Reconstruction of the starting time series of rainy season in Yunnan and the evolvement of summer monsoon during 1711–1982

According to the textual research into the historical documents dominated by ar-chives yearly, as well as the verification with several other kinds of data, the later or earlier starting time of the rainy seasons in Yunnan during 1711–1982 has been reconstructed. The analysis indicates that there are obvious fluctuations in the starting date of the rainy seasons in Yunnan in a year or years, and long fluctuation on the decadal scale. The rainy season comes earlier in the early 18th century, later in the 19th century and earlier again in the 20th century. This reflects to a certain degree the gradual change of the summer monsoon in Yunnan. There exists an obvious quasi-3 years cycle, which is related to El-Nino’s quasi-3 years cycle, and a 11.3-year cycle which is notably related to the 11-year cycle of the solar activity of starting date of the rainy seasons in Yunnan. Meanwhile, the dissertation finds that the El-Nino is very important to the starting date of the rainy seasons in Yunnan. The starting date of the rainy seasons in Yunnan often comes later or normally in the year of El-Nino. However, there is an obvious imperfect period in such influence, which in turn may mean that there is a certain fluctuation in the effect of ENSO on Asian summer monsoon.     

长江中下游地区汛期极端降水量的异常特征分析

利用长江中下游地区（湖北、湖南、江苏、安徽、江西、浙江和上海）1960—2004年87个台站汛期（5--9月）逐日降水资料，统计不同台站近45年逐年汛期极端降水量，运用E（）F、鼢、趋势分析及Morlet小波分析等方法进行了时空分布特征分析。结果表明：长江中下游地区汛期极端降水量的平均空间差异较大；一致性异常特征是长江中下游地区汛期极端降水量的最主要的空间模态，而南、北反向变化模态也是比较重要的；长江中下游地区汛期极端降水量可分为两湖平原型、北方型、沿海型及南方型4个主要的空间分型；各主要空间分区极端降水量的年际变化特征差异较大，但从长期变化趋势来看，除了东北部极少部分区域外，其余区域汛期极端降水量均表现为增加趋势；另外近45年来长江中下游地区汛期极端降水量各分区的周期振荡不完全一致。     

滁州市汛期雨量多尺度时间变化特征及其GA-BP模型预测

利用集合经验模态分解EEMD方法分析了滁州地区汛期雨量的多尺度时间变化特征,以国家气候中心的百余项环流指数作为预报因子并利用主成分分析进行预测因子优化,在此基础上建立基于GA-BP神经网络的汛期雨量气候预测模型,研究结果表明:该地区1961—2017年汛期雨量存在准2 a、4 a的年际尺度及准11 a、16 a的年代际尺度周期变化特征,总体趋势在20世纪80年代之前、20世纪80年代—21世纪及21世纪以来3个时段分别对应增多—减少—增多的趋势;基于GA-BP神经网络的两种方案对滁州地区7个站的预测误差分别为122 mm和144 mm,其中利用主成分分析进行因子优化的方案预测效果更好。     

MOISTURE TRANSPORT PATHS AND SOURCES OF SOUTH CHINA ANNUALLY FIRST RAINY SEASON AND THEIR RELATIONSHIP WITH THE ONSET OF THE SOUTH CHINA SEA SUMMER MONSOON

This study simulated the moisture transport process of southern China annually first rainy season (SCAFRS) using a Lagrangian airflow trajectory model (Hybrid Single Particle Lagrangian Integrated Trajectory: HYSPLIT), to determine SCAFRS moisture transport characteristics and their relationship with South China Sea summer monsoon (SCSSM). It is found that the moisture transport paths and sources of SCAFRS are closely related to the onset of SCSSM. Divided by SCSSM onset dates, the moisture transport characteristics of SCAFRS are compared quantitatively. Before the onset of SCSSM, precipitation of SCAFRS mainly comes from western Pacific and eastern China. Their contributions are 24% and 25%, respectively. The amount of water vapor carried along the path coming from Bay of Bengal-South China Sea (BSC) is relatively high, but the contribution rate of this path to SCAFRS precipitation is relatively low. Mainly due to strong precipitation over Bay of Bengal before the onset of SCSSM, this region is a moisture sink, which makes most moisture deposit in this region and only a small portion of water vapor transported to southern China. After the onset of SCSSM, most water vapor is transported to southern China by the southwesterly paths. The Indian Ocean is the main moisture source, which contributes almost 25% to SCAFRS precipitation. The contributions of moisture originating from BSC and eastern China to southern China precipitation after the onset of SCSSM are 21% and 18%, respectively.     

青岛汛期降水量与太平洋年代际振荡的关系

基于太平洋海面温度（SST）、大气环流及青岛降水量资料,分析并发现了青岛汛期（6—9月）降水量与太平洋年代际振荡（PDO）指数存在重要联系。当PDO处于冷位相时,西北太平洋区SST偏高,北美沿岸以及热带中东太平洋区SST偏低,西太平洋副热带高压偏弱偏东,脊线偏北,东亚夏季风偏强,青岛汛期降水量偏多,反之偏少。定义了一个新的太平洋SST距平指数SSTI,该指数包含了西北太平洋与热带中东太平洋SST距平反相变化的协同影响,也包含了PDO与ENSO的协同影响。与PDO指数、西北太平洋及热带中东太平洋SST相比,该指数与青岛汛期降水量相关性更好,通常SSTI正指数对应着汛期西太平洋副热带高压脊线偏北,面积偏小、强度偏弱,东亚夏季风偏强,有利于青岛汛期降水量偏多,反之偏少。SSTI指数可作为青岛汛期降水量预测的指示因子。     

广东前汛期一次锋前暖区暴雨触发机制分析

利用ECMWF的ERAdata再分析资料、Micaps实况等数据,对2015年5月22—24日的广东大范围强降雨过程进行分析,结果表明:该次过程是一次典型的华南前汛期降雨过程,过程中存在明显的两种性质降雨,即粤西北由南支短波槽、低涡、切变线造成的锋面型暴雨,而珠江口及其东侧的暖区暴雨主要是由西南低空急流脉冲引起的;珠江口及其东侧的暖区暴雨的触发机制主要为中尺度能量锋、地面辐合线以及地形等。     

牟汶河下游汛期分期及洪水分级的确定

为实现对牟汶河下游蓄水建筑物进行汛期动态控制,依据流域范家镇站、北望站和大汶口站3个雨量站1980~2011年日降雨资料,采用模糊分析方法,对牟汶河下游汛期进行了分期;以北望站同时期的历年最大洪峰流量资料,利用重现期法确定了洪水分级流量。     

TerraSAR-X卫星影像中大比例尺测图应用研究

为了研究TerraSAR-X卫星的中大比例尺测图可行性,文章利用成都都江堰市3m、1m分辨率的TerraSAR-X影像、控制点及可见光卫星影像,对TerraSAR-X影像在多云多雨地区的中、大比例尺测图展开了研究和分析。实验表明利用TerraSAR-X影像能够符合中大比例尺基础地图测图或修测的精度要求,可用于困难地区1∶50 000~1∶10 000比例尺的基础测图生产和更新。     

2014年汛期气候预测先兆信号应用及其复杂性初探

文章系统回顾了2014年汛期气候预测的主要先兆信号。其信号特征是：2014年前期赤道中东太平洋由呈正常略偏冷向偏暖状态发展,预计可能在夏季形成一次暖事件,冬季格陵兰海冰偏多、青藏高原积雪正常略偏多,这些特征对后期东亚夏季风有明显影响。通过对前期先兆信号的分析,预测了2014年我国夏季雨带位置偏北,但较2013年偏南,主要多雨区位于华北南部至江淮。从实况和预报的对比来看,夏季主雨带预测与实况有较大差异,没有预报出“北少南多”的降水分布型,但东北、华北北部、长江中游、华南西部和西南南部等地预测与实况一致。另外,热带气旋、梅雨和华北雨季的预测与实况基本一致。最后对汛期气候预测的复杂性及存在的不足进行了分析和讨论。     

Radiative characteristics in a Japanese forested drainage basin during snowmelt

Radiative characteristics in a forested drainage basin during the snowmelt season were examined in order to better understand and predict snowmelt runoff in the basin. A method for estimating net radiation in a forest (R_nf) was presented using the total sky view factor (P) and the sun path sky view factor (Q). Solar radiation, albedo, atmospheric radiation and air temperature observed at an open site were also required. The total and the sun path sky view factors were determined from all‐sky photographs. Q was expressed as a linear function of P for 0·15<P<0·86 regardless of forest type. For P<0·15, Q was set to zero, and for P>0·86, Q was equal to unity. The short‐wave radiation budget at the forest floor (S_nf) increased with P, whereas the long‐wave radiation budget (L_nf) decreased with P. R_nf increased with P for 0·15<P<0·86, and changed little with P for P<0·15 and P>0·86, as the increase in S_nf was offset by the decrease in L_nf . The forest effect on R_nf was diminished under cloudy or high albedo conditions, because S_nf was easily offset by L_nf . This estimation method was extended to the whole basin, and R_nf was obtained over a watershed covered by trees. At the beginning of the snowmelt season when the albedo remained high, the forest effect became null because the decrease in S_nf was balanced by the increase in L_nf . As the albedo gradually lowered with the advance of the snowmelt season, the decrease in S_nf owing to forest covers exceeded the increase in L_nf , and the forest effect to decrease R_nf became evident. Copyright © 1999 John Wiley & Sons, Ltd.