Main Detrainment Height of Deep Convection Systems over the Tibetan Plateau and Its Southern Slope

Deep convection systems (DCSs) can rapidly lift water vapor and other pollutants from the lower troposphere to the upper troposphere and lower stratosphere. The main detrainment height determines the level to which the air parcel is lifted. We analyzed the main detrainment height over the Tibetan Plateau and its southern slope based on the CloudSat Cloud Profiling Radar 2B_GEOPROF dataset and the Aura Microwave Limb Sounder Level 2 cloud ice product onboard the A-train constellation of Earth-observing satellites. It was found that the DCSs over the Tibetan Plateau and its southern slope have a higher main detrainment height (about 10?16 km) than other regions in the same latitude. The mean main detrainment heights are 12.9 and 13.3 km over the Tibetan Plateau and its southern slope, respectively. The cloud ice water path decreases by 16.8% after excluding the influences of DCSs, and the height with the maximum increase in cloud ice water content is located at 178 hPa (about 13 km). The main detrainment height and outflow horizontal range are higher and larger over the central and eastern Tibetan Plateau, the west of the southern slope, and the southeastern edge of the Tibetan Plateau than that over the northwestern Tibetan Plateau. The main detrainment height and outflow horizontal range are lower and broader at nighttime than during daytime.     

GRAPES全球奇异向量方法改进及试验分析

基于总能量模的奇异向量扰动常用于构造集合预报的初始条件。以建立GRAPES（Global and Regional Assimilation PrEdiction System）全球集合预报系统为目的,基于前期研发的GRAPES全球模式奇异向量方法,在GRAPES全球切线性模式和伴随模式2.0版的框架下,开展了引入线性化边界层方案来改善奇异向量结构,并提高奇异向量计算效率的研究。通过连续试验,从奇异向量的扰动能量结构、扰动能量谱及扰动空间分布等方面,综合分析改进GRAPES全球奇异向量的结构及演变特征。试验结果表明,改进后的GRAPES奇异向量方法有效抑制了之前扰动能量在近地面层不合理的快速增长,同时,奇异向量最优扰动的结构更客观地体现了中高纬度区域大气初始条件中的斜压不稳定扰动及其演变,如在初始时刻奇异向量扰动能量主要位于对流层中层,并呈现出随高度向西倾斜的大气斜压特征;经过线性化演变,扰动能量向较大水平尺度转移,并在垂直结构上表现出向对流层高层上传及向对流层低层下传的特征等。针对GRAPES奇异向量迭代求解中伴随模式计算耗时为主的情况,改进伴随模式中广义共轭余差方案的调用方式,并采用大内存存储法来提高其计算效率,进而将奇异向量总计算时间缩短了25%。总之,改进后的GRAPES奇异向量方法,可应用于构建面向业务应用的GRAPES全球集合预报系统。      

Integrating farmers’ adaptive knowledge into flood management and adaptation policies in the Vietnamese Mekong Delta: A social learning perspective

Flood management and adaptation are important elements in sustaining farming production in the Vietnamese Mekong Delta (VMD). While over the past decades hydraulic development introduced by the central government has substantially benefited the rural economy, it has simultaneously caused multiple barriers to rural adaptation. We investigate the relational practices (i.e., learning interactions) taking place within and across the flood management and adaptation boundaries from the perspective of social learning. We explore whether and how adaptive knowledge (i.e., experimental and experiential knowledge) derived from farmers’ everyday adaptation practices contributes to local flood management and adaptation policies in the selected areas. We collected data through nine focus groups with farmers and thirty-three interviews with government officials, environmental scientists, and farmers. Qualitative analysis suggests that such processes are largely shaped by the institutional context where the boundary is embedded. This study found that while the highly bureaucratic operation of flood management creates constraints for feedback, the more informal arrangements set in place at the local level provide flexible platforms conducive to open communication, collaborative learning, and exchange of knowledge among the different actors. This study highlights the pivotal role of shadow systems that provide space for establishing and maintaining informal interactions and relationships between social actors (e.g., interactions between farmers and extension officials) in stimulating and influencing, from the bottom-up, the emergence of adaptive knowledge about flood management and adaptation in a local context.     

边界层参数化对海南岛海风环流结构模拟的影响

利用WRF V3.7详细分析了应用8种边界层参数化方案（YSU、MYNN2.5、MYNN3、ACM2、BouLac、UW、SH、GBM）所模拟的2014年5月25日海南岛海风环流结构的差异,其中YSU、ACM2和SH为非局地闭合方案,MYNN2.5、MYNN3、BouLac、UW和GBM为局地闭合方案。结果表明:对于海风环流水平结构的模拟,15时,YSU、ACM2、BouLac、UW和SH模拟的北部海风较强,SH和GBM的内陆风速偏大。温度与海风发展强度相对应,MYNN2.5与MYNN3模拟的岛屿温度偏低,海陆温差小,海风相对较弱。对于海风环流垂直结构的模拟,09时海风开始,但强度较小,且存在残余陆风,向内陆传播距离较短,YSU、MYNN2.5和SH方案的海风相对较强。12时,海风已呈现出较为清晰的环流结构,YSU和ACM2的海风厚度及向内陆传播距离相对强于其它方案,MYNN3的环流结构则不太明显,且向内陆推进距离短,海风相对较弱。15时,海风发展强盛,MYNN2.5和MYNN3方案模拟的海风垂直强度较小,ACM2方案的海风垂直环流特征最为明显。18时,海风的强度和扰动均有所减弱,ACM2、BouLac和UW的整体海风相对强于其它方案。21时海风已基本转为陆风,BouLac与UW的陆风环流结构最为清晰。位温、水汽及海风垂直环流强度的发展变化与海风的演变过程基本一致。造成ACM2模拟海风偏强的原因是其边界层垂直混合偏强,形成了足够的湍流混合强度所致。对于边界层高度的模拟,ACM2的边界层顶最高,这与此方案所模拟的海风强度偏大相吻合,其它方案的边界层高度与海风强度并不完全一致。      

MODELLING UNKNOWN STRUCTURAL SYSTEMS THROUGH THE USE OF NEURAL NETWORKS

This paper explores the potential of using neural networks to identify the internal forces of typical systems encountered in the field of earthquake engineering and structural dynamics. After formulating the identification task as a neural network learning procedure, the method is applied to a representative chain-like system under deterministic and stochastic excitations. The neural network based identification method provides very good results for general classes of multi-degree-of-freedom structural systems. The range of validity of the approach is demonstrated, and some application issues are discussed for (a) partially known multi-degree-of-freedom systems and (b) completely unknown systems.     

IN SITU MEASUREMENTS OF BASAL WATER QUALITY AND PRESSURE AS AN INDICATOR OF THE CHARACTER OF SUBGLACIAL DRAINAGE SYSTEMS

Continuous subglacial measurements of turbidity and electrical conductivity — two indicators of basal water quality — can be used to help characterize subglacial drainage systems. These indicators of water quality yield information that complements that provided by water pressure measurements. Quantitative attributes of subglacial drainage systems, such as water velocity and subglacial residence time, as well as qualitative behaviour — for example, spatial and temporal variations in system morphology — can be deduced using water quality measurements. Interpretation is complicated by the many potential influences on turbidity and electrical conductivity, but when these complications are appreciated a richer interpretation results. To demonstrate the utility of basal water quality measurements, observations from Trapridge Glacier, Yukon Territory, Canada were examined. The data reveal complex behaviour of the drainage system, but constraints imposed by basal water quality measurements help to clarify the nature of the subglacial flow system. The measurement and interpretation methods described and demonstrated are applicable to other glaciers. As such, they should prove useful for characterizing different subglacial drainage configurations and behaviours, thereby improving our general understanding of the hydrology and dynamics of wet-based glaciers.     

基于多智能体的土地利用模拟与规划模型

利用多智能体和元胞自动机对城市土地资源的可持续利用进行了规划。根据环境经济学资源分配原理和可持续发展理论,提出结合多智能体及元胞自动机的微观规划模型,在时间和空间上合理分配及规划城市土地资源的利用,以避免浪费不可再生的土地资源。该模型由相互作用的多智能体层、元胞自动机层和环境因素层组成,可方便地探索不同土地利用政策下城市土地利用发展情景,能够为城市规划提供有用的决策依据。以广州市海珠区为实验区,在可持续发展为前提的规划下,模拟了1995-2010年的城市扩展的动态变化,并讨论了在不同规划情景下城市土地资源的利用效率及合理性。     

Landsat5图像的增益、偏置取值及其对行星反射率计算分析

在对比不同时相的数据或建立遥感反演模型时,往往需要将图像的灰度值转换为反射率。增益和偏置是计算反射率时使用的两个基本参数。如果这两个参数发生偏差,反射率的计算结果就有问题,大气校正和其他相关工作的结果也是不可靠的。根据USGS文档和1995年以来的Landsat5遥感图像,对图像头文件中的增益和偏置参数的取值进行了分析,并按照标准单位将增益和偏置进行了转换。为了分析不同增益和偏置取值对反射率计算结果的影响,对比计算了植被和水体的行星反射率值。对2004年7月26日的太湖地区的TM图像。取值分为4种方案: (1)使用遥感软件ENV14．0计算;(2)使用图像头文件中的参数;(3)使用USGS提供的同期参数;(4)使用USGS提供的2003年前的参数。结果表明,不同增益和偏置取值导致计算结果变化较大。如果以方案3的USGS参数计算的结果为标准,那么,其他方案的相对误差值可从0．2％到20％以上。从各个波段的误差分布看,以第3波段的相对误差最小。从工作成果的可比性角度出发,建议统一使用USGS的增益和偏置参数计算Landsat5图像的反射率。     

基于耦合场分割算法的ANSYS二次开发及其应用

耦合场分割算法是当前研究的热点之一。本文首先对比研究了瞬态热传导方程和孔压增长消散方程,提出了一种适用于ANSYS二次开发的分割算法,并对2种不同的预测值计算模式对计算稳定性的影响进行了分析。然后,据之对ANSYS温度—结构耦合场模块进行了开发,形成了岩土工程有效应力计算模块。数值实验表明,这种方法具有较高的计算精度和良好的计算稳定性,并且能够充分利用现有程序资源,具有较高的工程应用价值。     

利用奇异谱分析提高极移中长期预报精度

由于空间大地观测数据传输耗时及处理过程复杂, 导致极移测量值的获取存在时延, 无法满足对高精度的极移预报值有重大需求的应用领域. 针对极移复杂的时变特性, 提出一种基于奇异谱分析(singular spectrum analysis, SSA)的预报方法. 首先用SSA分离提取极移时序中的高频组分与低频组分; 其次建立最小二乘(least square, LS)外推与自回归(AutoreGressive, AR)模型对极移高频和低频组分进行组合预报. 结果表明, SSA方法能够准确地分离和提取极移低频和高频组分, 对低频和高频组分组合预报可以显著改善极移的中长期(30--365d)预报精度, 与国际地球自转和参考系服务局(International Earth Rotation and Reference Systems Service, IERS)提供的A公报中的极移预报值相比, SSA方法对极移PMX分量(本初子午线方向)和PMY分量(西90$^\circ$子午线方向)的中长期预报精度改进最高分别可达45.97%和62.44%. 研究结果验证了SSA方法对极移中长期预报改进的有效性.