气候变化领域集成服务门户(CCPortal): A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model

CCPortal

DOI	10.1029/2019JD031286
	A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model
	Bassett R.; Young P.J.; Blair G.S.; Samreen F.; Simm W.
发表日期	2020
ISSN	2169897X
卷号	125 期号:7
英文摘要	The Weather Research and Forecasting (WRF) community model is widely used to explore cross-scale atmospheric features. Although WRF uncertainty studies exist, these usually involve ensembles where different physics options are selected (e.g., the boundary layer scheme) or adjusting individual parameters. Uncertainty from perturbing initial conditions, which generates internal model variability (IMV), has rarely been considered. Moreover, many off-line WRF research studies generate conclusions based on a single model run without addressing any form of uncertainty. To demonstrate the importance of IMV, or noise, we present a 4-month case study of summer 2018 over London, UK, using a 244-member initial condition ensemble. Simply by changing the model start time, a median 2-m temperature range or IMV of 1.2 °C was found (occasionally exceeding 8 °C). During our analysis, episodes of high and low IMV were found for all variables explored, explained by a relationship with the boundary condition data. Periods of slower wind speed input contained increased IMV, and vice versa, which we hypothesis is related to how strongly the boundary conditions influence the nested region. We also show the importance of IMV effects for the uncertainty of derived variables like the urban heat island, whose median variation in magnitude is 1 °C. Finally, a realistic ensemble size to capture the majority of WRF IMV is also estimated, essential considering the high computational overheads (244 members equaled 140,000 CPU hours). We envisage that highlighting considerable IMV in this repeatable manner will help advance best practices for the WRF and wider regional climate modeling community. ©2020. The Authors.
英文关键词	ensemble; initial conditions; internal model variability (IMV); regional climate model (RCM); uncertainty; Weather Research and Forecasting (WRF)
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/186081
作者单位	Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom; Data Science Institute, Lancaster University, Lancaster, United Kingdom; School of Computing and Communications, Lancaster University, Lancaster, United Kingdom
推荐引用方式 GB/T 7714	Bassett R.,Young P.J.,Blair G.S.,et al. A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model[J],2020,125(7).
APA	Bassett R.,Young P.J.,Blair G.S.,Samreen F.,&Simm W..(2020).A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model.Journal of Geophysical Research: Atmospheres,125(7).
MLA	Bassett R.,et al."A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model".Journal of Geophysical Research: Atmospheres 125.7(2020).