基于城市内涝仿真模型的天津风暴潮灾害评估

基于城市内涝仿真模型,根据天津沿海地区的地形、地貌特征以及排水系统等对城市内涝仿真模型进行改进,在沿海边界和河口设置时变水位,使得模型拓展到既能模拟暴雨产生的内涝,也能模拟由于风暴潮侵袭造成的淹没情景。该模型对天津沿海地区历史上典型风暴潮个例以及10年、20年、50年、100年一遇重现期风暴潮产生的积水范围和积水深度进行了模拟,并对2012年8月3日台风达维 (1210) 造成的天津沿海风暴潮进行了业务试应用。将历史风暴潮个例模拟结果以及2012年8月3日的评估结果与实际灾情进行对比,结果显示模型具有较好的模拟能力,可应用于风暴潮灾害的评估和预估业务中,为相关部门和行业提供决策参考。     

Sheltered coastal environments as archives of paleo-tsunami deposits: Observations from the 2004 Indian Ocean tsunami

The 2004 earthquake left several traces of coseismic land deformation and tsunami deposits, both on the islands along the plate boundary and distant shores of the Indian Ocean rim countries. Researchers are now exploring these sites to develop a chronology of past events. Where the coastal regions are also inundated by storm surges, there is an additional challenge to discriminate between the deposits formed by these two processes. Paleo-tsunami research relies largely on finding deposits where preservation potential is high and storm surge origin can be excluded. During the past decade of our work along the Andaman and Nicobar Islands and the east coast of India, we have observed that the 2004 tsunami deposits are best preserved in lagoons, inland streams and also on elevated terraces. Chronological evidence for older events obtained from such sites is better correlated with those from Thailand, Sri Lanka and Indonesia, reiterating their usefulness in tsunami geology studies.     

冬季北太平洋风暴轴异常及其与东亚大气环流的关系

利用NCEP/NCAR逐日再分析资料,应用滤波方差、相关分析、合成分析等研究了1963年冬季至2011年冬季北太平洋风暴轴的时空演变特征,并探讨了风暴轴活动强弱与东亚—北太平洋大气环流的关系。结果表明：与1980s后期风暴轴活动显著增强相比,近10 a来风暴轴活动又进入较气候平均水平偏低的阶段,且风暴轴主体位置有着向东北、西南两侧的振荡现象。风暴轴活动强（弱）年,东亚地区近地面温度偏高（低）、对流层低层阿留申低压和西伯利亚高压偏弱（强）、中国东部及日本上空850 hPa北风减弱（加强）;对流层中层东亚大槽减弱北缩（加深南进）、对流层高层西风急流减弱（加强）。风暴轴活动与冬季影响中国的冷空气活动次数相关关系显著。     

A high resolution forecast model of storm surge inundation

In order to forecast storm surge inundation, a two-dimensional model is established. In the model, an alternating computation sequence method is used to solve the governing equations, and the dry and wet method is introduced to treat the moving boundary. This model is easy to use. It has a friendly input interface and Arcview GIS is used as the output interface. The model is applied to the Shantou area to simulate the storm surge elevations and inundations caused by Typhoons 6903 ane 0104 using the same relevant parameters. The calculated results agree well with the observations.     

“雨燕”中风暴算法在北京奥运天气预报示范项目中的应用及改进

介绍了临近预报系统“雨燕”中的风暴系列算法, 包括风暴识别、 风暴追踪、 基于TREC技术的风暴位置预报, 以及预报位置实时评分算法等。利用2008年北京奥运期间出现的多个强对流天气, 统计出该风暴产品在30 min和60 min预报时效的绝对距离误差分别约为13 km和23 km。分析了北京奥运天气预报示范项目期间该风暴产品误差较大的原因, 主要集中于TREC技术本身及其适用范围, 以及风暴预报方案中处理细节的不足, 具体为TREC技术不适用于孤立的回波单体, 雷达探测边界对TREC技术的影响, TREC矢量有时呈现出一定的空间不连续性, 以及对孤立少动单体的不当处理等。针对上述原因, 提出了一系列的改进方案, 包括对用于风暴位置预报的TREC矢量增加一致性检验, 利用风暴的历史轨迹校正不恰当的TREC矢量; 对TREC技术中象素阵列大小进行统计分析, 选择最适合北京地区的阵列大小; 风暴预报位置超出回波范围时新的处理技巧等。利用北京奥运期间的强对流资料, 对改进后的风暴算法进行评估。结果表明, 一方面, 改进后的算法能较好地控制风暴预报位置的绝对误差, 在30 min和60 min预报时效, 绝对误差分别减小了25%和26%。另一方面, 由于预报位置精度的提高, 能够提升相邻时刻风暴匹配的效率, 使得与以前算法相比有更多的风暴样本参与了各个预报时效的评分。     

孟加拉湾风暴Mala结构及对云南强降水的影响

利用实时观测资料和NCEP(1°×1°)的6 h再分析资料,对2006年春季发生在孟加拉湾的超强风暴Mala的移动路径、强度变化、环流背景以及风暴温湿场、动力场特征等进行分析。结果表明:Mala在阿拉伯副热带高压和西太平洋副热带高压两高间辐合区生成、加强,并沿西太平洋副热带高压西侧西南或偏南气流移动。风暴发展、成熟到消亡,湿度对风暴的作用比温度明显;动力场结构除具有台风结构的一般特征外,在风暴发展期,中心附近散度场从低层到高层为辐合和辐散交替结构,表明风暴内部高空辐散抽吸作用对于风暴发展起到重要作用。登陆后风暴低压内自身能量和水汽与冷空气共同作用下,在冷暖交汇处出现强烈的上升运动和激发出中尺度辐合线是造成云南强降水主要原因。     

Babbling brook to thunderous torrent: Using sound to monitor river stage

The passive, ambient sound above the water from a river has previously untapped potential for determining flow characteristics such as stage. Measuring sub-aerial sound could provide a new, efficient way to continuously monitor river stage, without the need for in-stream infrastructure. Previous published work has suggested that there might be a relationship between sound and river stage, but the analysis has been restricted to a narrow range of flow conditions and river morphologies. We present a method to determine site suitability and the process of how to record and analyse sound. Data collected along a 500 m length of the River Washburn during July 2019 is used to determine what makes a site suitable for sound monitoring. We found that sound is controlled by roughness elements in the channel, such as a boulder or weir, which influences the sound produced. On the basis of these findings, we collect audio recordings from six sites around the northeast of England, covering a range of flow conditions and different roughness elements, since 2019. We use data from those sites collected during storms Ciara and Dennis to produce a relationship between this sound and river stage. Our analysis has shown a positive relationship between an R² of 0.73 and 0.99 in all rivers, but requires careful site selection and data processing to achieve the best results. We introduce a filter that is capable of isolating a river's sound from other environmental sound. Future work in examining the role of these roughness elements is required to understand the full extent of this technique. By demonstrating that sound can operate as a hydrometric tool, we suggest that sound monitoring could be used to provide cost-effective monitoring devices, either to detect relative change in a river or, after more research, a reliable stage measurement.     

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

Storm surges pose significant danger and havoc to the coastal residents' safety, property, and lives, particularly at offshore locations with shallow water levels. Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans. In addition to experienced predictions and numerical models, artificial intelligence (AI) techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations. Convolutional neural network (CNN) and long short-term memory (LSTM) are two of the most important models among AI techniques. However, they have been scarcely utilised for surge level (SL) forecasting, and combinations of the two models are even rarer. This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information. The architectures of the CNN, LSTM, and two sequential techniques of combining the models (LSTM–CNN and CNN–LSTM) were constructed via a trial-and-error approach and knowledge obtained from previous studies. As a case study, 11 a of hourly observed SL and wind data of the Xiuying Station, Hainan Province, China, were organised as inputs for training to verify the feasibility and superiority of the proposed models. The results show that CNN and LSTM had evident advantages over support vector regression (SVR) and multilayer perceptron (MLP), and the combined models outperformed the individual models (CNN and LSTM), mostly by 4％–6％. However, on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges, the accuracy was found to improve by over 10％ at all forecasting steps.     

台风“利奇马”对渤海风暴潮影响研究

利用实测资料对比和FVCOM数值模拟等方法,研究了"利奇马"台风风暴潮在渤海的演变规律。研究表明:渤海沿岸的风暴潮过程是由局地风"直接作用"及外部天气系统"间接影响"共同作用引起的,且它们引起的风暴潮时空分布明显不同。在"利奇马"台风风暴潮中,"直接作用"引起的风暴增水在渤海湾和莱州湾沿岸分别约占总风暴增水的2/3和1/2。     

台风风暴潮情景构建与时空推演

因台风风暴潮的突发性、情景演变时间的连续性和路径的不确定性,导致应急决策者在应急救援中难以做出正确决策,针对这一现状,将“情景—应对”应用在台风风暴潮应急决策中。本文在分析台风风暴潮情景、情景要素的概念模型基础上,首先通过资料搜集、属性识别等方法提取关键情景要素,采用框架表示法构建情景;然后分析台风风暴潮情景演变规律及演变路径;其次通过动态贝叶斯网络法构建台风风暴潮动态情景网络;最后利用先验概率与条件概率计算情景状态概率,实现了台风风暴潮的关键情景推演。本文以2018年9月16日11时至17时山竹台风对广东省沿海城市影响为例,演示了台风风暴潮的情景推演流程及关键技术。实证分析结果表明,溃堤、海水倒灌、洪水、滑坡发生的概率分别为85%、81%、74%、54%,验证了情景推演在风暴潮中应用的合理性。