用单站探空资料分析对流层气柱水汽总量

使用北京地区１９９６,１９９７年７月中至８月上旬常规探空（标准层和特性层）资料,计算了各层气柱的水汽混合比,绘制了水汽垂直分布的廓线,并据水汽廓线分布把大气分为四种类型,通过对各层水汽混合比积分,算出该时段每日０８,２０时的北京单站对流层气柱累积水汽量,研究气柱水汽总量与降水的关系,为预报降水量级,降水范围提供依据,这一分析结果可与辐射仪遥感水汽技术得到的结果进行比较,并为应用其它新的探测技术（如     

长江三角洲城市群对夏季降水影响机制的模拟研究

利用耦合了单层城市冠层模型UCM的中尺度模式WRF,对长江三角洲城市群夏季城市化效应进行了5 a(2003—2007年)高分辨数值模拟,并作了长江三角洲地区有无城市的对比试验。结果表明,城市化使得长三角城市群及其邻近地区,夏季近地层水汽混合比呈明显减少趋势,而850~700 h Pa层的水汽混合比有所增大。通过对比有与无城市各等级降水日所对应的2 m水汽混合比,得出小雨降水日对应的2 m水汽混合比差异与总降水日对应的差异最为接近。通过分析环流场、散度场和垂直速度场发现,水汽混合比的垂直变化是由于城市群的存在使得近地层辐合、850~700 h Pa层辐散的配置增强,以及在城市群上空增强了的垂直上升运动,从而增强城区对流活动,水汽的垂直输送也更为活跃,由此可能导致对流性降水的增加。     

合肥市郊低层大气的激光雷达探测研究

利用L300米散射激光雷达对合肥市郊大气边界层进行长期系统观测.分析讨论了大气边界层气溶胶消光系数与温度、湿度的关系, 大气边界层气溶胶消光系数垂直分布和时间变化的主要特征, 给出了激光雷达探测的大气边界层高度的统计特征及其与无线电气象探空仪探测大气边界层高度的比较结果.     

利用HALOE资料分析中层大气中水汽和甲烷的分布特征

利用1991年12月至2004年5月的HALOE资料，分析了中层大气中微量气体水汽和甲烷的垂直和水平分布特征。垂直分布特征是:水汽混合比在对流层顶和平流层底达到极小值（此极小值区被称为湿层顶），平流层里水汽混合比随高度增加，在平流层上层和中间层低层混合比出现明显的扰动，在中间层顶再次达到极小值，向上混合比又随高度增加。甲烷混合比从100 hPa附近向上混合比一直减少。经向分布特征主要表现为:平流层中下层水汽混合比低值区在热带地区上拱，水汽混合比自低纬向高纬递增；而该气层甲烷混合比则是高值区在热带地区上拱，甲烷混合比自低纬向高纬递减。在低平流层副热带20°S～30°S（20°N～30°N）附近二者混合比水平梯度相对偏大。平流层中上层二者等值线在北半球夏季变成双峰形势，北半球冬季仍是单峰形势。中间层二者都主要表现为冬、夏季分布形势相反。在北半球夏季30°N，平流层中下层水汽和甲烷混合比纬向梯度很小，对流层上层以及中间层二者混合比纬向梯度明显。     

预估临边探测大气温度和水汽廓线的反演精度

针对在研仪器——大气辐射超高光谱探测仪的临边探测模式,模拟计算了大气温度和水汽的权重函数。以此为基础,利用信息量和权重函数线性化方法,结合仪器的可探测亮温阈值0.3 K,计算并分析6种大气状态下,大气温度和水汽混合比廓线在不同反演精度条件下可获得的光谱通道数,在满足最佳光谱通道数200的要求下,理论上预估其反演精度。温度廓线整体反演精度为0.6 K,水汽混合比廓线反演精度可达到5%,但热带大气在16~20 km高度的水汽廓线反演精度仅为10%。反演精度预估,仅提供了一种全面认识仪器性能的方法,精度的确定还有赖于真实探测数据的获取和反演方法。     

基于一维变分算法的红外高光谱(IASI)卫星遥感大气温湿廓线研究

大气温湿度廓线是大气重要参数,在数值天气预报及天气预警中具有重要的应用价值。为获得高精度的大气温度与水汽混合比廓线数据,研究了基于Metop-A/IASI红外高光谱资料的大气温度与水汽混合比廓线变分反演方法。利用IASI高光谱传感器温度和水汽探测通道资料,结合CRTM模式和WRF模式预报技术,使用一维变分方法,研究了卫星资料质量控制、背景误差协方差本地化、观测误差协方差计算等方法,构建了大气温度及水汽混合比廓线变分反演系统,并在北京、青岛、沈阳3个地区开展了反演试验。以探空为标准的反演结果对比显示,使用WRF模式预报值作为背景场,温度的平均误差绝对值小于0.6 K,均方根误差为0.89 K;水汽混合比的平均误差绝对值小于0.021 g/kg,均方根误差为0.02 g/kg。试验结果表明:基于一维变分方法,可以利用Metop-A/IASI红外高光谱资料进行大气温度与水汽混合比廓线高精度探测。     

激光雷达在气象和大气环境监测中的应用

论述了激光雷达的结构、分类以及不同种类激光雷达的工作原理。跟踪激光雷达最新的发展和应用,根据探测物质的不同,分别讨论了激光雷达在探测气溶胶、云、边界层、温度、能见度、风、大气成分、水汽和钠层方面的应用,综述了目前国外星载激光雷达的发展和新的应用动态。进一步讨论了在激光雷达探测中存在的问题和今后的发展趋势,认为激光雷达将向多波长、多探测功能、商业化、区域化及全球化方向发展,它在气象与环境监测中正在发挥越来越重要的作用。     

气象激光雷达的发展现状

激光雷达能用来测量云、雾、能见度、空中风场、大气密度和大气温度,也可以用来监测空气中的有害气体。该文介绍了用于气象探测的半导体激光雷达、微脉冲激光雷达、一般弹性散射激光雷达、多普勒激光雷达、差分吸收激光雷达和Raman激光雷达的发展现状和趋势,认为人眼安全、高重复频率和宽探测范围是气象激光雷达的发展方向。固体激光雷达的气象应用最广泛,微脉冲激光雷达能同时测量云底高度、能见度和获得大气气溶胶消光廓线,有比较好的应用前景,半导体激光雷达是测量云底高度的理想工具。     

高纬度强对流与对流层上层水汽变化关系的数值模拟研究

应用WRF中尺度模式模拟了发生在黑龙江省西南部的一次区域性暴雨过程,通过云微物理参数化方案的敏感性试验,分析了对流云体中水汽垂直输送特征.结果表明:强对流活动使对流层上层局地水汽平均增加10倍以上,对流活动对于水汽的垂直输送以及对高层水汽含量的改变具有非常显著的作用.云微物理参数化方案,对于整个对流层水汽通量密度变化趋势有较好的表现.在不同方案中,0.5 ～9 km水汽通量密度及24 h总水汽垂直输送量,随高度变化差异较大.这是由平均垂直速度对不同方案敏感性造成的,不同的参数化方案,水汽通量最大值间最多相差可达27.9％.在不同的方案中,对流层上层加湿作用持续时间和对流层上层平均水汽混合比的最大值较敏感:对流活动可造成模拟区域对流层上层增湿持续16 ～20 h不等;对流层上层平均水汽混合比的最大值差异明显,最大可达15.8％.进行24 h平均后,上述物理量对方案的敏感性可减小到8.3％.所以,云微物理方案的不确定性对于暴雨过程的时间尺度是不可忽视的.     

多普勒激光雷达探测台风“利奇马”边界层风场精度分析

多普勒激光雷达在台风等强天气背景下的探测能力亟待研究,为此将多普勒激光雷达与70 m测风塔超声风温仪在同址同高度探测台风"利奇马"影响期间的边界层风场数据进行对比,并分析多普勒激光雷达的误差分布以及变化情况。结果显示:在高度70 m上,两者的水平风速、风向相关系数分别为0.97和0.99,垂直风速的相关系数为0.36。以超声风温仪为参考值,激光雷达水平风速、垂直风速和风向均方根误差分别为1.06 m/s、0.46 m/s和17.10°。深入研究表明:降水对多普勒激光雷达测量水平风速和垂直风速误差均有一定影响。当激光雷达信噪比大于2 000时,各参量的误差与信噪比呈负相关关系。研究表明多普勒激光雷达至少可以较好地刻画台风环流内的水平风场结构及演变,可应用于台风外围环流影响下(即较弱降雨条件下)边界层风场的高分辨率探测和研究。     

TROPOSPHERIC WATER VAPOR OBSERVATIONS USING A RAMAN LIDAR OVER HEFEI*

This paper presents a Raman lidar for measuring tropospheric water vapor profiles over Hefei(31.9°N,117.17°E),China.Intercomparisons of water vapor mixing ratio obtained by this Raman lidar and GZZ-59 type radiosonde observations show the good agreements when relative humidity is higher than 20%.Typical vertical profiles and seasonal variations of water vapor mixing ratio distribution are reported.Many observation eases indicate that the high moisture layer corresponds to large aerosol scattering ratios in the lower troposphere.     

Coments on “derivation of water vapor fluxes from lidar measurements” by W. E. Eichingeret al.

Eichingeret al. (1993) discuss the use of a Raman lidar for measurements of water vapor fluxes and suggest that the method is superior to the use of standard micrometeorological, point sensor measurements. Although the lidar shows considerable promise for some purposes, we believe that their criticisms of point sensors are not warranted; that there are difficulties with their application of dissipation techniques to extract fluxes; and that they misinterpret the existence of plume structures as statistically significant spatial variations of turbulent fluxes.     

Temperature and Water Vapor Weighting Functions from Radiative Transfer Equation with Surface Emissivity and Solar Reflectivity

Linearization of Radiative Transfer Equation (RTE) is the key step in physical retrieval of atmospheric temperature and moisture profiles from InfRared (IR) sounder observations. In this paper, the successive forms of temperature and water vapor mixing ratio component weighting functions are derived by applying one term variation method to RTE with surface emissivity and solar reflectivity contained. Retrivals of temperature and water vapor mixing ratio profiles from simulated Atmospheric Infrared Sounder (AIRS) observations with surface emissivity and solar reflectivity are presented.     

The retrieval of the Asian dust depolarization ratio in Korea with the correction of the polarization-dependent transmission

The linear particle depolarization ratios were retrieved from the observation with a multiwavelength Raman lidar at the Gwangju Institute of Science and Technology (GIST), Korea (35.11°N, 126.54°E). The measurements were carried out in spring (March to May) 2011. The transmission ratio measurements were performed to solve problems of the depolarization-dependent transmission at a receiver of the lidar and applied to correct the retrieved depolarization ratio of Asian dust at first time in Korea. The analyzed data from the GIST multiwavelength Raman lidar were classified into three categories according to the linear particle depolarization ratios, which are pure Asian dust on 21 March, the intermediate case which means Asian dust mixed with urban pollution on 13 May, and haze case on 10 April. The measured transmission ratios were applied to these cases respectively. We found that the transmission ratio is needed to be used to retrieve the accurate depolarization ratio of Asian dust and also would be useful to distinguish the mixed dust particles between intermediate case and haze. The particle depolarization ratios of pure Asian dust were approximately 0.25 at 532 nm and 0.14 at 532 nm for the intermediate case. The linear particle depolarization ratios of pure Asian dust observed with the GIST multiwavelength Raman lidar were compared to the linear particle depolarization ratios of Saharan dust observed in Morocco and Asian dust observed both in Japan and China.     

Can Water Vapour Raman Lidar Resolve Profiles of Turbulent Variables in the Convective Boundary Layer?

High-resolution water vapour measurements made by the Atmospheric Radiation Measurement (ARM) Raman lidar operated at the Southern Great Plains Climate Research Facility site near Lamont, Oklahoma, U.S.A. are presented. Using a 2-h measurement period for the convective boundary layer (CBL) on 13 September 2005, with temporal and spatial resolutions of 10 s and 75 m, respectively, spectral and autocovariance analyses of water vapour mixing ratio time series are performed. It is demonstrated that the major part of the inertial subrange was detected and that the integral scale was significantly larger than the time resolution. Consequently, the major part of the turbulent fluctuations was resolved. Different methods to retrieve noise error profiles yield consistent results and compare well with noise profiles estimated using Poisson statistics of the Raman lidar signals. Integral scale, mixing-ratio variance, skewness, and kurtosis profiles were determined including error bars with respect to statistical and sampling errors. The integral scale ranges between 70 and 130 s at the top of the CBL. Within the CBL, up to the third order, noise errors are significantly smaller than sampling errors and the absolute values of turbulent variables, respectively. The mixing-ratio variance profile rises monotonically from ≈0.07 to ≈3.7 g² kg⁻² in the entrainment zone. The skewness is nearly zero up to 0.6 z/z _i , becomes −1 around 0.7–0.8 z/z _i , crosses zero at about 0.95 z/z _i , and reaches about 1.7 at 1.1 z/z _i (here, z is the height and z _i is the CBL depth). The noise errors are too large to derive fourth-order moments with sufficient accuracy. Consequently, to the best of our knowledge, the ARM Raman lidar is the first water vapour Raman lidar with demonstrated capability to retrieve profiles of turbulent variables up to the third order during daytime throughout the atmospheric CBL.     

Estimation Of Atmospheric Water Vapour Flux Profiles In The Nocturnal Unstable Urban Boundary Layer With Doppler Sodar And Raman Lidar

The vertical wind profiles determined by Doppler sodar and the water vapourmixing ratio profiles obtained by Raman lidar are used to estimate the atmosphericwater vapour flux profiles in the nocturnal urban boundary layer under unstableconditions. The experiment was conducted for several nights in the central areaof Rome under a variety of moisture conditions and different urban boundary-layerflow regimes. Despite some scatter in the profiles, the latent heat flux is found tobe positive throughout the depth of the nocturnal urban boundary-layer. Thelayer-averaged flux shows a variation between -4 to +40 W m^-2, whileindividual values of flux in excess of +150 W m^-2 pertain to a case offree convection during cold air advection caused by the sea breeze. The qualityof flux estimates is found to be highly limited by the low sampling rates employedin the experiment resulting in errors to the order of 60%. Therefore, the results mustbe viewed as estimates rather than precise measurements. The skewness profiles ofthe turbulent fluctuations of vertical velocity and water vapour mixing ratio are alsopositive.     

Application of Radar Reflectivity Factor in Initializing Cloud-Resolving Mesoscale Model. Part Ⅰ: Retrieval of Microphysical Elements and Vertical Velocity

Assuming that cloud reaches static state in the warm microphysical processes, water vapor mixing ratio(qv), cloud water mixing ratio (qc), and vertical velocity (w) can be calculated from rain water mixing ratio (qr)- Through relation of Z-qr, qr can be retrieved by radar reflectivity factor (Z). Retrieval results indicate that the distributions of mixing ratios of vapor, cloud, rain, and vertical velocity are consistent with radar images, and the three-dimensional spatial structure of the convective cloud is presented. Treating q,v saturated at the echo area, the retrieved qr is about 0.1 g kg-1, qc is always less than 0.3 g kg-1, w is usually below 0.5 m s-1, and rain droplet terminal velocity (vr) is around 5.0 m s-1 in the place where radar reflectivity factor is about 25 dBz; in the place where echo is 45 dBz, the retrieved qr and qc are always about 3.0 g kg-1, w is greater than 5.0 m s-1, and vr is around 7.0 m s-1. In the vertical, the maximum updraft velocity is greater than 3.0 m s-1 at the height of around 5.0 kin, the maximum cloud water content is about 3.0 g kg-1 above 5 km and the maximum rain water content is about 3.0 g kg-1 below 6 kin. Due to the assumption that the cloud is in static state, there will be some errors in the retrieved variables within the clouds which axe rapidly growing or dying-out, and in such cases, more sophisticated radar data control technique will help to improve the retrieval results.     

激光雷达和探空反演的混合层高度对比分析

本文利用2016年12月至2017年11月期间晴朗少云天气下的成都微脉冲激光雷达观测数据反演的混合层高度,与温江探空资料确定的混合层高度进行了对比和误差分析,结果表明:基于探空资料和激光雷达数据反演的混合层高度具有较好的一致性,两者相关系数达0.75,激光雷达反演的混合层高度略低于基于探空资料确定的值,在混合层高度为1000～2000m时,两种方法计算所得的值偏差幅度最小,约为20%;在1000m以内和2000m以上,偏差幅度略有增大,为26%;两种方法反演的混合层高度变化趋势较为一致,均呈现出12月、1月较低,4月、5月较高的特点;混合层高度具有明显的日变化特征:上午混合层高度迅速增高,午后增长速度减慢并发展到最大高度,日落后迅速降低;混合层内相对湿度的增加、残留层的存在是导致激光雷达反演混合层高度时产生较大误差的原因之一。