Data Assimilation for the REsilient City (DARE)

数据同化是一种新兴的数学技术，通过在动态反馈环中将不确定的模式预测与不同的观测数据集相结合，来改进来自大型和复杂预报模式的预测。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相结合，并在预测洪水、雪、冰和热应力等城市自然灾害的技能方面产生阶段性变化。这项研究将使用合成孔径雷达(SAR)数据来开发一种工具，用于实时检测城市洪水泛滥的地区。合成孔径雷达传感器在大范围内从太空拍摄图像，可以看穿云层。传感器的分辨率高达1M，能够拍摄被洪水淹没的街道的图像。然而，由于建筑物造成的阴影，香港特别行政区可能看不到大部分市区地面。此外，即使干燥，阴影区也可能被错误地归类为水。我们的新方法是结合激光雷达数据使用合成孔径雷达模拟器。合成孔径雷达模拟器估计图像中由于阴影而看不到水的区域，并将这些区域从所考虑的地面掩蔽出来，从而产生更准确的洪水范围。这种类型的信息可以被急救人员用来监测重要的基础设施，了解不断演变的洪水的范围和深度。合成孔径雷达图像也可以用来提取水位观测，这些观测可以被同化成洪水淹没模型，以校准系统并保持预测的正轨。我们最近的开创性工作证明了在农村地区进行基于地球观测的洪水淹没数据同化和预报的可能性。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，以便它可以直接应用于英国的任何地方(包括城市地区)。例如，系统的行为预计会随着更大的洪水、更陡峭的河流、更快的水流等而发生变化。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头得出新类型的水位观测，也可以被吸收以提高预测技能。许多环境危害是由天气(例如，热应力、大风、雾)造成的。数值天气预报的技巧受到初始数据准确性的强烈制约，这些数据是通过同化昂贵的观测值估计的。廉价的机会数据集(公民科学、传感器网络等)的来源正在蓬勃发展。这是可以利用的，然而，缺乏关于城市地区自然变异性的知识阻碍了对这些数据的理解。这项建议通过考虑一系列尺度上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城市自然变异性造成的不确定性。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对空中交通管制的报告(用于得出风和温度的观测)。这项研究的影响将通过与洪水警报和天气预报的业务提供商(环境局和气象局)合作来保证。这项研究的某些方面将与环境分析研究所合作进行商业化，并将形成一个在“与环境变化共存”界面使用数字技术的研究人员和产业界的网络。这将包括研讨会、网络研讨会、培训和行业研究小组，以跨越学术学科之间的障碍，在学术界和行业之间建立桥梁，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.