TRMM海表降水率同化在热带气旋Danas(2001) 数值模拟中的应用研究

研究了TRMM/TMI海表降水率资料的四维变分同化在热带气旋(TC)数值模拟中的作用.使用中尺度气象模式MM5设计了若干数值试验模拟了TC Danas(2001)由热带低压初生到台风生成的发展过程.在满足MM5模式动力约束的前提下,将TRMM海表降水率资料直接同化进入较高分辨率(18 km)的模式初始场.结果表明,使用MM5模式的4D-VAR同化系统直接同化TRMM/TMI海表降水率资料是可行的.这种做法提高了TRMM资料的利用率,不仅在模式初始场中加入了更多实测信息,而且避免了两次同化(1DVAR+4DVAR)可能引起的误差.直接同化TRMM资料通过调整气压、温度、湿度等要素初始场,改善了模式对热带气旋结构(如暖心、涡度、散度)的描述和降水的模拟.在此基础上,同化不仅改进了对Danas强度的模拟,而且成功地模拟了热带气旋环境场的演变过程,因而改进了路径的模拟.     

华北地区夏季降雨量与南海海温长期变化的关系

比较了华北地区7个站与17个站1951-1997年夏季（6，7，8月）降雨量与气候随时间的变化特征，并对其成因作了探讨。结果表明，用北京、天津、邢台、烟台、郑州、太原和济南等7个站可代表该地区夏季降雨量与气候的多尺度变化特征，过去47a该地区依次经历了湿凉、湿热、湿凉、干热、湿热几个时期，降雨量的长期变化与南海前冬（1-2月）海温成负相关。前冬南海海温偏高，意味着初夏南海地区大气对流低频振动偏弱，南海夏季风爆发较晚，西南季风较弱，夏季西太平洋副高位置偏南，华北地区大气低层北风加强，华北地区夏季少雨，前冬南海海温偏低时情况则相反，考虑冬季（1-2月）南海南温和7-8月西太平洋副高脊线位置（纬度）的影响用均生函数建模，试验结果与用子波变换重构方法考虑华北地区夏季降雨量的变化趋势比较，二者相吻合，预测试验结果与过去3a的实况基本一致。     

Conversion of kinetic energy from synoptic scale disturbance to low-frequency fluctuation over the Yangtze River valley in the summers of 1997 and 1999

In order to investigate the conversion of kinetic energy from a synoptic scale disturbance （SSD; period≤seven days） to a low-frequency fluctuation （LFF; period〉seven days）, the budget equation of the LFF kinetic energy is derived. The energy conversion is then calculated and analyzed for the summers of 1997 and 1999. The results show that the energy conversion from the SSD to the LFF is obviously enhanced in the middle and lower troposphere during the heavy rainfall, suggesting this to be one of mechanisms inducing the heavy rainfall, although the local LFF kinetic energy may not be enhanced.     

Impact of 4DVAR assimilation of rainfall data on the simulation of mesoscale precipitation systems in a mei-yu heavy rainfall event

The multi-scale weather systems associated with a mei-yu front and the corresponding heavy precipitation during a particular heavy rainfall event that occurred on 4 5 July 2003 in east China were successfully simulated through rainfall assimilation using the PSU/NCAR non-hydrostatic, mesoscale, numerical model （MM5） and its four-dimensional, variational, data assimilation （4DVAR） system. For this case, the improvement of the process via the 4DVAR rainfall assimilation into the simulation of mesoscale precipitation systems is investigated. With the rainfall assimilation, the convection is triggered at the right location and time, and the evolution and spatial distribution of the mesoscale convective systems （MCSs） are also more correctly simulated. Through the interactions between MCSs and the weather systems at different scales, including the low-level jet and mei-yu front, the simulation of the entire mei-yu weather system is significantly improved, both during the data assimilation window and the subsequent 12-h period. The results suggest that the rainfall assimilation first provides positive impact at the convective scale and the influences are then propagated upscale to the meso- and sub-synoptic scales.
Through a set of sensitive experiments designed to evaluate the impact of different initial variables on the simulation of mei-yu heavy rainfall, it was found that the moisture field and meridional wind had the strongest effect during the convection initialization stage, however, after the convection was fully triggered, all of the variables at the initial condition seemed to have comparable importance.     

北京精细下垫面信息引入对暴雨模拟的影响

首先根据2000年环北京实际的精细下垫面布局资料（500m分辨率）,按美国USGS陆面资料分类标准（25类）对其提供的全球30 s经纬分辨率（≈1 km）下垫面分类资料进行了更新设计.进而针对一个北京夏季暴雨过程,利用10:3.3km双向双重嵌套的MM5V3.6-Noah LSM陆气耦合模式进行24h数值对比试验,研究了北京精细下垫面信息引入对暴雨的影响.分析表明：新设计的陆面资料更真实地反映了环北京区域的下垫面结构,尤其针对北京城区面积迅增特征;同时还修正了原资料将亚洲中纬度区域落叶阔叶林下垫面类型归属为热带(或亚热带)稀疏大草原类型的问题.其在数值天气模式中的引入会对短期暴雨过程的发生发展产生重要影响.对此次暴雨主要降水中心的模拟,12h差值分布范围远达30km以上,中心值相对差异可达30%.研究发现在城市下垫面和大气相互间存在一个重要的相互影响机制,即由于城区面积的扩大会导致自然植被减少,进而会减少地表蒸发及相应局地大气水分供应、加深边界层高度并增强大气水汽混合,这不利于降水的发生发展.     

青藏高原东部暴雨的中尺度有效位能收支分析

针对2018年7月10-11日青藏高原东部一次暴雨过程,利用模式模拟资料分析了有效位能分布特征,成因及其对降水发展演变的影响.结果表明,有效位能主要分布在对流层低层4km以下和高层8-14km,高层有效位能和降水有更好的对应性西北冷平流和降水粒子下落的蒸发作用是低层有效位能高值中心的主要成因,而降水过程释放潜热带来的热...     

Role of sea surface temperature and rainfall in determining the stock and fishery of the common octopus (Octopus vulgaris,Mollusca, Cephalopoda) in Tunisia

Octopus (Octopus vulgaris, Mollusca, Cephalopoda) is an important and valuable fishery resource on the eastern and southern coasts of Tunisia, but its landings are highly variable. This paper explores the effect of environment on octopus catch per unit effort (CPUE) during a 12‐year period, through correlation analyses and the incorporation into surplus production models of sea surface temperature (SST) and rainfall data collected during cold (January–May) and hot (August–October) seasons. CLIMPROD software was used to select the appropriate model and fit it to the fishery and environment data. In both seasons, SST significantly contributed to CPUE variability; fishery production was influenced positively by cold season SST but negatively by hot season SST. Due to a poor fit with cold season data, the impact of rainfall was analysed only for the hot season, during which it has a positive effect on production. Results are discussed in view of the life‐cycle of octopus and the dynamics of the Tunisian fishery. This first study of octopus variability in Tunisia highlights the necessity to incorporate environmental influence into stock assessment and management advice.     

基于DTW与K-means算法的河北场雨及雨型分区特征研究

深入挖掘气象站点的观测降雨数据,研究区域降雨的雨型规律,对于洪涝灾害预警和减灾措施制订有重要意义.本文基于河北省2005-2017年3189个站点逐小时降雨观测数据,进行"场雨"的划定,进而提取历史上各场雨的累积雨量、时长指标.采用数据挖掘技术中的DTW相似性算法进行场雨雨型的自动归类,将场雨分成Ⅰ-Ⅶ共7种雨型,包括...     

Modelling bivariate rainfall distribution and generating bivariate correlated rainfall data in neighbouring meteorological subdivisions using copula

The rainfall patterns of neighbouring meteorological subdivisions of India are similar because of similar climatological and geographical characteristics. Analysing the rainfall pattern separately for these meteorological subdivisions may not always capture the correlation and tail dependence. Furthermore, generating the multivariate rainfall data separately may not preserve the correlation. In this study, copula method is used to derive the bivariate distribution of monsoon rainfall in neighbouring meteorological subdivisions. Different Archimedean copulas are used for this purpose and the best copula is selected based on nonparametric test and tail dependence coefficient. The fitted copula is then applied to derive the bivariate distribution, joint return period and conditional distribution. Bivariate rainfall data is generated with the fitted copula and it is observed with the increase of sample size, the generated data is able to capture the correlation as well as tail dependence. The methodology is demonstrated with the case study of two neighbouring meteorological subdivisions of North‐East India: Assam and Meghalaya meteorological subdivision and Nagaland, Manipur, Mizoram and Tripura meteorological subdivision. Copyright © 2010 John Wiley & Sons, Ltd.