Impacts of initial condition perturbation blending in 10- and 40-member convection-allowing ensemble forecasts

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Aaron Johnson, Xuguang Wang
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引用次数: 0

Abstract

A series of convection-allowing 36-hour ensemble forecasts during the 2018 Spring season are used to better understand the impacts of ensemble configuration and blending different sources of initial condition (IC) perturbation. Ten- and 40-member ensemble configurations are initialized with the multi-scale IC perturbations generated as a product of convective-scale data assimilation (MULTI), and initialized with the MULTI IC perturbations blended with IC perturbations downscaled from coarser resolution ensembles (BLEND). The forecast performance of both precipitation and non-precipitation variables is consistently improved by the larger ensemble size. The benefit of the larger ensemble is largely, but not entirely, due to compensating for under-dispersion in the fixed-physics ensemble configuration. A consistent improvement in precipitation forecast skill results from blending in the 10-member ensemble configuration, corresponding to a reduction in the ensemble calibration error (i.e., reliability component of Brier Score). In the 40-member ensemble configuration, the advantage of blending is limited to the ∼18-22 hour lead times at all precipitation thresholds, and the ∼35-36 hour lead times at the lowest threshold, both corresponding to an improved resolution component of the Brier Score. The advantage of blending in the 40-member ensemble during the diurnal convection maximum of ∼18-22 hour lead times is primarily due to cases with relatively weak synoptic scale forcing while advantages at later lead times beyond ∼30 hours lead time are most prominent on cases with relatively strong synoptic scale forcing. The impacts of blending and ensemble configuration on forecasts of non-precipitation variables is generally consistent with the impacts on the precipitation forecasts.
初始条件扰动混合在允许对流的 10 成员和 40 成员集合预报中的影响
利用2018年春季一系列允许对流的36小时集合预报,更好地了解集合配置和混合不同初始条件(IC)扰动源的影响。10人和40人集合配置使用对流尺度数据同化生成的多尺度IC扰动(MULTI)进行初始化,并使用MULTI IC扰动与较粗分辨率集合降尺度IC扰动(BLEND)混合进行初始化。集合规模越大,降水和非降水变量的预报性能就越好。较大集合的好处主要是(但不完全是)补偿了固定物理集合配置中的分散不足。在 10 个成员的集合配置中,降水预报技能因集合校准误差(即 Brier Score 的可靠性部分)的减少而得到持续改善。在 40 个成员的集合配置中,混合的优势仅限于所有降水阈值的 18-22 小时前置时间,以及最低阈值的 35-36 小时前置时间,两者都对应于 Brier Score 分辨率部分的提高。在昼夜对流最大值的 18-22 小时前置时间内,40 成员集合的混合优势主要是由于具有相对较弱的同步尺度强迫的情况,而在∼30 小时前置时间以后的前置时间内,混合优势在具有相对较强的同步尺度强迫的情况下最为突出。混合和集合配置对非降水变量预报的影响与对降水预报的影响基本一致。
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来源期刊
Monthly Weather Review
Monthly Weather Review 地学-气象与大气科学
CiteScore
6.40
自引率
12.50%
发文量
186
审稿时长
3-6 weeks
期刊介绍: Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.
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