气候变化领域集成服务门户(CCPortal): Comparison and Verification of Point-Wise and Patch-Wise Localized Probability-Matched Mean Algorithms for Ensemble Consensus Precipitation Forecasts

CCPortal

DOI	10.1029/2020GL087839
	Comparison and Verification of Point-Wise and Patch-Wise Localized Probability-Matched Mean Algorithms for Ensemble Consensus Precipitation Forecasts
	Snook N.; Kong F.; Clark A.; Roberts B.; Brewster K.A.; Xue M.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:12
英文摘要	When applied to precipitation on large forecast domains, the probability-matched ensemble mean (PM mean) can exhibit biases and artifacts due to using distributions from widely varying precipitation regimes. Recent studies have investigated localized PM (LPM) means, which apply the PM mean over local areas surrounding individual points or local patches, the latter requiring far fewer computational resources. In this study, point-wise and patch-wise LPM means are evaluated for 18–24-hr precipitation forecasts of a quasi-operational ensemble of 10 Finite-Volume Cubed-Sphere (FV3) forecast members. Point-wise and patch-wise LPM means exhibited similar forecast performance, outperforming PM and simple means in terms of fractions skill score and variance spectra while exhibiting superior bias characteristics when light smoothing was applied. Based on the results, an LPM mean using local patches of 60 × 60 km and calculation domains of 180 × 180 km is well suited for operational warm-season precipitation forecasting over the contiguous United States. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	Weather forecasting; Computational resources; Forecast performance; Local areas; Point wise; Precipitation forecast; Precipitation regimes; Skill Score; Warm season precipitation; Probability distributions; algorithm; comparative study; computer system; ensemble forecasting; finite volume method; model test; precipitation assessment; probability; smoothing; spatial distribution
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/170252
作者单位	Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK, United States; NOAA/OAR/National Severe Storms Laboratory, Norman, OK, United States; Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK, United States; NOAA/NCEP/Storm Prediction Center, Norman, OK, United States; School of Meteorology, University of Oklahoma, Norman, OK, United States
推荐引用方式 GB/T 7714	Snook N.,Kong F.,Clark A.,et al. Comparison and Verification of Point-Wise and Patch-Wise Localized Probability-Matched Mean Algorithms for Ensemble Consensus Precipitation Forecasts[J],2020,47(12).
APA	Snook N.,Kong F.,Clark A.,Roberts B.,Brewster K.A.,&Xue M..(2020).Comparison and Verification of Point-Wise and Patch-Wise Localized Probability-Matched Mean Algorithms for Ensemble Consensus Precipitation Forecasts.Geophysical Research Letters,47(12).
MLA	Snook N.,et al."Comparison and Verification of Point-Wise and Patch-Wise Localized Probability-Matched Mean Algorithms for Ensemble Consensus Precipitation Forecasts".Geophysical Research Letters 47.12(2020).