Including spatial distribution in a data‐driven rainfall‐runoff model to improve reservoir inflow forecasting in Taiwan

Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd.     

一次江淮大暴雨过程中尺度系统结构分析

本文应用观测资料和中尺度数值模拟结果对1999年6月23日发生在江淮地区一次梅雨锋暴雨过程进行研究，揭示了影响这次暴雨过程的物理条件、云团的演变特征及与中尺度系统的关系，分析表明，在暴雨生成和发展过程中，多个中尺度云团在相继生成和移动发展，暴雨中心是几个发展较强的中尺度对流云团造成的，西南风低空急流为暴雨提供了水汽输送，并且通过强垂直运动向对流层中上层输送水汽，这次暴雨与中尺度系统发生发展有直接关系，西南涡分裂出一系列的小涡旋，这些小涡旋边向东移边减弱，并且同时在地面上引发小低压，这些中尺度低压增强低空水平辐合，成为触发不稳定能量的机制，低空急流中心与雨区相互对应，且急流风速增强，风速水平切变梯度增大的过程对应着强降水过程。     

中国西北干旱区年降雨量的时空变化

通过对中国西部地区６８个站３０年年降雨量的分析，把中国西部划分为６个降雨量性质不相关的区。通过对西北３个区的分析，把年降雨量划分为８种不同的空间分布类型。分析表明，控制中国西北干旱区的天气系统主要为西风系统。在过去的３０年间，西北干旱区的气候并非都是变得越来越干，不同的地区变化情况不同。     

中国东部夏季极端降水统计特征及其与El Nio的联系

选取中国东部1961—2012年夏季5—9月无缺测429站逐日降水资料,利用广义帕雷托分布(GPD)拟合,研究中国东部52 a以及El Nio发展年和衰减年极端降水的统计特征,并分析其成因。结果表明:1)中国东部降水阈值呈由东南向西北递减的态势,且基本为线性增加趋势。2)华南地区尺度参数最大,出现极端降水的概率大。黄河以南地区尺度参数变化趋势正值较多,出现极端降水的概率增加。3)El Nio发展年夏季,西太平洋上有气旋环流异常,中国东南部受气旋西侧的异常偏北气流影响,多地阈值偏小,只有福建东南部及黑龙江中西部易发生破纪录的极端降水。4)El Nio衰减年夏季,西太平洋上为异常反气旋环流,中国东南部受反气旋西侧的异常偏南气流影响,多地阈值偏大,广东中东部及皖鄂赣交界处发生洪涝灾害的可能性增大。     

武汉台形变观测与降雨参数之间的定量分析与研究

基于“前兆台网(站)观测数据跟踪分析平台”,对武汉台形变观测资料进行了系统分析,提取出观测曲线受降雨干扰影响的事件,采用降雨总量、初始驱动降雨量和瞬时降雨量最大值等降雨参数对降雨干扰事件进行统计分析。结果表明:降雨总量达40 mm、初始驱动降雨量为0.3 mm或瞬时降雨量最大值达0.6 mm时,DSQ型水管倾斜仪易受降雨干扰;SSY型铟瓦棒伸缩仪当降雨总量超60 mm或瞬时降雨量最大值大于0.5 mm时易受降雨干扰;VS型垂直摆倾斜仪受降雨干扰与降雨总量、初始驱动降雨量和瞬时降雨量最大值无显著相关关系;降雨总量对形变仪器观测物理量的影响基本呈现线性;而形变仪器观测物理量与初始驱动降雨量、瞬时降雨量最大值无显著相关关系。认为武汉台形变观测受降雨影响主要来自降雨渗透影响和周边水体荷载变化影响两个方面。     

澳大利亚东南部持续性降水的成因

利用热带天气图，日本ＧＭＳ卫星云图，ＥＣＭＷＦ格点风场资料，对澳大利亚东南部持续性降水的两类主要天气过程，热带云涌－冷锋尾流气旋锋生过程和阻塞反气旋北侧回流降水过程进行了分析，从云型演变，环流形热，热带流场等方面揭示了澳大利亚东南部持续性降水天气过程的基本特征、为业务预报提供参考。     

副热带高压边缘连续两次强降水形成机制分析

利用常规观测资料、自动气象站加密观测资料、GPS/MET水汽监测资料、FY-2E卫星云图和NCEP/NCAR 1°×1°再分析资料,对副热带高压边缘山东南部连续两次强降水的形成机制进行分析。结果表明,两次强降水都是由副热带高压边缘500 hPa弱西风槽过境影响产生的,副热带高压主体加强西移,850～700 hPa有较强的西南急流。强降水产生在西南低空急流的前方、暖式切变线附近;西南低空急流加强北上强降水开始,急流减弱强降水结束。强降水区与CAPE的高值区、低层水汽通量高值舌、水汽辐合中心、暖平流中心有较好的对应关系。西南低空急流、GPS/MET水汽监测对强降水的短时预报有一定的指示性。对流云团TBB最低为-78～-62 ℃,各观测站对应最大小时雨量为40～90 mm。强降水期间,850 hPa及以下有中尺度涡旋发展,涡旋尺度小,气压场上表现很弱,流场上表现明显,有明显的气旋性环流中心,在925 hPa涡旋中心东南部的暖平流中心降水强度最大。第一次强降水的中尺度涡旋源地发展,稳定少动,在其东南部上升运动强且降水强度大;第二次强降水中,冷空气在低层从西北部侵入,形成气旋,向东北移动,强降水产生在冷锋前部的暖区中,对流不稳定能量高,降水强度大、范围大。     

SIMULATION OF BOUNDARY LAYER EFFECTS ON A HEAVY RAINFALL EVENT CAUSED BY A MESOSCALE CONVECTIVE SYSTEM OVER THE YELLOW RIVER MIDSTREAM AREA

A heavy rainfall event caused by a mesoscale convective system (MCS), which occurred over the Yellow River midstream area during 7–9 July 2016, was analyzed using observational, high-resolution satellite, NCEP/NCAR reanalysis, and numerical simulation data. This heavy rainfall event was caused by one mesoscale convective complex (MCC) and five MCSs successively. The MCC rainstorm occurred when southwesterly winds strengthened into a jet. The MCS rainstorms occurred when low-level wind fields weakened, but their easterly components in the lower and boundary layers increased continuously. Numerical analysis revealed that there were obvious differences between the MCC and MCS rainstorms, including their three-dimensional airflow structure, disturbances in wind fields and vapor distributions, and characteristics of energy conversion and propagation. Formation of the MCC was related to southerly conveyed water vapor and energy to the north, with obvious water vapor exchange between the free atmosphere and the boundary layer. Continuous regeneration and development of the MCSs mainly relied on maintenance of an upward extension of a positive water vapor disturbance. The MCC rainstorm was triggered by large range of convergent ascending motion caused by a southerly jet, and easterly disturbance within the boundary layer. While a southerly fluctuation and easterly disturbance in the boundary layer were important triggers of the MCS rainstorms. Maintenance and development of the MCC and MCSs were linked to secondary circulation, resulting from convergence of Ekman non-equilibrium flow in the boundary layer. Both intensity and motion of the convergence centers in MCC and MCS cases were different. Clearly, sub-synoptic scale systems in the middle troposphere played a leading role in determining precipitation distribution during this event. Although mesoscale systems triggered by the sub-synoptic scale system induced the heavy rainfall, small-scale disturbances within the boundary layer determined its intensity and location.     

Eurasian Snow Cover Variability and Its Association with Summer Rainfall in China

This study investigates the statistical linkage between summer rainfall in China and the preceding spring Eurasian snow water equivalent (SWE), using the datasets of summer rainfall observations from 513 stations, satellite-observed snow water equivalent, and atmospheric circulation variables in the NCEP/NCAR re-analysis during the period from 1979 to 2004. The first two coupled modes are identified by using the singular value decomposition (SVD) method. The leading SVD mode of the spring SWE variability shows a coherent negative anomaly in most of Eurasia with the opposite anomaly in some small areas of the Tibetan Plateau and East Asia. The mode displays strong interannual variability, superposed on an interdecadal variation that occurred in the late 1980s, with persistent negative phases in 1979--1987 and frequent positive phases afterwards. When the leading mode is in its positive phase, it corresponds to less SWE in spring throughout most of Eurasia. Meanwhile, excessive SWE in some small areas of the Tibetan Plateau and East Asia, summer rainfall in South and Southeast China tends to be increased, whereas it would be decreased in the up-reaches of the Yellow River. In recent two decades, the decreased spring SWE in Eurasia may be one of reasons for severe droughts in North and Northeast China and much more significant rainfall events in South and Southeast China. The second SVD mode of the spring SWE variability shows opposite spatial variations in western and eastern Eurasia, while most of the Tibetan Plateau and East Asia are in phase. This mode significantly correlates with the succeeding summer rainfall in North and Northeast China, that is, less spring SWE in western Eurasia and excessive SWE in eastern Eurasia and the Tibetan Plateau tend to be associated with decreased summer rainfall in North and Northeast China.     

中国大陆流域分区TRMM降水质量评价

根据中国境内2 257个气象站点1998-2013年逐日降水资料,结合流域分区,采用探测准确性、相关系数以及相对误差等指标,对热带降水测量（TRMM）降水精度和一致性进行系统评价。结果表明:① TRMM日降水准确性从东南沿海向西北内陆递减;② 气象站点年均降水日数显著大于TRMM年均降水日数;③ 西北片区以外气象站点降水量和TRMM降水量在月尺度和年尺度上均具有较好的相关关系;④ 各流域年均TRMM面降水量均高于气象站点面降水量,且TRMM面降水量相对误差雨季较小,枯季较大;⑤ 各流域TRMM面降水量与气象站点面降水量演变趋势基本一致,南方各流域年降水量均呈减少趋势,北方各流域年降水量均呈增加趋势,全国尺度上年降水量呈微弱的减少趋势。