What Is the Predictability Limit of Midlatitude Weather?
What Is the Predictability Limit of Midlatitude Weather?
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DOI：
10.1175/jas-d-18-0269.1
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发表时间：
2019-04
影响因子：
3.1
通讯作者：
Fuqing Zhang;Y. Q. Sun;L. Magnusson;R. Buizza;Shian‐Jiann Lin;Jan‐Huey Chen;K. Emanuel
中科院分区：
文献类型：
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作者：
Fuqing Zhang;Y. Q. Sun;L. Magnusson;R. Buizza;Shian‐Jiann Lin;Jan‐Huey Chen;K. Emanuel
理解诸如中纬度冬季风暴和夏季季风暴雨等日常天气现象的可预报性极限对数值天气预报（NWP）至关重要。利用欧洲中期天气预报中心（ECMWF；9千米业务模式）的集合试验以及美国下一代全球预报系统（NGGPS；3千米）的孪生试验，通过前所未有的高分辨率全球模式对这一可预报性极限进行了研究。结果表明，中纬度天气的可预报性极限可能确实存在，并且是潜在动力系统和不稳定性所固有的，即使预报模式和初始条件近乎完美。目前，中纬度瞬时天气的有效预报时效约为10天，这就是实际的可预报性极限。将当前初始条件的不确定性降低一个数量级，可使日常天气的确定性预报时效最多延长5天，但对于改进雷暴等小尺度现象的预报空间则要小得多。实现这一额外的可预报性极限可带来巨大的社会经济效益，但需要整个气象界协同努力，设计更好的数值天气模式，改进观测，并通过先进的数据同化和计算技术更好地利用观测资料。
Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.