DOI: 10.1002/2016JD025489
论文题名: Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts
作者: Shastri H. ; Ghosh S. ; Karmakar S.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2017
卷: 122, 期: 3 起始页码: 1617
结束页码: 1634
语种: 英语
英文关键词: extreme rainfall forecasts
; probabilistic forecasts
; quantile regression
; urban extremes
Scopus关键词: early warning system
; extreme event
; flood forecasting
; monsoon
; numerical model
; precipitation (climatology)
; precipitation assessment
; precipitation intensity
; probability
; rainfall
; regression analysis
; urban climate
; vulnerability
; weather forecasting
; India
; Maharashtra
; Mumbai
英文摘要: Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning. ©2017. American Geophysical Union. All Rights Reserved. 资助项目: 06/23/2013-INCSW/194-213
; II
; MoES/PAMC/H&C/36/2013-PC‐
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/62713
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Powai, India; Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, India; Centre for Environmental Science and Engineering, Indian Institute of Technology Bombay, Powai, India
Recommended Citation:
Shastri H.,Ghosh S.,Karmakar S.. Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts[J]. Journal of Geophysical Research: Atmospheres,2017-01-01,122(3)