美国毗连地区高分辨率快速更新(HRRR)模式预报中的暖季降水分类

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Monthly Weather Review Pub Date : 2023-11-21 DOI:10.1175/mwr-d-23-0108.1
I-Han Chen, Judith Berner, Christian Keil, Ying-Hwa Kuo, George C. Craig
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引用次数: 0

摘要

本研究利用对流调整时间尺度来确定美国毗连区(CONUS)不同地区的平衡和非平衡对流的气候频率,并通过允许对流的高分辨率快速更新(HRRR)预报系统进行建模。我们发现,被北纬 40 度平行线分隔的北部和南部区域的气候有质的不同。对流调节时间尺度反映了这样一个事实,即北部区域的对流受同步流(导致平衡)的支配,而南部区域的对流则以局部强迫、非平衡对流为主。利用机器学习算法,我们证明对流调整时间尺度诊断提供了合理的分类,与平衡和非平衡对流的基本动态一致。此外,对流调整时间尺度还能显示模式定量降水预报(QPF)的质量,因为它能正确反映在强同步强迫下降水的较高定量降水预报技能。这种基于对流强迫强度的诊断方法将在未来对美国中部不同地区的研究中使用,以客观地区分不同的天气状况,并探索与暖季降水可预测性的潜在联系。
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Classification of Warm-Season Precipitation in High-Resolution Rapid Refresh (HRRR) model forecasts over the Contiguous United States
This study uses the convective adjustment time scale to identify the climatological frequency of equilibrium and non-equilibrium convection in different parts of the Contiguous United States (CONUS) as modeled by the operational convection-allowing High-Resolution Rapid Refresh (HRRR) forecast system. We find a qualitatively different climatology in the northern and southern domains separated by the 40°N parallel. The convective adjustment time scale picks up the fact that convection over the northern domains is governed by synoptic flow (leading to equilibrium) while locally forced, non-equilibrium convection dominates over the southern domains. Using a machine learning algorithm, we demonstrate that the convective adjustment timescale diagnostic provides a sensible classification that agrees with the underlying dynamics of equilibrium and non-equilibrium convection. Furthermore, the convective adjustment time scale can indicate the model quantitative precipitation forecast (QPF) quality, as it correctly reflects the higher QPF skill for precipitation under strong synoptic forcing. This diagnostic based on the strength of forcing for convection will be employed in future studies across different parts of CONUS to objectively distinguish different weather situations and explore the potential connection to warm-season precipitation predictability.
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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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