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首页> 外文期刊>SOLA: Scientific Online Letters on the Atmosphere >Sensitivity to Initial and Boundary Perturbations in Convective-Scale Ensemble-Based Data Assimilation: Imperfect-Model OSSEs
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Sensitivity to Initial and Boundary Perturbations in Convective-Scale Ensemble-Based Data Assimilation: Imperfect-Model OSSEs

机译：对对流级合奏的数据同化中的初始和边界扰动的敏感性：不完美的模型OSSES

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摘要

This study investigates the impact of applying different types of initial and boundary perturbations for convective-scale ensemble data assimilation systems. Several observing system simulation experiments (OSSEs) were performed with a 2-km horizontal resolution, considering a realistic environment, taking model error into account, and combining different perturbations' types with warm/cold start initialization. Initial perturbations produce a long-lasting impact on the analysis's quality, particularly for variables not directly linked to radar observations. Warm-started experiments provide the most accurate analysis and forecasts and a more consistent ensemble spread across the different spatial scales. Random small-scale perturbations exhibit similar results, although a longer convergence time is required to up-and-downscale the initial perturbations to obtain a similar error reduction. Adding random large-scale perturbations reduce the error in the first assimilation cycles but produce a slightly detrimental effect afterward.
机译：本研究调查了对对流级集合数据同化系统应用不同类型的初始和边界扰动的影响。考虑到实际环境，以2公里的水平分辨率进行了几个横向分辨率，考虑到算法错误，并将不同的扰动类型与温暖/冷启动初始化相结合的不同扰动类型进行了几个观察系统仿真实验（OSSes）。初始扰动会对分析的质量产生持久的影响，特别是对于与雷达观测没有直接相关的变量。温暖的实验提供了最准确的分析和预测，以及跨越不同空间尺度的更加一致的集合。随机小规模扰动表现出类似的结果，尽管需要更长的收敛时间来上升初始扰动以获得类似的误差减少。添加随机大规模扰动减少了第一次同化循环中的错误，但后来产生略微有害的效果。

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