雨滴谱的变化对降水估测的影响研究

选取2013年5月20日发生在广东三水的一次飑线过程作为研究对象,首先结合飑线回波带移经三水及其上空雷达回波的时间-高度分布特征将降水过程划分为3个阶段,然后通过计算各时刻的粒子总数密度、中值体积直径和峰值数分析降水过程雨滴谱的变化,再对Z-R关系(Z=aR~b)进行分析,根据雨滴谱实测资料分别统计整体Z—R关系和3个降水阶段的Z—R关系,在此基础上讨论雨滴谱的变化和雷达观测的回波强度对降水估计的影响。结果表明:中值体积直径在对流云降水阶段和层状云降水阶段基本一致,但对流云降水阶段的粒子总数密度远大于层状云降水阶段;对流云降水阶段以双峰型为主,当降水向层状云类型发展时,多峰谱比例增加;雷达观测的回波强度常低于雨滴谱计算的反射率因子,离地面越近两者的相关性越好;根据3个降水阶段分别进行Z—R关系拟合,即分型Z-R关系,通过相对误差分析可知,利用分型Z—R关系反演雨强的效果明显优于整体Z-R关系反演效果,雨滴谱在层化降水阶段估计的相对误差最小、对流云降水阶段反演精度稍低于层状云降水阶段,这与对流云降水中雨强和雨滴谱谱型变化大且快有关;在雷达观测方面,利用分型Z-R关系反演雨强的相对误差较小而雷达观测的误差在对流云降水阶段较小,当降水向层状云降水转化时,雷达观测引起的相对误差增大,这主要是由于对流云降水阶段中雨滴谱仪和雷达对应的回波强度误差最小,也与雷达观测精度、两种仪器采样的时空差异和雨滴谱特征变化等因素有关。     

山西汾阳地区层状云和对流云降水雨滴谱特征

基于多普勒天气雷达和OTT Parsivel激光雨滴谱仪资料对山西汾阳地区2次降水进行分析,对比对流云和层状云降水的雨滴谱特征。结果表明:层状云降水雨滴平均数浓度和雨强分别为286.20个·m~(-3)和1.33 mm·h~(-1),对流云降水雨滴平均数浓度和雨强分别为516.13个·m~(-3)和10.17 mm·h~(-1);对流云降水雨强主要由降水粒子数浓度决定,直径为1—2 mm的粒子对2种云系雨强贡献最大;2种云系不同雨强下雨滴谱分布和雨滴平均谱分布均呈单峰型,对流云降水雨滴平均谱宽大于层状云降水雨滴平均谱宽,Gamma分布对2种云系降水平均谱拟合均存在一定偏差;通过雨滴谱计算的雷达反射率因子估算降水会造成对降水的低估。     

辽宁地区不同降水云系雨滴谱参数及其特征量研究

利用位于辽宁省沈阳市和辽阳市的Parsivel（Particle Size and Velocity）激光雨滴谱仪观测到的雨滴谱资料按照积雨云降水系统、积层混合云降水系统和层状云降水系统分析雨滴谱特征量和谱参数的平均特征及随时间的演变特征。结果表明:Gamma分布拟合谱参数N₀和λ按照层状云、积层混合云和积雨云的顺序减小,谱参数μ按照层状云、积层混合云和积雨云的顺序增大;直径小于1 mm的降水粒子对数浓度的贡献最大,直径大于1 mm的降水粒子对雷达反射率的贡献最大;M-P分布的谱参数N₀与雨强I具有幂函数关系,并且随着雨强I的增大而增大,谱参数λ与雨强I具有指数函数关系,随着雨强I的增大而减小。     

一次飑线过程的雨滴谱特征研究

利用4台Thies激光雨滴谱仪组成的观测网和CINRADA/SA多普勒雷达观测资料,通过单点雨滴谱和积分参数时间序列分析、以及γ谱拟合参数和Z-R关系等的统计分析,研究成熟平行层状飑线不同部位雨滴谱和积分参数的演变特征。结果表明,不同部位雨滴谱和积分参数演变特征存在明显差别,但有一致的基本特征,即在雨强增大阶段为较小的小粒子数浓度,较大的大粒子数浓度和谱宽,而雨强减弱阶段为较大的小粒子数浓度,较小的大粒子数浓度和谱宽,所以,雨强增大阶段具有较低的雨滴浓度和较大的雷达反射率因子,以及较小的γ谱斜率参数λ和形状参数μ。但有时雨强减弱阶段存在较大的大粒子数浓度和谱宽,因此,具有较大的雷达反射率因子;统计表明,γ谱三参数N0、μ、λ与雨强的关系可以用幂函数拟合, N0随雨强增大而增大,μ、λ随着雨强的增大而减小。λ-μ关系可以用二次多项式拟合,对流云Z-R关系为Z=324R1.60。不同部位雨滴谱演变特征的差异对Z-R关系等统计关系影响明显,但对λ-μ关系影响较小。平行层状飑线不同部位雨滴谱和积分参数演变特征与拖曳层状飑线对流带典型雨滴谱演变特征类似,但也存在一些明显差别,这些差异是否与平行层状飑线动力结构的不同有关,尚需进一步的研究。      

昭苏夏季层状云和对流云降水的雨滴谱特征

利用2020—2021年昭苏地区夏季的雨滴谱数据，研究层状云和对流云降水的微物理参量及雨滴谱特征。结果表明：对流云降水的粒子数浓度和粒子直径明显偏大，较大的粒子直径和粒子数浓度使得其降水强度和液态含水量远大于层状云降水。两类降水云的雨滴谱均为单峰结构，峰值直径主要分布在0.5～0.625 mm，对流云降水的雨滴谱谱宽明显大于层状云降水。两类降水云的雨滴直径和粒子数浓度与青藏高原中部的观测值相近，且昭苏地区的对流云滴谱更倾向于大陆性对流簇。研究结果有助于加深对昭苏地区降水的微物理特征及其演变规律的理解。     

广东两次飑线过程的微物理特征分析研究

基于中国气象局龙门云物理野外科学试验基地2DVD（Two-Dimensional Video Disdrometer）雨滴谱观测资料, 分析广东地区2017年5月4日（槽前型飑线）和2017年8月22日（东风型飑线）两次不同飑线系统不同降水类型的雨滴谱特征。根据雨强和雷达反射率随时间变化将降水分成对流降水和层云降水, 同时以20 mm/h为阈值将对流降水划分为对流前沿、对流中心和对流后沿。结果表明, 两次飑线系统在不同降水时期的微物理特征参数变化有所差异。槽前型飑线过程中, 对流降水的粒子分布较为分散, 中等粒径的粒子比重较高, 且对流区前半部分粒子尺寸大于“大陆性”对流特征, 后半部分粒子尺寸小于“海洋性”对流特征; 层云降水的粒子分布较为集中, 小粒径粒子居多。而东风型飑线整个降水时期基本上是由高浓度中小粒径粒子组成, 降水粒子粒径分布较为集中, 对流降水粒子介于“海洋性”和“大陆性”对流区之间。      

拉萨夏季一次典型降水过程雨滴谱特征及Z-I关系分析

研究降水滴谱特征和谱分布对了解高原降水的微物理特征、雷达定量估测降水以及科学实施人工增雨作业尤为重要。本文选取2018年7月8—9日拉萨夏季一次典型降水过程，利用DSG5型降水现象仪和地面小时降水资料分析了高原夏季对流云和混合云降水的雨滴谱分布特征及〖WTBX〗Z I〖WTBZ〗关系。结果表明：降水现象仪和翻斗雨量计的降水变化趋势较为一致。对流云降水中2.0~3.0 mm的降水粒子对雨强的贡献最大，混合云降水雨强的主要贡献者是1.0~2.0 mm的粒子；混合云降水阶段的雨滴谱数浓度比对流云大一个量级。对流云和混合云降水的雨强与雨滴的质量加权平均直径和数浓度密切相关。拉萨地区雨滴谱适合〖WTBX〗Γ〖WTBZ〗分布，其拟合谱参数与青藏高原其他地区的差异表明高原地区雨滴谱分布存在时空差异；混合云降水谱参数〖WTBX〗N0、μ〖WTBZ〗和〖WTBX〗λ〖WTBZ〗与雨强的变化趋势相反。混合云降水〖WTBX〗Z I〖WTBZ〗关系的系数和指数均小于对流云降水。应用标准〖WTBX〗Z I〖WTBZ〗关系，对流云降水阶段雷达低估降水强度；混合云降水阶段，当雨强<2.3 mm雷达低估降水，否则雷达高估降水。     

广东东莞不同类型云的雨滴谱和降水特征

利用设置在东莞站的Parsivel激光降水粒子谱测量系统于2010年获取的观测资料,对数据有效性进行验证并开展15次降水的特征参量分析。层状云降水（S）、积层混合云降水（M）、积雨云降水（C）各选取两次典型过程,对各种特征参量之间的相关性和雨滴谱特征进行细致分析,结果表明：平均粒子直径、平均雨强、平均雨水含量、过程最大立方根直径、过程最大雨强的分布规律明显,基本遵循“C>M>S”的规律;同种类型降水的雨滴谱型非常接近,层状云存在单峰谱,混合云和积状云是明显的双峰谱或多峰谱; M、C型降水的大雨滴明显多于S型降水;雨水含量与雨强的相关性最好,雷达反射率因子与雨强的相关性次之;层状云降水主要为1 mm以下小粒子,积状云和积层混合云降水雨滴谱宽较大,1 mm以上大粒子数浓度较大。     

庐山层状云和对流云雨滴谱比较分析

本文从庐山近3 a的雨滴谱观测资料中选取数例层状云降水和对流云降水个例,通过对两类云降水的平均雨滴谱分析拟合、各微物理量演变以及速度谱的对比研究,得出以下结论:庐山对流云降水的平均雨滴谱很宽,有直径大于10 mm的大雨滴出现。Γ分布,对层状云降水拟合,较MP分布差,而对对流云降水拟合,较MP分布好。两类云降水的雨强变化都是由最大雨滴直径和粒子数浓度共同决定的,但对于对流云降水,最大雨滴直径的决定作用更为重要。雨滴直径较小时,两类云降水的实测速度大于经验公式值;而雨滴直径较大时,实测速度值分布在经验公式曲线两侧,但对流云降水的分布偏差要大于层状云降水。     

太原地区雨滴谱季节分布特征北大核心CSCD

利用2017年12月-2022年11月太原雨滴谱数据,研究太原地区不同雨强和不同降水类型雨滴谱季节分布特征。结果表明:太原地区四季谱分布均呈单峰结构且均以雨滴直径D<1 mm的小雨滴为主,但对雨强R贡献最大的是D为1~2 mm的雨滴。各季节R<1 mm·h^(-1)的降雨均占比最大,但夏季超过50%雨量来自R≥5 mm·h^(-1)雨滴的贡献;R<2 mm·h^(-1)时,冬季大雨滴浓度更高,而小雨滴浓度相对较低;R≥5 mm·h^(-1)时,夏季雨滴浓度更高。四季均以层状云降水为主,标准化截距参数lgNw和质量加权直径D_(m)差异较小;对流云降水多发生在夏季且更接近海洋性对流,春、秋季既非大陆性也非海洋性对流。采用最小二乘法得到形状因子与斜率参数的μ-λ、降水动能以及反射率因子与雨强的Z-R关系曲线,其中μ-λ季节变化小,但地域性差异显著;幂函数和二项式函数分别对于降水动能参数关系E_(t)-R和E_(d)-D_(m)拟合效果更优;Z-R关系系数与指数成反比,对于层状云降水,春、秋季经典关系均高估降雨,冬、夏季存在经典关系由高估转为低估的情况;对于对流云降水,夏、秋季经典关系略高估降雨。     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics.     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

正The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth's climate and environment.     

ANALYSIS OF “BIMODAL PATTERN” STORM ACTIVITY CHARACTERISTICS OF THE BAY OF BENGAL

Storms that occur at the Bay of Bengal (BoB) are of a bimodal pattern, which is different from that of the other sea areas. By using the NCEP, SST and JTWC data, the causes of the bimodal pattern storm activity of the BoB are diagnosed and analyzed in this paper. The result shows that the seasonal variation of general atmosphere circulation in East Asia has a regulating and controlling impact on the BoB storm activity, and the “bimodal period” of the storm activity corresponds exactly to the seasonal conversion period of atmospheric circulation. The minor wind speed of shear spring and autumn contributed to the storm, which was a crucial factor for the generation and occurrence of the “bimodal pattern” storm activity in the BoB. The analysis on sea surface temperature (SST) shows that the SSTs of all the year around in the BoB area meet the conditions required for the generation of tropical cyclones (TCs). However, the SSTs in the central area of the bay are higher than that of the surrounding areas in spring and autumn, which facilitates the occurrence of a “two-peak” storm activity pattern. The genesis potential index (GPI) quantifies and reflects the environmental conditions for the generation of the BoB storms. For GPI, the intense low-level vortex disturbance in the troposphere and high-humidity atmosphere are the sufficient conditions for storms, while large maximum wind velocity of the ground vortex radius and small vertical wind shear are the necessary conditions of storms.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.     

AN ATTRIBUTION ANALYSIS OF CHANGES IN POTENTIAL EVAPOTRANSPIRATION IN THE BEIJING–TIANJIN–HEBEI REGION UNDER CLIMATE CHANGE

Based on the measurements obtained at 64 national meteorological stations in the Beijing–Tianjin–Hebei (BTH) region between 1970 and 2013, the potential evapotranspiration (ET0) in this region was estimated using the Penman–Monteith equation and its sensitivity to maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Vw), net radiation (Rn) and water vapor pressure (Pwv) was analyzed, respectively. The results are shown as follows. (1) The climatic elements in the BTH region underwent significant changes in the study period. Vw and Rn decreased significantly, whereas Tmin, Tmax and Pwv increased considerably. (2) In the BTH region, ET0 also exhibited a significant decreasing trend, and the sensitivity of ET0 to the climatic elements exhibited seasonal characteristics. Of all the climatic elements, ET0 was most sensitive to Pwv in the fall and winter and Rn in the spring and summer. On the annual scale, ET0 was most sensitive to Pwv, followed by Rn, Vw, Tmax and Tmin. In addition, the sensitivity coefficient of ET0 with respect to Pwv had a negative value for all the areas, indicating that increases in Pwv can prevent ET0 from increasing. (3) The sensitivity of ET0 to Tmin and Tmax was significantly lower than its sensitivity to other climatic elements. However, increases in temperature can lead to changes in Pwv and Rn. The temperature should be considered the key intrinsic climatic element that has caused the "evaporation paradox" phenomenon in the BTH region.     

Revisiting the Concentration Observations and Source Apportionment of Atmospheric Ammonia

正While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

Analysis of Atmospheric Boundary Layer Height Characteristics over the Arctic Ocean Using the Aircraft and GPS Soundings

正ERRATUM to: Atmospheric and Oceanic Science Letters, 4(2011), 124-130 On page 126 of the printed edition (Issue 2, Volume 4), Fig. 2 was a wrong figure because the contact author made mistake giving the wrong one. The corrected edition has been updated on our website. The editorial office is sincerely sorry for any     

Index to Vol.31

正AN Junling;see LI Ying et al.;(5),1221—1232AN Junling;see QU Yu et al.;(4),787-800AN Junling;see WANG Feng et al.;(6),1331-1342Ania POLOMSKA-HARLICK;see Jieshun ZHU et al.;(4),743-754Baek-Min KIM;see Seong-Joong KIM et al.;(4),863-878BAI Tao;see LI Gang et al.;(1),66-84BAO Qing;see YANG Jing et al.;(5),1147—1156BEI Naifang;