面向灾害风险评估的热带气旋路径及强度随机模拟综述

历史热带气旋记录时间序列较短空间差异大,热带气旋灾害风险评估经常面临样本不足,特别是超强台风及巨灾记录历史样本的问题,从而导致传统概率统计方法失效。过去20多年来,逐渐发展出一套完整的方法体系进行热带气旋路径及强度随机模拟,其特点是充分利用历史总体样本信息,生成大量符合历史样本特征的热带气旋路径及强度随机事件样本集,从而有效地解决了局地历史样本不足的问题。在回顾热带气旋的年频次、季节分布、路径分布、强度及影响范围时空规律研究进展基础上,系统综述了用于热带气旋路径及随机模拟的起始点模型、行进模型、终止点模型、洋面强度模型、陆地衰减模型及结果检验方法等领域的进展及不足,然后对其在世界各地的应用进行了概述,并对未来研究改进方向及应用领域进行了展望。     

福建省强降水诱发地质灾害特征分析

福建省地质灾害调查结果表明省内地质灾害以土质类滑坡和崩塌为主,灾点多分布广规模小危害大。地质灾害点密集分布在戴云山脉周边地区。地质灾害的发生时段相对集中在5～8月,对应福建的霉雨季和台风季。群发性的地质灾害主要与单次暴雨强度或久旱后的强降雨影响关系密切。研究表明不同的地质环境背景对触发地质灾害的降雨有不同的敏感度。根据不同地质环境下降水与地质灾害发生概率之间的关系可以建立地质灾害潜势预报模型。引发地质灾害的降雨天气系统主要有热带气旋、东风波、热带云团、热带辐合带、低槽、切变等。     

全球变化背景下热带气旋主要变化特征及影响因素

热带气旋是可以影响全球中低纬度海域的气象现象。系统总结和回顾了国内外热带气旋的主要特征、潜在影响因素及影响机制的相关研究进展,并对其在全球变化背景下的变化趋势进行了总结和剖析。全球变暖以来,热带气旋的源地和路径都出现极移的趋势,移动速度略有增加,频率减小并且强度增大,但各大洋存在显著差异。重点回顾了火山活动、厄尔尼诺—南方涛动和太平洋年代际振荡、太阳辐射、热带辐合带以及气溶胶等因素对热带气旋的影响。其中,火山喷发导致平流层存在大量气溶胶,通过降低海表温度对热带气旋产生消极影响,但这种机制存在地域性差异;厄尔尼诺—南方涛动和太平洋年代际振荡以遥相关的方式调制全球热带气旋活动;太阳辐射和热带辐合带的变化与热带气旋频数存在相关性;气溶胶对不同发展阶段的热带气旋存在相反的影响机制。由于器测热带气旋数据在时间长度上和大部分替代指标在分辨率上的不足,严重制约了全球变化背景下热带气旋潜在影响因素的研究。未来可以通过寻找高分辨率记录载体来量化热带气旋活动历史,进一步解析热带气旋与潜在影响因素的关系,完善在气候波动影响下热带气旋活动的变化机制。     

北上热带气旋对东北地区暴雨洪水影响分析

热带气旋是造成东北的东南部地区夏季暴雨的主要天气系统之一。据统计，在东北的东南部地区发生的大暴雨洪水中约有５０％左右的暴雨洪水与热带气旋的影响有关。为做好旋汛水情工作，本文从实用的角度，总结了北上热带气旋的基本规律，分析了有热带气旋影响降雨的几种主要形式以及相应的典型暴雨洪水实例。     

中国沿海登陆热带气旋活动特征分析

热带气旋是危害中国最严重的天气系统,分析和认识中国沿海登陆热带气旋活动的新特征对防灾减灾具有重要意义。依据近70年气象资料,采用统计学方法,对登陆中国沿海的热带气旋特征进行分析,研究发现:在气候变化的背景下,登陆中国的热带气旋发生了明显变化。近年台风登陆频数高于往年平均,其整体强度和最大值均呈增大趋势,年台风强度的不稳定性加剧;研究还发现台风强度越高,其生成地纬度带范围越窄且越靠近赤道;建立了高强度热带气旋（STY和SUPER TY）时间和纬度的关系"φ—m"。检验了台风季长与初旋日呈负相关且不受厄尔尼诺现象影响,台风季长符合正态分布并给出概率密度公式。     

中国热带气旋暴雨洪水的分布和水文特性

描述了由热带气旋形成和影响的暴雨及其洪水的地域分布,给出了区划;讨论了热带气旋暴雨洪水的水文特性,并与非热带气旋暴雨洪水作了比较。     

非线性最优扰动方法在热带气旋目标观测研究和外场试验中的应用

综述了我国学者近年来用非线性最优扰动方法探索热带气旋目标观测及其外场试验的主要进展,具体包括：从提高数值模式初始场精度的角度,用条件非线性最优扰动方法确定了热带气旋路径和强度预报的目标观测敏感区,并成功应用于“风云四号”气象卫星和下投探空仪台风目标观测外场试验,助力业务部门获得了宝贵资料。从减小模式误差或外强迫不确定性的角度,将非线性强迫奇异向量方法应用于探讨热带气旋强度预报的敏感性,揭示了模式误差的敏感气象要素和敏感区,以及海表温度强迫的敏感区;用集合扰动的思路识别了热带气旋快速增强过程预报的行星边界层的模式误差敏感区。讨论了目前热带气旋目标观测研究存在的问题以及可能的解决方法,展望了未来热带气旋目标观测研究应努力的前沿方向,及其在实际预报中的应用前景。     

TRMM3B42降雨数据在渭河流域的应用分析

运用渭河流域24个气象站点日降雨数据对2001~2012年热带测雨卫星(TRMM)3B42数据在不同子流域、不同降雨强度以及不同时间尺度的精度进行了对比验证,并对比分析了基于TRMM和站点数据的渭河流域降雨时空分布特征。结果显示:在不同子流域的日TRMM数据比站点观测数据对低值降雨更为敏感,而在极大值降雨数据观测上两者差距较大,月尺度TRMM站点观测数据确定性系数在0.89到0.96之间;两种数据在流域降雨的时空分布上表现一致性,在年内6月中旬~10月初为湿润多雨期,其余月份降雨较少,空间分布呈东南部大,西北部小的格局。     

过去1100年来南海降雨量和热带气旋频数在增加

和目前全球变暖背景下的气候变化一样,最近几十年热带气旋数目和强度的变化可能受到自然因素和人类因素的双重影响[1],为了客观评估全球变暖在过去热带气旋变化中的地位,我们就需要知道自然条件下热带气旋的变幅.     

西北太平洋热带气旋和台风活动若干气候问题的研究

热带气旋和台风是严重的突发性自然灾害之一。近20年来,对热带气旋和台风活动的气候学研究成为台风领域的一个重要研究方向,并取得了显著的研究进展。通过对西北太平洋热带气旋和台风活动的季节、年际和年代际时间尺度变化的研究回顾,揭示了造成热带气旋和台风活动不同时间尺度变化的主要影响机制,其中包括低频振荡、季风槽和西传赤道波动、ENSO和QBO现象等,这些系统主要通过改变西北太平洋上空的环流,而影响到西北太平洋热带气旋活动以及登陆我国台风的不同时间尺度变化。还在西北太平洋海域热带气旋和台风活动的气候学研究进展和作者最新的研究成果的基础上,展望了该领域的研究前景,并提出当前此研究领域中一些亟需研究的科学问题,主要包括了季风槽区能量交换、不同海域动力过程、赤道波动演变,以及热带气旋的季节和更长时间尺度的预测。     

Interannual variability of tropical cyclone activity along the Pacific coast of North America

The interannual variability of near-coastal eastern North Pacific tropical cyclones is described using a data set of cyclone tracks constructed from U.S. and Mexican oceanic and atmospheric reports for the period 1951-2006. Near-coastal cyclone counts are enumerated monthly, allowing us to distinguish interannual variability during different phases of the May-November tropical cyclone season. In these data more tropical cyclones affect the Pacific coast in May-July, the early months of the tropical cyclone season, during La Niña years, when equatorial Pacific sea surface temperatures are anomalously cool, than during El Niño years. The difference in early season cyclone counts between La Niña and El Niño years was particularly pronounced during the mid-twentieth century epoch when cool equatorial temperatures were enhanced as described by an index of the Pacific Decadal Oscillation. Composite maps from years with high and low near-coastal cyclone counts show that the atmospheric circulation anomalies associated with cool sea surface temperatures in the eastern equatorial Pacific are consistent with preferential steering of tropical cyclones northeastward toward the west coast of Mexico.     

Stochastic simulation model for tropical cyclone tracks,with special emphasis on landfall behavior

We consider a spatial stochastic model for the simulation of tropical cyclone tracks, which has recently been introduced. Cyclone tracks are represented as labeled polygonal lines, which are described by the movement directions, translational speeds, and wind speeds of the cyclones in regular 6-h intervals. In the present paper, we compare return levels for wind speeds of historically observed cyclone tracks with those generated by the simulator, where a mismatch is shown for most of the considered coastal regions. To adjust this discrepancy, we develop a stochastic algorithm for acceptance and rejection of simulated cyclone tracks with landfall. It is based on the fact that the locations, translational speeds, and wind speeds of cyclones at landfall constitute three-dimensional Poisson point processes, which are a basic model type in stochastic geometry. Due to that, a well-known thinning property of Poisson processes can be applied. This means that to each simulated cyclone, an acceptance probability is assigned, which is higher for cyclones with suitable landfall characteristics and lower for implausible ones. More intuitively, the algorithm comprises the simulation of a more comprehensive cyclone event set than needed and the random selection of those tracks that best match historical observations at landfall. A particular advantage of our algorithm is its applicability to multiple landfalls, i.e., to cyclones that successively make landfall at two geographically distinct coastlines, which is the most relevant case in applications. It turns out that the extended simulator provides a much better accordance between landfall characteristics of historical and simulated cyclone tracks.     

Identification of clusters in tropical cyclone tracks of North Indian Ocean

Tropical cyclones are a key climate-related hazard in South Asia. Assessment of the risk of cyclone impacts requires a comprehensive characterization of historical cyclone climatology. This study analyzes the tracks of tropical cyclones in the North Indian Ocean. Based on their spatial characteristics, cyclone tracks appear to be grouped into five well-defined clusters. These clusters correspond to distinct regions of cyclonic activity and exhibit differences in characteristics such as genesis location, probability of landfall, duration, and maximum intensity. Some of the identified clusters appear particularly important with regard to impacts because events in these clusters have greater landfall probability and are more intense. The clustering approach is likely to provide useful insights for the characterization of cyclone risk.     

Experimental superensemble forecasts of tropical cyclones over the Bay of Bengal

This study entails the implementation of an experimental real time forecast capability for tropical cyclones over the Bay of Bengal basin of North Indian Ocean. This work is being built on the experience gained from a number of recent studies using the concept of superensemble developed at the Florida State University (FSU). Real time hurricane forecasts are one of the major components of superensemble modeling at FSU. The superensemble approach of training followed by real time forecasts produces the best forecasts for tracks and intensity (up to 5 days) of Atlantic hurricanes and Pacific typhoons. Improvements in track forecasts of about 25–35% compared to current operational forecast models has been noted over the Atlantic Ocean basin. The intensity forecasts for hurricanes are only marginally better than the best models. In this paper, we address tropical cyclone forecasts over the Bay of Bengal for the years 1996–2000. The main result from this study is that the position and intensity errors for tropical cyclone forecasts over the Bay of Bengal from the multimodel superensemble are generally less than those of all of the participating models during 1- to 3-day forecasts. Some of the major tropical cyclones, such as the November 1996 Andhra Pradesh cyclone and October 1999 Orissa super cyclone were well handled by this superensemble approach. A conclusion from this study is that the proposed approach may be a viable way to construct improved forecasts of Bay of Bengal tropical cyclone positions and intensity.     

Tropical cyclone hazard assessment using model-based track simulation

A method is introduced for assessing the probabilities and intensities of tropical cyclones at landfall and applied to data from the North Atlantic. First, a recently developed model for the basin-wide Monte-Carlo simulation of tropical cyclone tracks is enhanced and transferred to the North Atlantic basin. Subsequently, a large number of synthetic tracks is generated by means of an implementation of this model. This synthetic data is far more comprehensive than the available historical data, while exhibiting the same basic characteristics. It, thus, creates a more sound basis for assessing landfall probabilities than previously available, especially in areas with a low historical landfall frequency.     

Impact of modification of initial cyclonic structure on the prediction of a cyclone over the Arabian Sea

Most tropical cyclones have very few observations in their vicinity. Hence either they go undetected in standard analyses or are analyzed very poorly, with ill defined centres and locations. Such initial errors obviously have major impact on the forecast of cyclone tracks using numerical models. One way of overcoming the above difficulty is to remove the weak initial vortex and replace it with a synthetic vortex (with the correct size, intensity and location) in the initial analysis. The objective of this study is to investigate the impact of introducing NCAR–AFWA synthetic vortex scheme in the regional model MM5 on the simulation of a tropical cyclone formed over the Arabian Sea during November 2003. Two sets of numerical experiments are conducted in this study. While the first set utilizes the NCEP reanalysis as the initial and lateral boundary conditions, the second set utilizes the NCAR–AFWA synthetic vortex scheme. The results of the two sets of MM5 simulations are compared with one another as well as with the observations and the NCEP reanalysis. It is found that inclusion of the synthetic vortex has resulted in improvements in the simulation of wind asymmetries, warm temperature anomalies, stronger vertical velocity fields and consequently in the overall structure of the tropical cyclone. The time series of the minimum sea level pressure and maximum wind speed reveal that the model simulations are closer to observations when synthetic vortex was introduced in the model. The central minimum pressure reduces by 17 hPa while the maximum wind speed associated with the tropical cyclone enhances by 17 m s ⁻¹ with the introduction of the synthetic vortex. While the lowest central pressure estimated from the satellite image is 988 hPa, the corresponding value in the synthetic vortex simulated cyclone is 993 hPa. Improvements in the overall structure and initial location of the center of the system have contributed to considerable reduction in the vector track prediction errors ie. 642 km in 24 h, 788 km in 48 h and 1145 km in 72 h. Further, simulation with the synthetic vortex shows realistic spatial distribution of the precipitation associated with the tropical cyclone.     

The use and performance of mesoscale models over the Indian region for two high-impact events

High-impact mesoscale weather events, occurring in different parts of India in all seasons, lead to major weather- and climate-related disasters. Several research groups and operational weather forecasting centres in India have adopted mesoscale models for research and operational usage. This paper reviews the work done by different groups with respect to two specific events, (1) unprecedented locally heavy rainfall near Mumbai (Santa Cruz) on 26 and 27 July 2005 and (2) the Orissa super-cyclone of 29 and 30 October 1999 from its incipient stage on 24 and 25 October 1999. Considerable variability in the prediction of the intensity and location of mesoscale heavy rainfall, as well as in the intensity and path of the super-cyclone, are found. In order to reduce uncertainty in dynamical prediction, it is necessary that the model dynamics, physics, resolution, boundary conditions and availability of data on land–ocean surface processes are tuned separately to the specific event types, such as heavy monsoon rainfall, tropical cyclone genesis and movement and severe local thunderstorms, as the processes controlling such types of events may require suitable treatments for their proper simulations through appropriate dynamics, physics and resolution.     

China’s tropical cyclone disaster risk source analysis based on the gray density clustering

In recent years, tropical cyclones on the Pacific Northwest have decreased. We cannot infer that tropical cyclones impact China have reduced, because the Pacific Northwest is not homogeneous, and the variation characteristics of tropical cyclones in different sea areas are not clear. This paper uses gray relational density clustering algorithm to cluster tropical cyclone data sets between 1949 and 2008, according to the generated position of tropical cyclones, generated density and the possibility of landing. The Pacific Northwest is divided into different sea areas. Then, we analyze the risk of tropical cyclones generated in these sea areas. The results show that the probability of tropical cyclones landing generated in some sea areas is very high, reached 74 %, but the probability of tropical cyclones landing generated in other sea areas is only 2 %. Tropical cyclones generated in some sea areas are more likely to develop into typhoons, strong typhoons and so on, but the intensity of tropical cyclones generated in other sea areas is lower, there is little risk for China. Finally, according to the climate change stage trends, we divide the period 1949–2008 into three stages and analyze the tropical cyclone risk of each sea areas.     

A fast intensity simulator for tropical cyclone risk analysis

Robust estimates of tropical cyclone risk can be made using large sets of storm events synthesized from historical data or from physics-based algorithms. While storm tracks can be synthesized very rapidly from statistical algorithms or simple dynamical models (such as the beta-and-advection model), estimation of storm intensity by using full-physics models is generally too expensive to be practical. Although purely statistical intensity algorithms are fast, they may not be general enough to encompass the effects of natural or anthropogenic climate change. Here we present a fast, physically motivated intensity algorithm consisting of two coupled ordinary differential equations predicting the evolution of a wind speed and an inner core moisture variable. The algorithm includes the effects of ocean coupling and environmental wind shear but does not explicitly simulate spatial structure, which must be handled parametrically. We evaluate this algorithm by using it to simulate several historical events and by comparing a risk analysis based on it to an existing method for assessing long-term tropical cyclone risk. For simulations based on the recent climate, the two techniques perform comparably well, though the new technique does better with interannual variability in the Atlantic. Compared to the existing method, the new method produces a smaller increase in global tropical cyclone frequency in response to global warming, but a comparable increase in power dissipation.     

The occurrence laws of campus stampede accidents in China and its prevention and control measures

This research appraises how residential built environment growth influences coastal exposure and how this component of societal vulnerability contributes to tropical cyclone impact and disaster potential. Historical housing unit data and future demographic projections from a high-resolution, spatial allocation model illustrate that the area within 50 km of the US Atlantic and Gulf Coastlines has the greatest housing unit density of any physiographic region in the USA, with residential development in this region outpacing non-coastal areas. Tropical cyclone exposure for six at-risk metropolitan statistical areas (MSAs) along the US Atlantic and Gulf Coasts are assessed. All six MSAs evaluated are distinct in their development character, yet all experienced significant growth from 1940 through the contemporary period; projections from the model under various socioeconomic pathways reveal that this growth is anticipated to continue during the twenty-first century. Using a worst-case scenario framework, the historical and future residential data for the six MSAs are intersected with synthetic hurricane wind swaths generated from contemporary landfalling events. The New York City MSA contains the greatest residential built environment exposure, but Miami is the most rapidly changing MSA and has the greatest potential for hurricane disaster occurrence based on the juxtaposition of climatological risk and exposure. A disaster potential metric illustrates that all six MSAs will experience significant increases in disaster probability during the twenty-first century. This analysis facilitates a detailed spatiotemporal assessment of US coastal region vulnerability, providing decision makers with information that may be used to evaluate the potential for tropical cyclone disasters, mitigate tropical cyclone hazard impacts, and build community resilience for these and other hazards in the face of environmental and societal change.