DOI: 10.1002/2017MS001003
Scopus记录号: 2-s2.0-85026634371
论文题名: Impact of bias-corrected reanalysis-derived lateral boundary conditions on WRF simulations
作者: Moalafhi D ; B ; , Sharma A ; , Evans J ; P ; , Mehrotra R ; , Rocheta E
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2017
卷: 9, 期: 4 起始页码: 1828
结束页码: 1846
语种: 英语
英文关键词: Atmospheric humidity
; Boundary conditions
; Infrared devices
; Infrared instruments
; Statistics
; Atmospheric infrared sounders
; Dynamical downscaling
; Lateral boundary conditions
; Mean and standard deviations
; Reanalyses
; Southern Africa
; Temperature and relative humidity
; Weather research and forecasting models
; Weather forecasting
; accuracy assessment
; air temperature
; boundary condition
; climate modeling
; data set
; downscaling
; experimental study
; forecasting method
; hydrological modeling
; observational method
; performance assessment
; relative humidity
; satellite data
; South Africa
英文摘要: Lateral and lower boundary conditions derived from a suitable global reanalysis data set form the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis data sets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational data sets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias-corrected WRF simulations are, however, found to be marginally better than mean only bias-corrected WRF simulations and raw ERA-I reanalysis-driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted. © 2017. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75744
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Department of Environmental Science, University of Botswana, Gaborone, Botswana; School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia; Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia
Recommended Citation:
Moalafhi D,B,, Sharma A,et al. Impact of bias-corrected reanalysis-derived lateral boundary conditions on WRF simulations[J]. Journal of Advances in Modeling Earth Systems,2017-01-01,9(4)