论文标题
基于合奏的大规模数据同化的弹性框架
An elastic framework for ensemble-based large-scale data assimilation
论文作者
论文摘要
混沌系统的预测依赖于使用数值模型的传感器数据（观测）的浮动融合来决定良好的系统轨迹并补偿非线性反馈效应。基于整体的数据同化（DA）是根据传播扰动模型实现的集合的主要方法。在本文中，我们开发了一个弹性，在线，容忍性和模块化的框架，称为Melissa-da，用于大规模集合的DA。 Melissa-DA允许在运行时添加弹性或去除计算资源以进行状态传播。基于列表调度的动态负载平衡。集合成员生成的数据的在线处理能够避免使用基于文件的I/O瓶颈。我们的实施嵌入了PDAF并行DA引擎，从而实现了各种DA方法的使用。 Melissa-Da可以通过将成员背景状态转换为分析状态来支持额外的基于合奏的可能性。实验证实了多达16,240个核心的Melissa-Da的出色可伸缩性，以依靠约4 m网格电池的域上的Parparflow模型来传播16,384个成员，以供区域水文临界区域同化。
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.
