Data Assimilation for the REsilient City (DARE)

数据同化是一种新兴的数学技术，通过将不确定的模型预测与动态反馈回路中的各种观测数据相结合，改进大型复杂预测模型的预测。该项目将使用先进的数据同化技术，将一系列先进的传感器与最先进的计算模型联合收割机结合起来，并在洪水、雪、冰和热应力等城市自然灾害的预测技能方面产生重大变化。该研究将使用合成孔径雷达（SAR）数据开发一种实时检测城市洪水地区的工具。合成孔径雷达传感器从广阔的空间区域拍摄图像，可以透过云层看到。这些传感器的分辨率高达1米，能够对被洪水淹没的街道进行成像。然而，由于建筑物造成的阴影，SAR可能看不到城市地面的大部分区域。此外，阴影区域可能被误分类为水，即使是干燥的。我们的新方法是结合激光雷达数据使用SAR模拟器。SAR模拟器估计图像中由于阴影而无法看到水的区域，并将这些区域从所考虑的地面表面屏蔽掉，从而获得更准确的洪水范围。这种类型的信息可以被第一响应者用来监测重要的基础设施，并了解不断变化的洪水的范围和深度，SAR图像也可以用来提取水位观测值，这些观测值可以被同化到洪水淹没模型中，以校准系统并保持预测的正确性。我们最近的突破性工作表明了基于地球观测的洪水淹没数据同化和预测农村地区的可能性。在这个新项目中，我们的目标是进行科学和数学研究，以增加我们的洪水数据同化系统的灵活性，使其可以直接应用于英国的任何地点（包括城市地区）。例如，系统的行为预计会因更大的洪水、更陡的河流、更快的水流等而改变。此外，我们将开发技术，从智能手机照片、交通和河流闭路电视摄像机中获得新型水位观测，这些技术也可以被吸收，以提高预测技能。热应激、大风、雾）。数值天气预报的技巧受到初始数据准确性的强烈限制，初始数据是通过同化昂贵的观测数据来估计的。廉价的机会数据集的来源正在蓬勃发展（公民科学，传感器网络等）。这是可以利用的，但由于缺乏对城市地区自然变异性的了解，妨碍了对这些数据的吸收。该提案通过考虑各种尺度的数值天气预报模型和具有不同"足迹"的观测数据，解决了观测模型比较中由于城市自然变异造成的不确定性问题。我们将把这些结果应用于公民科学自动气象站数据、汽车温度传感器和商用飞机向空中交通管制部门提交的报告（用于获得风和温度的观测结果）。这项研究的影响将通过与洪水警报和天气预报的运营提供商（环境局和气象局）合作来保证。将与环境分析研究所一起将研究的各个方面商业化，并将形成一个研究人员和工业界在"与环境变化共存"界面上使用数字技术的网络。这将有一个研讨会，网络研讨会，培训和行业研究小组的计划，以跨越学科之间的障碍，在学术界和工业界之间建立桥梁，并为初级和高级研究人员提供探索想法的空间。资助的试点项目将启动活动，并帮助确定未来的研究议程。

Data assimilation is an emerging mathematical technique for improving predictions from large and complex forecasting models, by combining uncertain model predictions with a diverse set of observational data in a dynamic feedback loop. The project will use advanced data assimilation to combine a range of advanced sensors with state-of-the-art computational models and produce a step-change in the skill of forecasts of urban natural hazards such as floods, snow, ice and heat stress. The research will use synthetic aperture radar (SAR) data to develop a tool for real-time detection of flooded urban areas. SAR sensors take images from space over a wide area and can see through clouds. The sensors have resolutions as high as 1m, and are able to image flooded streets. However, substantial areas of urban ground surface may not be visible to the SAR due to shadows caused by buildings. Furthermore, shadowed areas may be misclassified as water even if dry. Our new approach is to use a SAR simulator in conjunction with lidar data. The SAR simulator estimates regions in the image in which water will not be visible due to shadow, and masks these out from the ground surface considered, resulting in a more accurate flood extent. This type of information could be used by first responders to monitor vital infrastructure and understand the extent and depth of the evolving flood.SAR images can also be used to extract water level observations, which may be assimilated into a flood inundation model, to calibrate the system and keep predictions on track. Our recent ground-breaking work demonstrates the possibility of earth-observation-based flood inundation data assimilation and forecasting over a rural area. In this new project we aim to carry out scientific and mathematical studies to increase the flexibility of our flood data assimilation system, so that it can be straightforwardly applied at any location in the UK (including urban areas). For example, the behaviour of the system is expected to change for larger floods, steeper rivers, faster flow etc. In addition, we will develop techniques to derive new types of water level observations from smartphone photographs, traffic and river CCTV cameras, that can also be assimilated to improve predictive skill.A number of environmental hazards are caused by the weather (e.g., heat stress, high winds, fog). The skill of numerical weather prediction is strongly constrained by the accuracy of the initial data, as estimated by assimilating expensive observations. There are burgeoning sources of inexpensive datasets of opportunity (citizen science, sensor networks etc.) that could be used, however lack of knowledge about natural variability in urban areas hinders uptake of these data. This proposal addresses uncertainty due to urban natural variability in observation-model comparisons, by considering numerical weather prediction models on a range of scales, and observational data with different "footprints". We will apply these results to citizen science automatic weather station data, car temperature sensors and commercial aircraft reports made to air traffic control (used to derive observations of winds and temperature).The impact of this research will be guaranteed by working with operational providers of flood warnings and weather forecasts (the Environment Agency and Met Office). Commercialization of aspects of the research will be pursued in conjunction with the Institute for Environmental Analytics.A network of researchers and industry working with digital technology at the "Living with Environmental Change" interface will be formed. This will have a programme of workshops, webinars, training and industry study groups to cross barriers between academic disciplines, creating bridges between academia and industry and providing space for junior and senior researchers to explore ideas. Funded pilot projects will kick-start activities and help define the future research agenda.