吉林省春季降水性层状云基本结构及降水潜力的初步研究

2001年、2002年利用美国粒子测量系统（PMS）对吉林省5～7月降水性层状云在实施人工增雨作业的同时进行了科研探测,取得了一批资料,对这些资料进行计算后得到以下结论： （1）吉林省春季降水性层状云宏观结构主要分为三种云型即：Ns、As op-Sc op、Astra。 （2）不同云型其云中过冷水含量、云滴数密度不同,其中Ns云型的云中过冷水含量、云滴数密度最大,分别达到0．2737g／m^3、205．3个／L,其次是Asop-Sc op云层,其过冷水含量、云滴数密度分别为0．1693g／m^3、117．5个／L,过冷水含量、云滴数密度最小的云层结构为Astra,分别为0.1054 g／m^3、98．6个／L。 （3）云中过冷水含量与云底高度为负相关,与过冷层厚度为正相关. （4）三种云型的云中过冷水含量、云滴数密度距0℃层高度的分布基本一致：在距0℃层高度以上400-600m高度范围内过冷水含量、云滴数密度达最大,之后随着距0℃层高度的增加云中过冷水含量、云滴数密度迅速减小。 （5）吉林省春季降水性层状云的可播度为86％;三种云型的人工降水潜力有所不同,即：Ns云型潜力最大,达41．3％,其次是As op-Sc op云型潜力为28．4％,潜力最小的云型为As tra,为26．6％。     

图像替换去云的接缝消除研究

利用影像替换的方法去除云层,是提高遥感影像信息的一种有效方法。但是,对于拼接线的消除,不产生影像突变效应是一个必要环节。本文介绍了基于小波变换的拼接缝消除方法及其改进方法一基于拼接线的接缝羽化消除法。该方法能够在影像局部替换完成后,更好地修正拼接缝附近的灰度差异,使之在拼接缝处的灰度有一个光滑的过渡。     

黄河上游降水云层对流特性及降水微结构机制研究

主要利用1997—2000年6～9月在黄河上游河曲地区实施人工增水实验所获取的各类降水云层回波、雨滴谱及多点宏观气象资料，进一步阐明各类云层降水中对流作用的普遍性和特殊性，并且通过全过程的雨滴谱的演变个例，初步揭示了该地区云中降水微物理过程可能存在冷云和暖云两种机制且暖云机制起重要作用。以上是该地区产生降水频繁的重要原因。     

遥感图像薄云滤波增强

针对遥感图像地学应用中的云层覆盖问题,首先介绍了遥感图像去云处理的一般方法,然后重点研究了薄云覆盖图像的滤波增强技术和实现方法。在阐述传统的同态滤波算法的基础上,将高频增强滤波算法引入薄云区遥感图像增强处理中。试验表明,这两种方法均可取得良好的增强效果。     

浅谈如何判别冰粒、霰、米雪

冰粒、霰、米雪、雪、冰雹都属于固态降水,它们中的下降情况、形状及来自的云层、着地的特征有时会相同。在实际工作中,雪和冰雹相对比较容易识别,而冰粒、霰、米雪却容易混淆,可这几种天气现象在冬季又很常见。当有固态降水出现时常把霰认为冰粒,米雪记为雪。记录的正确与否,应根据当时的天气条件,配合存在的云层,下降的特征,形状等来区别。     

Typical Structure, Variety, and Multi-Scale Characteristics of Meiyu Front

Meiyu front plays an important role in summer rainfall in central China. Based on the GMS-5 satellite images, NCEP reanalyses （2.5°×2.5°） and final analyses （1°×1°） data, and meteorological conventional sounding observations, the horizontal and vertical structures of the Meiyu front were summarized using multiple diagnostic variables, including winds, temperature, jet stream, front, pseduo-equivalent potential temperature, divergence, vertical motion, static instability, etc. In this paper, four cases were selected and analyzed, two of which are in 26-28 June and 23 July 2002 during the Experiment on Heavy Rain in the Meiyu period in the lower reaches of the Yangtze River, and the others are in May and July 1998. The two cases in July 1998 and July 2002 are the secondary Meiyu front cases. The results show that the structures and characteristics of the Meiyu front are different for various cases, or at various places and time, or at various stages of one case, and the frontal characteristics can be converted from the polar front to the equatorial front. Because of the interaction of the different scale circulations in the high and low latitudes, the horizontal structure of the Meiyu front has various forms.
The results in this paper also show that the typical Meiyu front consists of a narrow band with a high gradient of potential equivalent temperature below 500 hPa, south of which is warm and moist air mass, and north of which is the transformed air mass from the midlatitude ocean or polar continent. Below the mid troposphere, south of the front blows southwesterlies, while north blows easterlies. The ascending motion and precipitation usually occur ahead of the Meiyu front. In the upper troposphere, the subtropical front is above the Meiyu front, but two fronts are separated. In addition, the upper westerly jet stream and the easterlies to the south of the Meiyu front result in the upper divergent flow field.
The multi-scale characteristics of the horizontal structure of the Meiyu front can     

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 qv 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 km, 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 km. Due to the assumption that the cloud is in static state, there will be some errors in the retrieved variables within the clouds which are rapidly growing or dying-out, and in such cases, more sophisticated radar data control technique will help to improve the retrieval results.     

Application of Radar Reflectivity Factor in Initializing Cloud-Resolving Mesoscale Model. Part Ⅱ： Numerical Simulation Experiments

Microphysics elements and vertical velocity retrieved were incorporated using the nudging method into the initial data assimilation of GRAPES （Global/Regional Assimilation and Prediction System） model. Simulation experiments indicated that nudging technique was effective in forcing the model forecast gradually consistent to the observations, yielding the thermodynamically and dynamically balanced analysis field. As viewed from the simulation results, water vapor is vital to precipitation, and it is a governing factor for the amount and duration of precipitation. The initial cloud water, rain water, and vertical velocity determine the strength distribution of convection and precipitation at the beginning time of forecast; the horizontal wind field steers the motion of the mesoscale weather system embedded in and impacts the position of precipitation zone to a large extent. The simulation experiments show that the influence of the initial retrieval data on prediction weakens with the increase of forecast time, and within the first hour of forecast, the retrieval data have an important impact on the evolution of the weather system, but its influence becomes trivial after the first three hours. Changing the nudging coefficient and the integral time-spacing of numerical model will bring some influences to the results. Herein only one radar reflectivity was used, the radar observations did not cover the whole model domain, and some empirical parameters were used in the retrieval method, therefore some differences still lie between simulation and observation to a certain extent, and further studies on several aspects are expected.     

液态降水记录与云层关系的分析

通过对液态降水与云层、季节、云量、降水性质、各气象要素等之间的关系进行分析,找出它们之间的规律,使液态降水的记录能更准确的反映当时天气状况稳定程度.     

高光谱遥感图像频域自适应同态滤波薄云去除

针对高光谱遥感图像波段多,各波段受云层影响程度不一致,云层本身不均匀等特点,提出了一种频域自适应同态滤波方法.该方法首先检测某个图像云层的范围和对应厚度,根据云层处于低频特点,利用变差函数确定滤波窗口,把云层按照空间邻域大小转换为傅立叶空间,根据云层厚薄调整高通滤波截止频率,然后反变换到空间域.该方法可解决频域空间同态滤波方法破坏无云区的信息,克服空间域滤波方法效果不佳,难以控制和解释的缺陷.结果表明,该方法在保持无云区信息不破坏前提下,较好地改善了不同厚薄云层的质量,计算机内存开销很小,对大数据处理速度较快.