Data assimilation (DA) is a powerful tool to optimally combine uncertain model simulations and observations. Among DA techniques, the particle filter (PF) has gained attention for its capacity to deal with nonlinear systems and for its relaxation of the Gaussian assumption. However, the PF may suffer from degeneracy and sample impoverishment. In this study, we propose an innovative approach, based on a tempered particle filter (TPF), aiming at mitigating PFs issues, thus extending over time the assimilation benefits. Probabilistic flood maps derived from synthetic aperture radar data are assimilated into a flood forecasting model through an iterative process including a particle mutation in order to keep diversity within the ensemble. Results show an improvement of the model forecasts accuracy, with respect to the Open Loop: on average the root mean square error (RMSE) of water levels decrease by 80% at the assimilation time and by 60% 2 days after the assimilation. A comparison with the Sequential Importance Sampling (SIS) is carried out showing that although SIS performances are generally comparable to the TPF ones at the assimilation time, they tend to decrease more quickly. For instance, on average TPF-based RMSE are 20% lower compared to the SIS-based ones 2 days after the assimilation. The application of the TPF determines higher critical success index values compared to the SIS. On average the increase in performances lasts for almost 3 days after the assimilation. Our study provides evidence that the application of the variant of the TPF enables more persistent benefits compared to the SIS.

中文翻译：

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增强粒子滤波器以增强 SAR 导出的洪水范围图与洪水预报模型的同化


数据同化（DA）是优化结合不确定模型模拟和观测的强大工具。在 DA 技术中，粒子滤波器（PF）因其处理非线性系统的能力以及对高斯假设的松弛而受到关注。然而，PF 可能会遭受简并和样本匮乏的影响。在这项研究中，我们提出了一种基于回火粒子过滤器（TPF）的创新方法，旨在减轻 PF 问题，从而随着时间的推移延长同化效益。从合成孔径雷达数据导出的概率洪水图通过包括粒子突变在内的迭代过程被同化到洪水预报模型中，以保持集合内的多样性。结果表明，相对于开环，模型预报精度有所提高：平均而言，水位均方根误差 (RMSE) 在同化时下降了 80%，在同化后 2 天下降了 60%。与顺序重要性采样（SIS）的比较表明，虽然 SIS 的性能在同化时通常与 TPF 的性能相当，但它们往往下降得更快。例如，同化后 2 天，基于 TPF 的 RMSE 平均比基于 SIS 的 RMSE 低 20%。与 SIS 相比，TPF 的应用决定了更高的关键成功指数值。平均而言，在同化后，性能的提高会持续近 3 天。我们的研究提供的证据表明，与 SIS 相比，TPF 变体的应用可以带来更持久的益处。