Improving flood prediction using data assimilation
Improving flood prediction using data assimilation
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发表时间：
2019
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通讯作者：
E. Cooper
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作者：
E. Cooper
河流泛滥是世界范围内一个代价高昂的问题。及时、准确地预测洪水的行为对于帮助人们做好准备至关重要。数学水动力模型可以在给定输入信息的情况下预测洪水的行为，如河流水深测量、当地地形、流入和模型参数的值。这些输入的不确定性导致模型预测的不准确；数据同化可用于通过将模型预测与观测信息相结合来改进预测，同时考虑到两者中的不确定性。在这篇论文中，我们研究了在洪水预报的数据同化中最大限度地利用来自星基合成孔径(SAR)仪器的观测信息的方法。我们利用集合变换卡尔曼滤波的合成双生试验表明，使用联合状态参数数据同化技术来修正模式的航道摩阻参数和水位，为模式预报提供了显著的、长期的好处。我们证明了渠道摩阻参数的误差和入流是相互依赖的。我们提出了一种新的观测算子，允许直接使用测量的SAR后向散射值，潜在地允许每幅SAR图像使用更多的观测值。我们在合成实验中对新的观测算子进行了测试，结果表明我们可以使用新的方法成功地更新洪水预报和模型航道摩阻参数值。我们表明，不同的观测算子方法可以产生显着不同的模式预报更新，并说明负责的物理机制。最后，我们使用新的观测算子来同化来自真实SAR图像的后向散射值，这表明我们的新方法可以在实际的案例研究中用于改进淹没预报。对不同观测算子产生更新的物理机制的更好的理解为改进SAR数据的观测影响提供了洞察力。
River flooding is a costly problem worldwide. Timely, accurate prediction of the behaviour of flood water is vital in helping people make preparations. Mathematical hydrodynamic models can predict the behaviour of flood water given information about inputs such as river bathymetry, local topography, inflows, and values for model parameters. Uncertainty in these inputs leads to inaccuracies in model predictions; data assimilation can be used to improve forecasts by combining model predictions with observational information, taking into account uncertainties in both. In this thesis we investigate ways to maximize the impact of observational information from satellite-based synthetic aperture (SAR) instruments in data assimilation for inundation forecasting. We show in synthetic twin experiments using an ensemble transform Kalman filter that using joint state-parameter data assimilation techniques to correct the model channel friction parameter as well as water levels provides a significant, long lasting benefit to the model forecast. We show that errors in the channel friction parameter and inflow are interdependent. We propose a novel observation operator that allows direct use of measured SAR backscatter values, potentially allowing the use of many more observations per SAR image. We test our new observation operator in synthetic experiments, showing that we can successfully update inundation forecasts and the value of the model channel friction parameter using our new approach. We show that different observation operator approaches can generate significantly different updates to model forecasts and illustrate the physical mechanisms responsible. Lastly, we use our new observation operator to assimilate backscatter values from real SAR images, showing that our new approach can be used to improve inundation forecasts in a real case study. Improved understanding of the physical mechanisms by which updates are generated by different observation operators provides insights into improving the observation impact of SAR data.