Prediction of chaotic systems relies on a floating fusion of sensor data (observations) with a numerical model to decide on a good system trajectory and to compensate nonlinear feedback effects. Ensemble-based data assimilation (DA) is a major method for this concern depending on propagating an ensemble of perturbed model realizations.In this paper we develop an elastic, online, fault-tolerant and modular framework called Melissa-DA for large-scale ensemble-based DA. Melissa-DA allows elastic addition or removal of compute resources for state propagation at runtime. Dynamic load balancing based on list scheduling ensuresefficient execution. Online processing of the data produced by ensemble members enables to avoid the I/O bottleneck of file-based approaches. Our implementation embeds the PDAF parallel DA engine, enabling the use of various DA methods. Melissa-DA can support extra ensemble-based DAmethods by implementing the transformation of member background states into analysis states. Experiments confirm the excellent scalability of Melissa-DA, running on up to 16,240 cores, to propagate 16,384 members for a regional hydrological critical zone assimilation relying on theParFlow model on a domain with about 4 M grid cells.

中文翻译：

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一个基于集合的大规模数据同化的弹性框架

混沌系统的预测依赖于传感器数据（观测值）与数值模型的浮动融合，以决定良好的系统轨迹并补偿非线性反馈效应。基于集成的数据同化（DA）是解决此问题的一种主要方法，这取决于传播受干扰的模型实现的集成。基于DA。Melissa-DA允许弹性添加或删除计算资源，以在运行时进行状态传播。基于列表调度的动态负载平衡可确保高效执行。集成成员生成的数据的在线处理可以避免基于文件的方法的I / O瓶颈。我们的实现嵌入了PDAF并行DA引擎，支持使用各种DA方法。通过实现将成员背景状态转换为分析状态，Melissa-DA可以支持基于整体的额外DA方法。实验证实，Melissa-DA具有出色的可扩展性，该内核可在多达16,240个核心上运行，从而可以在具有约4 M个网格单元的域上依靠ParFlow模型传播16,384个成员，以进行区域水文临界区同化。