基于拉曼激光雷达的全天时大气水汽含量探测与分析

发布时间：2019-04-25 20:56

【摘要】：大气水汽是重要的大气气象参数,它是大气最活跃的一种气体成份。水汽是成云降水过程中不可缺少的因素,同时在全球水文循环、气象学和大气动力学等一系列大气过程中也扮演着重要的角色。而水汽的这些重要影响都与水汽含量及其时空分布变化特征紧密相关。鉴于水汽的快速时空变化特性,全天时高精度的大气水汽探测对于研究降水、大气污染物扩散以及灰霾和雾霾的形成和消散等有着重要的科学研究意义和应用价值。针对全天时探测中白天强太阳背景光对弱水汽拉曼散射信号的干扰作用,论文首先进行了对现有拉曼激光雷达系统的仿真,详细讨论了接收视场角、滤光片带宽等主要参数对全天时探测性能的影响,并完成了全天时大气水汽探测激光雷达系统的优化设计与实验验证。根据激光雷达回波信号和噪声的特点,提出了基于小波去噪的白天太阳背景光高精度滤波抑制方法研究,讨论了分解层数、小波基函数、阈值函数以及阈值选取对去噪结果的影响,通过大量的数据分析和去噪评价函数对比,得到以小波基sym6-8,分解层数为5层,采用改进的阈值函数和改进通用阈值方法,可达到较好的去噪效果。并开展全天时激光雷达探测实验和数据反演,结果表明,在白天大气水汽探测距离可从1.5-2km提高至3km以上,验证了小波阈值去噪算法的有效性和全天时水汽探测的可行性。利用2013年11月-2016年7月西安理工大学激光雷达遥感中心探测数据和气象站数据,对西安地区大气水汽含量进行了长期统计分析。获得了不同高度层内水汽含量的变化和占比,分析了水汽含量的时空变化特性和季节变化特性,以及特殊天气过程(如降雨、降雪)前后水汽含量的变化趋势。并探讨了水汽含量与各气象要素的相关性,结果表明,水汽含量与地面温度和水汽压正相关,与气压负相关,而且与降水量、降水日数和降水效率有较强的相关性,为使用激光雷达探测手段指导农业生产以及人工降水工作提供有力依据。
[Abstract]:Atmospheric water vapor is an important atmospheric meteorological parameter. It is one of the most active gas components in the atmosphere. Water vapor is an indispensable factor in the process of cloud-forming precipitation and plays an important role in a series of atmospheric processes such as global hydrological cycle meteorology and atmospheric dynamics. These important effects of water vapor are closely related to water vapor content and its spatio-temporal distribution. In view of the rapid spatio-temporal variation of water vapor, the high-precision atmospheric water vapor detection during the whole day is of great scientific significance and application value for the study of precipitation, the diffusion of atmospheric pollutants, the formation and dissipation of haze and haze, and so on. In view of the interference of strong solar background light on weak water vapor Raman scattering signal in all-day detection, the simulation of the existing Raman lidar system is carried out firstly, and the field of view angle of reception is discussed in detail. The influence of filter bandwidth and other main parameters on all-day detection performance is studied. The optimized design and experimental verification of all-day Lidar system for atmospheric water vapor detection are completed. According to the characteristics of Lidar echo signal and noise, a high precision filtering method based on wavelet de-noising for daytime solar background light is proposed. The decomposition layer number and wavelet basis function are discussed. The influence of threshold function and threshold selection on de-noising results is studied. Through a lot of data analysis and comparison of de-noising evaluation function, the wavelet-based sym6-8, decomposition layer is used as 5 layers, and the improved threshold function and improved universal threshold method are adopted. Can achieve a better de-noising effect. All-day lidar detection experiments and data inversion are carried out. The results show that the range of atmospheric water vapor detection can be increased from 1.5-2km to more than 3km in the daytime. The validity of wavelet threshold de-noising algorithm and the feasibility of all-day water vapor detection are verified. From November 2013 to July 2016, a long-term statistical analysis of atmospheric water vapor content in Xi'an region was carried out based on the data from Lidar remote Sensing Center and Meteorological Station of Xi'an University of Technology. The variation and occupation ratio of water vapor content in different height layers are obtained. The temporal and spatial variation characteristics and seasonal variation characteristics of water vapor content are analyzed, as well as the change trend of water vapor content before and after special weather processes such as rainfall and snow. The correlation between water vapor content and meteorological factors is discussed. The results show that water vapor content is positively correlated with surface temperature and water vapor pressure, negatively correlated with atmospheric pressure, and strongly correlated with precipitation, precipitation days and precipitation efficiency. It provides a powerful basis for the use of lidar detection to guide agricultural production and artificial precipitation.
【学位授予单位】：西安理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P412.25;TN958.98

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