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Target categorization of aerosol and clouds by continuous multiwavelength-polarization lidar measurements

机译:通过连续多波长偏振激光雷达测量对气溶胶和云进行目标分类

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摘要

Absolute calibrated signals at 532 and 1064 nm and thedepolarization ratio from a multiwavelength lidar are used tocategorize primary aerosol but also clouds in high temporal andspatial resolution. Automatically derived particle backscattercoefficient profiles in low temporal resolution (30 min) areapplied to calibrate the lidar signals. From these calibrated lidarsignals, new atmospheric parameters in temporally high resolution(quasi-particle-backscatter coefficients) are derived. By usingthresholds obtained from multiyear, multisite EARLINET (European Aerosol Research Lidar Network) measurements,four aerosol classes (small; large, spherical; large, non-spherical;mixed, partly non-spherical) and several cloud classes (liquid, ice)are defined. Thus, particles are classified by their physical features(shape and size) instead of by source.The methodology is applied to 2 months of continuousobservations (24 h a day, 7 days a week) with themultiwavelength-Raman-polarization lidar Polly duringthe High-Definition Clouds and Precipitation foradvancing Climate Prediction (HD(CP)) Observational PrototypeExperiment (HOPE) in spring 2013. Cloudnet equipment was operatedcontinuously directly next to the lidar and is used for comparison.By discussing three 24 h case studies, it is shown that theaerosol discrimination is very feasible and informative and givesa good complement to the Cloudnet target categorization. Performingthe categorization for the 2-month data set of the entire HOPEcampaign, almost 1 million pixel (5 min  ×  30 m)could be analysed with the newly developed tool. We find that themajority of the aerosol trapped in the planetary boundarylayer (PBL) was composed of small particles as expected for a heavily populatedand industrialized area. Large, spherical aerosol was observed mostlyat the top of the PBL and close to the identified cloud bases,indicating the importance of hygroscopic growth of the particles athigh relative humidity. Interestingly, it is found that on severaldays non-spherical particles were dispersed from the ground into the atmosphere.
机译:532和1064nm处的绝对校准信号以及来自多波长激光雷达的去极化率用于对主要气溶胶以及高时空分辨率的云进行分类。在低时间分辨率(30分钟)内自动获得的粒子后向散射系数曲线可用于校准激光雷达信号。从这些校准的激光雷达信号中,可以得到时间分辨率较高的新的大气参数(准粒子反向散射系数)。通过使用从多年多站点EARLINET(欧洲气溶胶研究激光雷达网络)测量获得的阈值,得出了四种气溶胶类别(小;大,球形,大,非球形;混合,部分非球形)和几种云类别(液体,冰)。定义。因此,该粒子是根据其物理特征(形状和大小)而不是根据来源进行分类的。该方法适用于2个月的连续观测(24周/天,一周7天/周),高清期间使用多波长拉曼偏振激光雷达Polly 2013年春季,云和降水促进了气候预测(HD(CP))观测原型实验(HOPE)。Cloudnet设备直接在激光雷达附近连续运行并用于比较。通过讨论三个24小时的案例研究,发现气溶胶的判别这是非常可行且信息丰富的,对Cloudnet目标分类提供了很好的补充。对整个HOPEcampaign的2个月数据集进行分类,可以使用新开发的工具分析近100万像素(5 min×30 m)。我们发现,在人口稠密且工业化的地区,被困在行星边界层(PBL)中的气溶胶大部分由小颗粒组成。大型球形气溶胶主要出现在PBL的顶部,并且靠近已识别的云层底部,这表明在高相对湿度下颗粒的吸湿性生长非常重要。有趣的是,发现几天来,非球形颗粒从地面分散到大气中。

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