在Terra和Aqua两颗极轨卫星上的中分辨率成像光谱仪。388nm通道,1°×1°分辨率的气溶胶光学厚度(aerosol optical depth, AOD)可反映沙尘分布情况,分析沙尘的移动路径。
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利用ERA5-Land-hourly 0.1°×0.1°分辨率的2m气温数据用来分析沙尘天气前期及沙尘天气期间下垫面的温度情况,选取2019-10-26~2019-10-29每日14:00的2m气温数据,将后一日与前一日气温值做差,获得两日14:00的2m气温变化情况。
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混合单粒子拉格朗日积分轨道(hybrid single particle Lagrangian integrated trajectory,HYSPLIT)模型是由美国国家海洋和大气管理局的空气资源实验室和澳大利亚气象局在过去20年间联合研发的一种用于计算和分析大气污染物输送、扩散轨迹的专业模型。该模型已经被广泛地应用于多种污染物各个地区的传输和扩散研究中。
内蒙古自治区一次沙尘过程的激光雷达分析
Analysis of LiDAR in a sand dust process in Inner Mongolia autonomous region
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摘要: 为了探究沙尘污染过程中高时空分辨率的结构特征, 利用相干多普勒测风激光雷达、地面观测站、中分辨率成像光谱仪, 结合卫星气溶胶光学厚度数据产品、欧洲中期天气预报中心第5代再分析资料(ERA5)以及混合单粒子拉格朗日积分轨道(HYSPLIT)后向轨迹模式, 分析了2019-10-27~2019-10-28内蒙古自治区锡林郭勒盟一次典型的沙尘天气过程。结果表明, 此次沙尘是受高空冷涡、蒙古气旋的共同影响, 配合冷锋在高温时段过境沙源地, 热力叠加动力条件有利于沙尘随西风扩散; 沙尘来临前后, 地表气温变化明显; 卫星产品、HYSPLIT模式结合雷达风廓线可以更准确地确定沙尘来源, 激光雷达反演的气溶胶消光系数可反映边界层大气中气溶胶的变化情况; 2019-10-28T02:00地面PM10的质量浓度达到最大值268μg/m3, 消光系数超过30km-1, 达到最大值, 雷达反演数据在时间上会有延迟; 城市下垫面使得沙尘污染快速减弱, 雷达所在的草地下垫面容易受垂直风切变影响产生持续性污染。该研究对应用相干多普勒测风激光雷达、认识沙尘的污染过程以及传输特性很有帮助。
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关键词:
- 大气与海洋光学 /
- 沙尘过程监测 /
- 相干多普勒测风激光雷达 /
- 气溶胶消光系数 /
- 下垫面
Abstract: In order to explore the structural characteristics of high spatial and temporal resolution in the process of sand and dust pollution, a typical dust weather process on 2019-10-27~2019-10-28 in Xilin Gol League of Inner Mongolia Autonomous Region was analyzed using coherent Doppler wind light detection and ranging(LiDAR), ground observation station, aerosol optical depth (AOD) data product of medium resolution image spectrometer satellite, European Centre for Medium-Range Forecasts the 5th reanalysis data (ERA5), and hybrid single particle Lagrangian integrated trajectory (HYSPLIT) backward trajectory model. The results show that the dust was affected by the high altitude cold vortex and the Mongolian cyclone, and the cold front passed over the sand source area of the central and western Mongolia and the western Inner Mongolia Autonomous Region during the high temperature period. The thermal superposition dynamic conditions were favorable for the dust to spread with the westerly wind. The surface temperature changes obviously before and after the arrival of dust. Satellite products, HYSPLIT mode combined with wind profile of LiDAR can more accurately determine the source of dust. At 02:00 on 2019-10-28, the mass concentration of ground PM10 reached the maximum 268μg/m3, and the extinction coefficient exceeded 30km-1 and reached the maximum. Radar inversion data was delayed in time. The aerosol extinction coefficient retrieved by LiDAR can reflect the changes of aerosols in the boundary layer atmosphere. The urban underlying surface weakened the dust pollution rapidly, and the grassland underlying surface where the radar located was prone to be affected by vertical wind shear to produce persistent pollution. This research is helpful to application of coherent Doppler wind LiDAR, understand the pollution process and transmission characteristics of sand and dust. -
Table 1. Performance specifications of coherent Doppler wind LiDAR (WindMast PBL)
parameters value detection system coherent detection of pulsed laser wavelength 1550nm detection range 30m~3000m wind speed range 0m/s~75m/s wind velocity error ≤0.5m/s wind direction range 0°~360° wind direction error <5° vertical resolution 15m/30m period detection 1s/1min/2min/5min/10min scanning model velocity azimuth display/Doppler beam swinging data products horizontal wind speed and direction, air vertical velocity, horizontal turbulence intensity, signal of noise rate, aerosol back scatter coefficient and extinction coefficient, standard deviation of the air vertical velocity, the state of radar, surface air temperature, humidity, pressure, etc. -
[1] ZHANG X Y, ARIMOTO R, AN Z S. Dust emission from Chinese desert sources linked to variations in atmospheric circulation[J]. Journal of Geophysical Research Atmospheres, 1997, D102(23): 28041-28047. [2] CHEN Y, CHEN D H, WANG H, et al. Numerical simulations of dust radiative heating on the dust storm transport and meteorological fields by using an interactive weather-dust model[J]. Chinese Journal of Atmospheric Sciences, 2009, 33(1): 38-50(in Chinese). [3] CARLSON N. Atmospheric turbidity in Saharan dust out-breaks as determined by analyses of satellite brightnessdata[J]. Monthly Weather Review, 2009, 107(3): 322-335. [4] WANG H, ZHAO T L, ZHANG X Y, et al. Dust direct radiative a-ffects on the earth-atmosphere system over east asia: Early spring cooling and late spring warming[J]. Chinese Science Bulletin, 2011, 56(11): 858-868(in Chinese). doi: 10.1360/csb2011-56-11-858 [5] YUAN T G, CHEN S Y, KANG L T, et al. Temporal and spatial distribution characteristics and change trends of dust intensity in dust source regions of northern China during 1961-2010[J]. Journal of Arid Meteorology, 2016, 34(6): 927-935(in Chinese). [6] KONG F. Spatial and temporal evolution characteristics of days of disastrous dust weather in China from 1961 to 2017[J]. Journal of Arid Land Resources and Environment, 2020, 34(8): 116-123(in Chinese). [7] YANG X J, ZHANG Q, YE P L, et al. Characteristics and causes of persistent sand-dust weather in mid-march 2021 over northern China[J]. Journal of Desert Research, 2021, 41(3): 245-255(in Chinese). [8] ZHENG Y F, LIU Zh, LIU J J, et al. The spatio-temporal distribution and transport behavior of a dust event in north China[J]. Journal of Desert Research, 2013, 33(5): 1440-1452(in Chinese). [9] LIU D, ZHANG W, CHEN Y, et al. Analysis of the mechanism and transmission of dust in northwest China based on satellite remote-sensing data[J]. Journal of Desert Research, 2014, 34(6): 1605-1616(in Chinese). [10] TAN Z Y, MA M J, YANG Y, et al. A typical sand dust weather process and its influence on the air quality of Gansu[J]. Journal of Lanzhou University(Natural Sciences Edition), 2019, 55(6): 750-763(in Chinese). [11] GUO X N, WANG Y, MA X M, et al. Analysis of a typical heavy dust pollution weather in semi-arid region: A case study in eastern Qinghai[J]. Acta Scientiae Circumstantiae, 2021, 41(2): 343-353(in Chinese). [12] WANG M Zh, WEI W Sh, HE Q, et al. Preliminary study on sand-dust weather process detection using boundary wind profile radar[J]. Journal of Desert Research, 2011, 31(2): 352-356(in Chinese). [13] WEI W Sh, WANG M Z, HE Q. Wind profiler radar to monitor the dust weather[J]. Strategic Study of CAE, 2012, 14(10): 51-56 (in Chinese). [14] WANG M Zh. Detection analysis of sandstorm and precipitation process based on wind-profiling radar[D]. Lanzhou: Lanzhou University, 2014: 71-78(in Chinese). [15] LIU Ch, ZHANG B H, HUA C, et al. Application of wind profiler radar in a strong sand dust weather analysis in Beijing[J]. China Environmental Science, 2018, 38(5): 1663-1669(in Chinese). [16] WANG X Y, WANG B, WEN M Zh, et al. Statistical characteristic analysis of the boundary wind profile radar in Fujian hilly areas. Journal of the Meteorological Sciences, 2015, 35(3): 328-333(in Chinese). [17] LIU Ch, MAO W Q, FAN X, et al. Assessment of detection performance of ST wind profile radar in mountainous and hilly area[J]. Journal of Arid Meteorology, 2018, 36(2): 326-330(in Chinese). [18] LIU L L, YANG J, HUANG J, et al. Analysis of SO2 and NO2 concentration profiles in Huainan detected by a lidar[J]. Laser Technology, 2019, 43(3): 353-358(in Chinese). [19] LI M, LIU W R. Multiple-scattering effects on the visibility mea-surement of laser transmissometers in fog[J]. Laser Technology, 2020, 44(4): 503-508(in Chinese). [20] ZHU P, WANG Ch G, YAN J D, et al. Comparison of three kinds of wind data in boundary layers under complex surface conditions in Beijing[J]. Journal of Arid Meteorology, 2018, 36(5): 794-801(in Chinese). [21] SHANG X. Aerosol observation and inversion based on 1.5μm lidar[D]. Hefei: University of Science and Technology of China, 2020: 32-47(in Chinese). [22] SHEN L L. Analysis of dust transport in typical synoptic system in China[D]. Qingdao: Ocean University of China, 2010: 67-74(in Chinese). [23] DAI G Y, WANG X Y, SUN K W, et al. Calibration and retrieval of aerosol optical properties measured with coherent Doppler lidar[J]. Journal of Atmospheric and Oceanic Technology, 2021, 38: 1035-1045. [24] KUNZ D, GERAND J, LEEU W, et al. Inversion of lidar signals with the slope method[J]. Applied Optics, 1993, 32: 3249-3256. doi: 10.1364/AO.32.003249 [25] LEGAL T, LEGAL L, LEHN W. Measuring visibility using digital remote video cameras[C]//American Meteorological Society 9th Sympon Meteorology Observations & Instrument. New York, USA: IEEE, 1994: 87-89. [26] ZHANG J R, CHEN Y B, BU L B. Analysis of a dust process in Beijing based on aerosol and atmospheric wind field lidar[J]. Laser and Optoelectronics Progress, 2018, 55(8): 080102(in Chinese). doi: 10.3788/LOP55.080102