全球地球观测系统-S2S-2 次季节预报中的热带气旋

J. García‐Franco, Chia-Ying Lee, Suzana J. Camargo, Michael K. Tippett, Neljon G. Emlaw, Daehyun Kim, Young-Kwon Lim, A. Molod
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摘要

本文分析了美国国家航空航天局全球对地观测系统副季节到季节预报系统(GEOS-S2S)第 2 版中热带气旋(TC)的气候学、预报技能和可预测性。与观测结果相比,GEOS 合理地模拟了热带气旋的数量和空间分布,但在大西洋,由于加勒比海和墨西哥湾的成因率较低,模型模拟的热带气旋数量太少。通过成因潜势指数诊断的环境条件并不能清楚地解释成因率的模式偏差,尤其是在大西洋。在风暴尺度上,全球地球观测系统的再预测复制了观测到的热带气旋的热力学和动力学结构的几个关键方面,如暖核心和次级环流。然而,在评估垂直速度、降水和湿度时,该模式未能模拟偏离中心的眼墙。对热气旋成因和发生的预测能力分析表明,GEOS 的预测能力与世界气象组织 S2S 资料库中的其他全球模式相当,其预测能力还可通过增加集合规模进一步提高。经过校准后,GEOS 对北太平洋西部和南印度洋的预报提前 20 天即可达到熟练程度。基于模式的可预测性分析表明,马登-朱利安涛动(MJO)是预测 14 天前发生的热带气旋的重要来源。在强 MJO 条件下初始化的预测显示了第 3 周以后的可预测性。然而,由于预测分布中的模式偏差,与 MJO 相关的预测技能和可预测性之间存在明显差距,需要进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tropical cyclones in the GEOS-S2S-2 subseasonal forecasts
This paper analyzes the climatology, prediction skill, and predictability of tropical cyclones (TCs) in NASA’s Global Earth Observing System Subseasonal to Seasonal (GEOS-S2S) forecast system version 2. GEOS reasonably simulates the number and spatial distribution of TCs compared to observations except in the Atlantic where the model simulates too few TCs due to low genesis rates in the Caribbean Sea and Gulf of Mexico. The environmental conditions, diagnosed through a genesis potential index, do not clearly explain model biases in the genesis rates, especially in the Atlantic. At the storm-scale, GEOS reforecasts replicate several key aspects of the thermodynamic and dynamic structure of observed TCs, such as a warm core and the secondary circulation. The model, however, fails to simulate an off-center eyewall when evaluating vertical velocity, precipitation and moisture. The analysis of prediction skill of TC genesis and occurrence shows that GEOS has comparable skill to other global models in WMO S2S archive and that its skill could be further improved by increasing the ensemble size. After calibration, GEOS forecasts are skillful in the Western North Pacific and Southern Indian Ocean up to 20 days in advance. A model-based predictability analysis demonstrates the importance of the Madden-Julian Oscillation (MJO) as a source of predictability of TC occurrence beyond the 14 day lead-time. Forecasts initialized under strong MJO conditions show evidence of predictability beyond week 3. However, due to model biases in the forecast distribution there are notable gaps between MJO-related prediction skill and predictability which require further study.
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