Observation operators for assimilation of satellite observations in fluvial inundation forecasting
Observation operators for assimilation of satellite observations in fluvial inundation forecasting
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DOI：
10.5194/hess-23-2541-2019
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发表时间：
2018-12
影响因子：
6.3
通讯作者：
E. Cooper;S. Dance;J. García-Pintado;N. Nichols;Polly J. Smith
中科院分区：
文献类型：
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作者：
E. Cooper;S. Dance;J. García-Pintado;N. Nichols;Polly J. Smith
抽象。从卫星合成孔径雷达（SAR）仪器的图像包含大量的信息，洪水的位置在河流洪水事件。这种观测信息通常覆盖很大的空间区域，但只有在水位迅速变化的情况下才能在短时间内发挥作用。数据同化使我们能够结合联合收割机有价值的SAR衍生的观测信息与连续预测从计算流体动力学模型，从而产生一个更好的预测比单独使用的模型。为了以这种方式使用观测，需要合适的观测算子。在本文中，我们表明，不同类型的观测运营商可以产生非常不同的校正预测水位，这会影响预测的质量。我们讨论了不同的观测算子更新模拟水位的物理机制，并介绍了一种新的观测算子洪水预报。新的运营商的性能进行了比较，在合成实验与两个更传统的方法。传统的方法都是使用SAR得到的水位观测值来校正模型预测。我们的新运营商，而不是设计成使用从SAR仪器作为观测的后向散射值，这样的方法还没有被用于集合卡尔曼滤波框架之前。直接使用后向散射观测开辟了使用来自每个SAR图像的更多信息的可能性，并可能加快产生更新模型预测所需的观测所需的时间。我们比较了三种不同方法的优点和缺点，参考的物理机制，每个观测运营商允许数据同化更新水位合成孪生实验在理想化的域。
Abstract. Images from satellite-based synthetic aperture radar (SAR) instruments contain large amounts of information about the position of floodwater during a river flood event. This observational information typically covers a large spatial area but is only relevant for a short time if water levels are changing rapidly. Data assimilation allows us to combine valuable SAR-derived observed information with continuous predictions from a computational hydrodynamic model and thus to produce a better forecast than using the model alone. In order to use observations in this way, a suitable observation operator is required. In this paper we show that different types of observation operators can produce very different corrections to predicted water levels; this impacts the quality of the forecast produced. We discuss the physical mechanisms by which different observation operators update modelled water levels and introduce a novel observation operator for inundation forecasting. The performance of the new operator is compared in synthetic experiments with that of two more conventional approaches. The conventional approaches both use observations of water levels derived from SAR to correct model predictions. Our new operator is instead designed to use backscatter values from SAR instruments as observations; such an approach has not been used before in an ensemble Kalman filtering framework. Direct use of backscatter observations opens up the possibility of using more information from each SAR image and could potentially speed up the time taken to produce observations needed to update model predictions. We compare the strengths and weaknesses of the three different approaches with reference to the physical mechanisms with which each of the observation operators allow data assimilation to update water levels in synthetic twin experiments in an idealised domain.