Enhancing forecasting flood inundation mapping through data assimilation

及时的洪水淹没预报有助于积极主动的洪水管理，减少生命损失和基础设施损坏。该项目旨在研究利用洪水观测来核实和改进洪水淹没预测的新方法。国家/跨国尺度的洪水淹没业务预报的最新技术使用模拟库，其中来自数值天气预报（NWP）或观测的降雨数据驱动一系列过程模型和概率分析，最终的洪水预报是从预先计算的洪水图库中产生的。这种方法节省了计算时间，允许对大面积区域进行近实时更新，否则会带来重大挑战。然而，它对数据同化提出了挑战，在数据同化中，观测结果与模型预报在动态反馈回路中相结合，以保持预报正确。尽管如此，数据同化框架仍然提供了一致的技术，异质观测与模式数据的比较，预报验证和校准。在使用模拟库方法进行洪水淹没预测时，变化的长度尺度是什么？需要新的通用方法来了解集合变率的尺度，这可能是由驱动数值预报和水文模型，而不是预先计算的洪水灾害地图的分辨率。这将有助于为用户对预报作出定性解释，并为观测与模型的适当比较确定定量尺度参数。该方法将在一个新的背景下使用气象分数技能评分的想法。2.我们能否利用低分辨率卫星数据提供有用的城市洪水观测？基于卫星的合成孔径雷达仪器测量后向散射，在农村地区，可以使用图像处理将其转换为洪水范围。监督人员最近的工作是利用昂贵的高分辨率（2- 3米）合成孔径雷达数据以及高分辨率（2米）激光雷达数字表面模型和模拟洪水重现期地图确定城市地区的洪水范围。这些技术是否可以有效地扩展到使用较低分辨率的Sentinel 1开放数据或高分辨率激光雷达不可用的位置？3.哪些观察算子最合适？不同的观测模式比较方法可以提供更好或更差的区别预测，这取决于预测变率的长度尺度和观测可以解决的功能。这有助于逐像素淹没/未淹没的二元比较或相关的概率方法，淹没面积估计和水位估计的洪水范围与数字地形模型的交集。4.资料同化循环对模拟库系统有益吗？实施集合卡尔曼滤波器或重置粒子滤波器可以提供比标准模拟库方法更接近观测到的现实的预测。学生将使用第二年开发的观测算子和现有的数据同化软件库（如PDAF）构建一个系统，并评估对预报的影响。学生应开发通用方法，并通过不同地点的案例研究探索这些问题（例如，缅甸、印度、孟加拉国、爱尔兰、英国），使用集合和单一确定性预报方法，利用卫星、仪表和河流/道路摄像机提供的数据。在项目期间，学生将有机会通过与JBA的工作实习获得进一步的经验。

Timely flood inundation forecasts allow pro-active flood management, reducing loss of life and damage to infrastructure. This project aims to investigate new methods using observations of floods to verify and improve flood inundation forecasts. The state-of-the art in operational flood inundation forecasting at national/trans-national scales uses a simulation library, where rainfall data from numerical weather prediction (NWP) or observations drives a chain of process models and probabilistic analysis, and the final flood forecasts are produced from a pre-computed library of flood maps. This approach saves computation time, allowing near-real-time updating for large areas, which otherwise presents a significant challenge. However, it poses challenges for data assimilation, where observations are combined with model forecasts in a dynamical feedback loop, to keep forecasts on track. Nevertheless, the data assimilation framework still provides consistent techniques for comparison of heterogeneous observations with model data, forecast verification and calibration.The PhD project will address the following questions:1. What are the lengthscales of variability in flood inundation forecasts made using a simulation library approach? New generic methods are needed to understand the scales of ensemble variability, which may be determined by the driving NWP and hydrological models rather than the resolutions of the pre-computed flood hazard maps. This will be valuable for the qualitative interpretation of forecasts for users, as well as setting quantitative scale parameters for appropriate comparisons of observations with models. The approach will use the ideas of the meteorological Fractions Skill Score in a new context. 2. Can we use low resolution satellite data to give useful urban flood observations? Satellite-based Synthetic Aperture Radar (SAR) instruments measure backscatter, which in rural areas can be converted to flood extent using image processing. Recent work by the supervisors has identified flood extent in urban areas using expensive high resolution (2-3m) SAR data together with high resolution (2m) lidar digital surface models (DSMs) and modelled flood return period maps. Can these techniques be usefully extended to use lower resolution Sentinel 1 open data or for locations where high resolution lidar is not available? 3. Which observation operators are most appropriate? Different observation-model comparison approaches may provide better or worse discrimination between forecasts, depending on the lengthscales of forecast variability and the features that can be resolved by the observations. This lends itself to pixel-by-pixel flooded/not flooded binary comparisons or related probabilistic approaches, flooded area estimations and water level estimation by intersection of flood extents with a digital terrain model. 4. Is data assimilation cycling beneficial for a simulation library system? Implementing an ensemble Kalman filter or resampling particle filter may provide forecasts that are closer to the observed reality than the standard simulation library approach. The student will build a system using the observation operators developed in Year 2 and existing data assimilation software libraries (such as PDAF), and evaluate the impact on forecasts. The student is expected to develop generic methods and explore these questions through case studies for different locations (e.g., Myanmar, India, Bangladesh, Ireland, UK) using both ensemble and single deterministic forecast approaches, with data from satellites, gauges and river / road cameras. During the project, the student will have opportunities to gain further experience through work placements with JBA.