基于CloudSat资料的中国及周边地区云垂直结构统计分析

利用2006年7月-2009年2月的CloudSat 2B-GEOPROF-LIDAR资料,分析了中国及周边地区(0°～60°N,70°～140°E)的云垂直结构分布特征,并根据气候特征的地域差异从该区域选出8个子区域,逐区统计了云垂直结构特征.结果表明:整个研究区域内61%的云为单层云,39%的云为多层云,其中77%...     

基于ERA-Interim的中国云水量时空分布和变化趋势

利用欧洲中期天气预报中心(ECMWF)发布的新一代全球分辨率ERA-Interim再分析数据,用九点平滑、一元线性回归法分析了1979-2016年中国云水量时空分布特征和变化趋势。结果表明:(1)中国云水含量和云液水含量大值区主要位于四川东部-湖南850～500 h Pa,量值达0. 015～0. 045 g·kg~(-1),这一分布与该地区层状云的富集有关。云冰水含量大值区主要位于中东部地区(27°N-35°N,97°E-110°E) 500～250 hPa,量值达0. 006～0. 025 g·kg~(-1)。三者小值区均位于西北地区西部。(2)中国多年平均整层云水量无明显线性趋势。春季云水量呈略减少,秋、冬季呈略增加趋势,夏季无明显趋势。云水量有明显年际变化,夏季年际变化远小于其他季节;干旱区、半干旱区整层云液态水含量的年际变化大于湿润区,云冰水含量相反。云水量空间变化呈西增东减趋势。(3)云水量大值区对应水汽输送辐合和低层上升运动,且对流层中低层水汽通量散度可在一定程度上表征云水含量。从而为认识和理解气候变化对中国水资源的影响提供一定依据。     

基于径向基函数网络的云自动分类研究

采用 GMS- 5红外 ( 1 0 .5～ 1 2 .5μm)和可见光 ( 0 .55～ 0 .9μm)两通道资料 ,采集了 1 999年 7— 1 0月中国东南沿海 57区、58区和 59区包括晴空在内的 1 2类云目标样本 2 91 2个 ,采样窗尺寸为 8× 8像素 ,随机生成训练和测试两个样本子集。对径向基函数网络 ( radial base function neural network,RBF)在云分类问题研究中的应用价值进行了全面的测试与分析 ,得到了肯定的结论 ,提出了优化设计的方法。对6类云型分类试验 ,平均正确率为 86 % ;对 1 1类云型分类试验 ,平均正确率为6 7%。采用自组织竞争神经网络实现寻找 RBF神经网络的隐层神经元中心。在特征空间生成过程中 ,采用小波包分解算法实现模式特征抽出。结果表明 ,小波包分解特征能很好地描述不同云型的差异。     

基于神经网络和分形纹理的夜间浓雾遥感监测技术

利用地物光谱信息和图像纹理信息作为地物分类识别标志，将分形理论和BP神经网络应用于夜间浓雾的遥感监测，使夜间浓雾的监测精度明显提高。与传统最大似然法(MLC)比较，晴空地表、雾区、云区的识别精度均有提高，特别是云区的识别精度提高了10%，基于灰度连通域的灰度加权计盒维数图像纹理提取技术使云雾边界的提取更加合理，文章最后对类的归并作了讨论。     

用三通道合成彩色图像进行云的分类解释判读

为更好地利用FY-3A气象卫星上中分辨率光谱成像仪(MERSI)资料高空间分辨率及多光谱的优势,细致分析云系在宏、微观方面的多重特征,首先利用平面平行辐射传输模式(SBDART)证明了MERSI的0.65,1.6 μm和11.25 μm通道能够分别反映云光学厚度、云粒子大小、云顶高度的信息,然后采用三通道合成彩色图像的方法,对FY-3A气象卫星云图进行云的分类解释判读.该技术可直观区分有云区、无云区,显示海陆分界,并且使不同云类在云图上体现为不同颜色.同时,FY-3A气象卫星高达1000 m甚至250m的空间分辨率也使云的细致结构更为清晰,两项优势的叠加,大大提升了云的分类解释判读的准确程度和精细化水平.文中还尝试寻找典型云系的三通道特征值,以期为模糊C均值聚类(FCM)方法中聚类中心的选定提供经验值参考.     

基于模糊纹理光谱的全天空红外图像云分类

为了对全天空红外测云系统获得的红外图像进行云类自动识别, 提出了基于模糊纹理光谱结合云物理属性的全天空云类识别方法。首先根据不同滤波窗口的模糊纹理光谱图像特征, 确定了滤波窗口大小, 然后通过分析不同天空类型下的FUTS谱 (fuzzy uncertainty texture spectrum) 以及同一种天空类型下的FUTS谱, 考察了FUTS进行云类识别的适用性, 最后利用最小距离分类法和云基本物理属性对全天空红外图像进行了分类测试。在200个测试样本中, 层状云、积云、高积云、卷云和晴空的识别率分别为100%, 100%, 90%, 100%, 100%, 平均识别率达到98%。基于模糊纹理光谱的云分类算法对单一云空具有很好的分类效果, 可进一步应用于全天空红外图像的云分类识别。     

华南冷季暴雨中热带卷云羽特征研究

冷季暴雨特别是南方冷季暴雨不但常有发生,其强度和灾害甚至不亚于暖季暴雨。统计表明约有80%的华南冷季强降水与热带卷云羽(简称TP)活动相对应。为此,本文利用近7年FY-2静止气象卫星、TRMM卫星合成降水率数据、ECMWFInterim再分析资料以及地面与探空的常规观测资料等,综合分析了与华南冷季暴雨相关的热带卷云羽的相关特征。分析结果表明,与华南冷季降水有关的TP云产生的源地主要集中在0°~10°N、70°~140°E之间区域内。青藏高原大地形作用对TP引起的华南冷季降水分布具有不可忽视的作用。Hovm6ller图分析显示,TP云在爆发前,云系大多呈连续西传的特征,传播速度约为15~20 m·s~(-1),与大尺度斜压波波速相当或略快。一般来说,与弱降水相比,引起强降水的TP云在爆发前移速较快,受来自对流层中上层的动力强迫影响较大。TP云爆发后,云内强对流区总体移动速度不快,大部分集中在TP云的西南端较小范围内。副热带高空急流只是使得TP云的云盖快速向东扩展。上述特征可能是造成华南冷季强降水中只有个别站点强度大而整体降水强度不强的可能原因。     

低频图在贵州汛期延伸期强降水预测中的应用

本文基于低频图方法对贵州省2011—2015年59次区域性强降水过程对应的500hPa低频流场进行EOF统计分析,建立贵州省强降水过程的统计预测模型,通过外推试验开展贵州2016年汛期延伸期强降水过程预测,利用回算试验的预测准确率评估该方法的本地适用性。结果表明:影响贵州强降水的6个低频关键区分别为贝加尔湖以西地区(40°~70°N,80°~110°E,1区)、贝加尔湖以东地区(40°~70°N,110°~150°E,2区)、中国西南地区东部至华中地区(25°~40°N,100°~120°E,3区)、西太平洋地区(10°~40°N,120°~140°E,4区)、孟加拉湾地区(0°~25°N,70°~100°E,5区)和中国南海地区(0°~25°N,100°~120°E,6区)。当1、4区出现低频反气旋,3、5区出现低频气旋,2、6区有配合其它关键区的低频系统活动的环流配置为贵州省强降水过程预测模型。2016年汛期强降水过程进行预测试验的预测准确率为39.2%,表明低频图方法在贵州省强降水过程预测中的应用效果较好。     

基于CloudSat资料的中国及周边地区各类云的宏观特征分析

利用2006年7月—2009年4月的CloudSat2B-CLDCLASS云分类资料,针对中国及周边地区(0°—60°N,70°—140°E)各类云量和垂直结构参数的地理分布及季节变化进行了统计分析,并根据气候特征的地域差异从该区域选出8个子区域,逐区统计了各类云的垂直结构特征。结果表明,各类云量的分布存在较明显的区域差异和季节变化;青藏高原和帕米尔高原地区卷云、高层云和高积云等中高云的高度和厚度相对较小,陆上深对流云的云底高度大于海上,而热带、副热带地区云顶高度大于中纬度地区;除积云、层积云和雨层云外,中国南方地区其他各类云的云层厚度均大于北方地区;除了层积云外,其他各类云的云顶高度在各区域都存在比较明显的季节变化,低云云底高度的季节变化和区域差异都很小,而中高云的云底高度除了在印度洋季风区、南海和西太平洋地区季节差异较小外,其他地区季节差异较明显,各个地区在任何季节内,深对流云厚度最大,层积云最小;各类云出现频率随高度的分布具有较明显的区域差异;卷云与高积云的相关性比较强,经常相伴出现,夏季更加明显,而雨层云和深对流云之间相互排斥,两者几乎不可能同时出现。此外,统计中国及周边地区各类云的水平均一性发现,中...     

多光谱卫星图像的一种模糊聚类方法

基于二维光谱特征空间，用模糊C均值(FCM)聚类方法，对多光谱静止卫星(GMS-5)图像进行了云分类试验，得到了比较合理的分类结果。该方法利用不同光谱通道的卫星云图光谱特征构造出一个二维光谱特征空问，对云图在特征空间上的光谱特征点进行FCM聚类，然后与已知云类样本的特征进行比较，确定出各聚类域的类属，进而得到二维光谱空间的云分类图，实况接收的云图可通过查验特征像素点在分类图中的落区位置来实现云的分类。     

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;