Newton Fund (Invitation Only) Disaster Resilient Cities: Forecasting local climate extremes and physical hazards for Kuala Lumpur

马来西亚最近发生的自然灾害，如2014/15年的大范围洪水和2007年吉隆坡的山洪暴发，表明在预测破坏性自然灾害以及管理相关风险和后果的能力方面需要改进。适合“未来城市”的主题，这个项目的重点是预测和管理与吉隆坡相关的物理风险，吉隆坡是马来西亚的首都，也是马来西亚人口最多的城市，约有800万居民。该项目特别针对的灾害是洪水、山体滑坡、地陷洞、强风、城市高温和空气污染，这在吉隆坡很常见。一个由来自英国和马来西亚的16个研究和商业伙伴组成的财团已经为这个项目成立。基本策略是适应和结合现有技术，提高灾害预测能力，主要目标是开发适合于风险沟通的多灾种信息平台原型。主要受益者将是马来西亚地方政府和保险部门的风险管理者和决策者。该项目的目标与马来西亚科学行动计划有关，该计划的目标是动员科学促进社会福祉。剑桥大学（UoC）是该项目的英国主要合作伙伴，其主要作用是在气象预报包中。由于UoC首席研究员在城市灾害建模方面具有特殊的专业知识，并在组建财团方面发挥了主导作用，因此UoC也将在整个项目的整体管理中发挥突出作用。在气象工作计划范围内，UoC的主要任务将是评估马来西亚的长期预报能力，并开发具有灾害风险展望具体目标的数据处理模型。这是及时的，因为由主要天气和气候中心开发的全球长期预报系统现在在季节时间尺度上具有可证明的预测技能，并且具有足够的空间分辨率来代表与马来西亚部门相关的重要热带气象过程。UoC还将为马来西亚气象局领导的中尺度天气模拟工作包做出贡献。该项目的好处将包括：改进有关自然灾害发生风险的信息，使马来西亚地方当局能够制定更好的应急计划，以减轻此类灾害的影响。这将反过来为马来西亚公民提供经济效益和提高生活质量。地球物理灾害信息的改进也将有助于保险服务的发展。为吉隆坡开发的灾害信息系统将与马来西亚其他城市和东盟区域有关。有扩大这种系统的商业发展的余地，这对商业部门和未来城市的城市管理都有好处。关于UoC远程气象组成部分，技能调查的结果将通过所使用的方法和作为亚洲区域有益应用潜力的一个例子，与更广泛的季节预测研究界有关。

Recent natural disasters in Malaysia, such as the wide-spread floods in 2014/15 and the flash flooding of Kuala Lumpur in 2007, have revealed that improvements are required in the prediction of damaging natural hazards and in the capacity to manage the associated risks and consequences. Appropriate to the theme of 'future cities', the focus of this project is the prediction and management of physical risks relevant to Kuala Lumpur, which is the Malaysian capital and the most populated city in Malaysia with around 8 million inhabitants. The particular hazards to be targetted in this project, that are common in Kuala Lumpur, are floods, landslides, sink holes, strong winds, urban heat and air pollution.A consortium of 16 research and business partners from the UK and Malaysia has been assembled for this project. The basic strategy is to adapt and combine existing technologies to enhance hazard forecasting ability, and the main objective is to develop a prototype multi-hazard information platform suitable for communicating risks. The primary beneficiaries will be risk managers and decision-makers in Malaysian local government and the insurance sector.The project objectives relate to the Malaysian Science to Action initiative, which has an aim of mobilising science for societal well-being. The University of Cambridge (UoC) is the lead UK partner in the project, and its main role is in the meteorological forecasting package. As the UoC Principal Invesigator has particular expertise in urban hazard modelling and has had a leading role in assembling the consortium, UoC will also have a prominent role in the overall management of the whole project.Within the meteorological workplans, the main task of UoC will be to assess long-range forecast capability for Malaysia and develop data-processing models with specific objectives for hazard risk outlooks. This is timely because the global long-range forecast systems developed by leading weather and climate centres now have demonstrable predictive skill on seasonal timescales, and have spatial resolution sufficient to represent important tropical meteorological processes relevant to the Malaysian sector. UoC will also contribute to the mesoscale weather modelling workpackage led by the Malaysian Meteorological Department.Benefits of the project will include:Improved information regarding the risks of occurrence of natural hazards will enable Malaysian local authorities to make better contingency plans to mitigate the effects of such hazards. This will in turn provide economic benefits and improve the quality of life for Malaysian citizens. Improved information about geophysical hazards will also aid the development of insurance services.The hazard information system developed for Kuala Lumpur will have relevance to cities elsewhere in Malaysia and in the ASEAN region. There is scope for broadening commercial development of such systems, with benefit both to the commercial sector and to the future urban management of cities.With regard to the UoC long-range meteorology component, the results of the skill investigation will be relevant to the wider seasonal prediction research community, both through the methodology used and as an example of the potential for beneficial application in the Asian region.