Dynamics of real-time forecasting failure and recovery due to data gaps: A study using EnKF-based assimilation with the Lorenz model

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Sicheng Wu, Ruo-Qian Wang
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引用次数: 0

Abstract

Data assimilation-based real-time forecasting is widely used in meteorological and hydrological applications, where continuous data streams are employed to update forecasts and maintain accuracy. However, the reliability of the data source can be compromised due to sensor and communication failures or physical or cyber-attacks, and the impact of data stream failures on the accuracy of the forecasting system is not well understood. This study aims to systematically investigate the process of data stream failure and recovery for the first time. To achieve this, data gaps with varying lengths and timings are introduced to EnKF-based data assimilation system on the Lorenz model operating in both chaotic and periodic modes. Results show that the forecasting error grows exponentially in the chaotic mode but was limited in the periodic mode from the start of the data gap. For chaotic mode, the recovery of the system depends on the length of the data gap if the model error is not saturated; after saturation, the timing of the data stream recovery is important. Moreover, even long after restarting the data assimilation in the chaotic mode, the forecasting system cannot fully restore the original accuracy, while the periodic mode is generally resilient to disruption. This research introduces new metrics for quantifying system resilience and provides crucial insights into the long-term implications of data gaps, advancing our understanding of forecasting system behavior and reliability.
数据缺口导致的实时预报失败和恢复动态:基于 EnKF 的洛伦兹模式同化研究
基于数据同化的实时预报广泛应用于气象和水文领域,利用连续数据流更新预报并保持准确性。然而,由于传感器和通信故障、物理或网络攻击等原因,数据源的可靠性可能会受到影响,而数据流故障对预报系统准确性的影响尚不十分清楚。本研究旨在首次系统地研究数据流故障和恢复过程。为此,在基于 EnKF 的数据同化系统中引入了不同长度和时间的数据间隙,这些数据间隙是以混沌和周期模式运行的洛伦兹模型。结果表明,在混沌模式下,预报误差呈指数增长,但在周期模式下,从数据间隙开始,预报误差就受到了限制。在混沌模式下,如果模型误差未达到饱和,系统的恢复取决于数据间隙的长度;饱和之后,数据流恢复的时间非常重要。此外,即使在混沌模式下重启数据同化很长时间后,预报系统也无法完全恢复原来的精度,而周期模式一般对中断具有很强的恢复能力。这项研究引入了量化系统恢复能力的新指标,并对数据缺口的长期影响提出了重要见解,从而推进了我们对预报系统行为和可靠性的理解。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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