DOI: 10.1002/joc.5822
Scopus记录号: 2-s2.0-85052834763
论文题名: Evaluation of different large-scale predictor-based statistical downscaling models in simulating zone-wise monsoon precipitation over India
作者: Akhter J. ; Das L. ; Meher J.K. ; Deb A.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2019
卷: 39, 期: 1 起始页码: 465
结束页码: 482
语种: 英语
英文关键词: combined predictors
; EOF analysis
; homogeneous zones
; reanalysis
; statistical downscaling
Scopus关键词: Atmospheric humidity
; Atmospheric thermodynamics
; Calibration
; Linear regression
; Microcomputers
; Orthogonal functions
; combined predictors
; Eof analysis
; Homogeneous zone
; Reanalysis
; Statistical downscaling
; Principal component analysis
; calibration
; climate prediction
; computer simulation
; downscaling
; monsoon
; numerical model
; performance assessment
; precipitation (climatology)
; principal component analysis
; statistical analysis
; weather forecasting
; India
英文摘要: Selection of suitable predictors for downscaling local-scale precipitation from the wide range of large-scale predictors available in National Center for Atmospheric Research/National Centers for Environmental Prediction (NCAR/NCEP) reanalysis is a challenging task because of the existence of the complex interactions between local-scale predictands and large-scale predictor fields. An attempt was made to assess how well different large-scale predictors were able to reproduce local-scale monsoon precipitation over seven homogeneous zones of India through statistical downscaling. For calibration of downscaling (DS) models, the principal component (PC)-based multiple linear regression approach was adopted where each raw grid-point predictor field transformed into PCs using empirical orthogonal function (EOF) analysis. The predictors consistently producing better downscaled results across four nonoverlapping calibration and validation periods were identified as “superior predictor” (SP). It was found that some common predictors like precipitable water; specific and relative humidity at different levels have emerged as SP predictors over several zones. In general, SP predictors have not been much sensitive with small changes in the domain size. However, a decline in performances of DS models was noticed for the majority of SP predictors for a large increase in the size of domains. Especially, the largest South Asia domain has been the most inappropriate domain as very few predictors found to be suitable for downscaling. In general, about 40% out of 36 numbers of combined predictors were identified as potential SP predictors over the majority of the zones. Several numbers of combined SP predictors have also produced slightly superior skills compared to single SP predictors. In many cases, predictors showing poor performance as single predictors have produced improved performances when combined with other predictors. © 2018 Royal Meteorological Society Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116667
Appears in Collections: 全球变化的国际研究计划
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作者单位: Department of Physics, Jadavpur University, Kolkata, India; Department of Agricultural Meteorology and Physics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, India
Recommended Citation:
Akhter J.,Das L.,Meher J.K.,et al. Evaluation of different large-scale predictor-based statistical downscaling models in simulating zone-wise monsoon precipitation over India[J]. International Journal of Climatology,2019-01-01,39(1)