DOI: 10.1002/joc.5442
论文题名: Efficacy of tendency and linear inverse models to predict southern Peru's rainy season precipitation
作者: Wu S. ; Notaro M. ; Vavrus S. ; Mortensen E. ; Montgomery R. ; de Piérola J. ; Block P.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38, 期: 5 起始页码: 2590
结束页码: 2604
语种: 英语
英文关键词: drought
; linear inverse model
; operational forecast
; southern Peru precipitation
; statistical forecast
; tendency model
Scopus关键词: Atmospheric pressure
; Climatology
; Drought
; Nickel
; Oceanography
; Orthogonal functions
; Principal component analysis
; Regression analysis
; Surface waters
; Water resources
; Weather forecasting
; Empirical Orthogonal Function
; Interannual variability
; Linear inverse models
; Operational forecasts
; Precipitation forecast
; Precipitation mechanism
; Sea surface temperature (SST)
; Water resource planning
; Forecasting
英文摘要: Southern Peru receives over 60% of its annual climatological precipitation during the short period of January–March. This rainy season precipitation exhibits strong inter-annual and decadal variability, including severe drought events that incur devastating societal impacts and cause agricultural communities and mining facilities to compete for limited water resources. Improving existing seasonal prediction models of summertime precipitation could aid in water resource planning and allocation across this water-limited region. While various underlying mechanisms modulating inter-annual variability have been proposed by past studies, operational forecasts continue to be largely based on rudimentary El Niño-Southern Oscillation (ENSO)-based indices, such as Niño3.4, justifying further exploration of predictive skill. To bridge the gap between understanding precipitation mechanisms and operational forecasts, we perform systematic studies on the predictability and prediction skill of southern Peru's rainy season precipitation by constructing statistical forecast models using best available weather station and reanalysis data sets. We construct a simple regression model, based on the principal component (PC) tendency of tropical Pacific sea surface temperatures (SST), and a more advanced linear inverse model (LIM), based on the empirical orthogonal functions of tropical Pacific SST and large-scale atmospheric variables from reanalysis. Our results indicate that both the PC tendency and LIM models consistently outperform the ENSO-only based regression models in predicting precipitation at both the regional scale and for individual station, with improvements for individual stations ranging from 10 to over 200%. These encouraging results are likely to foster further development of operational precipitation forecasts for southern Peru. © 2018 Royal Meteorological Society Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116954
Appears in Collections: 气候减缓与适应
There are no files associated with this item.
作者单位: Nelson Institute Center for Climatic Research, University of Wisconsin-MadisonWI, United States; Department of Civil and Environmental Engineering, University of Wisconsin-MadisonWI, United States; Montgomery Associates Resource Solutions LLC, Cottage Grove, WI, United States; Southern Peru Copper Corporation, Santiago de Surco, Lima, Peru
Recommended Citation:
Wu S.,Notaro M.,Vavrus S.,et al. Efficacy of tendency and linear inverse models to predict southern Peru's rainy season precipitation[J]. International Journal of Climatology,2018-01-01,38(5)