DOI: 10.1002/joc.5897
Scopus记录号: 2-s2.0-85056283195
论文题名: Comparison of statistical and dynamical downscaling methods for seasonal-scale winter precipitation predictions over north India
作者: Tiwari P.R. ; Kar S.C. ; Mohanty U.C. ; Dey S. ; Sinha P. ; Shekhar M.S. ; Sokhi R.S.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2019
卷: 39, 期: 3 起始页码: 1504
结束页码: 1516
语种: 英语
英文关键词: bias correction
; CCA
; downscaling
; north India
; RegCM
; winter precipitation
Scopus关键词: Oceanography
; Surface waters
; Weather forecasting
; Bias correction
; Down-scaling
; North India
; RegCM
; Winter precipitation
; Climate models
英文摘要: The main aim of the present study is to analyse the capabilities of two downscaling approaches (statistical and dynamical) in predicting wintertime seasonal precipitation over north India. For this purpose, a canonical correlation analysis (CCA) based statistical downscaling approach and dynamical downscaling approach (at 30 km) with an optimized configuration of the regional climate model (RegCM) nested in coarse resolution global spectral model have been used for a period of 28 years (1982–2009). For CCA, nine predictors (precipitation, zonal and meridional winds at 850 and 200 hPa, temperature at 200 hPa and sea surface temperatures) over three different domains were selected. The predictors were chosen based on the statistically significant teleconnection maps and physically based relationships between precipitation over the study region and meteorological variables. The validation revealed that both the downscaling approaches provided improved precipitation forecasts compared to the global model. Reasons for improved prediction by downscaling techniques have been examined. The improvement mainly comes due to better representation of orography, westerly moisture transport and vertical pressure velocity in the regional climate model. Furthermore, two bias correction methods namely quantile mapping (QM) and mean bias-remove (MBR) have been applied on downscaled RegCM, statistically downscaled CCA as well as the global model products. It was found that when the QM-based bias correction is applied on dynamically downscaled RegCM products, it has better skill in predicting wintertime precipitation over the study region compared to the CCA-based statistical downscaling. Overall, the results indicate that the QM-based bias-corrected downscaled RegCM model is a useful tool for wintertime seasonal-scale precipitation prediction over north India. © 2018 Royal Meteorological Society Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116535
Appears in Collections: 全球变化的国际研究计划
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作者单位: Centre for Atmospheric and Climate Physics Research, Hatfield, United Kingdom; National Centre for Medium Range Weather Forecasting, Noida, India; School of Earth Ocean and Climate Sciences, IIT Bhubaneswar, Bhubaneswar, India; Centre for Atmospheric Sciences, IIT Delhi, Delhi, India; Snow and Avalanche Study Establishment, Chandigarh, India
Recommended Citation:
Tiwari P.R.,Kar S.C.,Mohanty U.C.,et al. Comparison of statistical and dynamical downscaling methods for seasonal-scale winter precipitation predictions over north India[J]. International Journal of Climatology,2019-01-01,39(3)