DOI: 10.1175/JCLI-D-14-00452.1
Scopus记录号: 2-s2.0-84957824227
论文题名: Exploring the predictability of 30-day extreme precipitation occurrence using a global SST-SLP correlation network
作者: Lu M. ; Lall U. ; Kawale J. ; Liess S. ; Kumar V.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2016
卷: 29, 期: 3 起始页码: 1013
结束页码: 1029
语种: 英语
Scopus关键词: Atmospheric movements
; Atmospheric pressure
; Atmospheric structure
; Atmospheric temperature
; Climatology
; Clustering algorithms
; Financial data processing
; Forecasting
; Graphic methods
; Meteorology
; Oceanography
; Precipitation (meteorology)
; Regression analysis
; Sea level
; Surface waters
; Atmospheric circulation
; Extreme events
; Principal components analysis
; Short-range predictions
; Statistical techniques
; Principal component analysis
; extreme event
; precipitation (climatology)
; prediction
; principal component analysis
; sea level pressure
; sea surface temperature
; weather forecasting
英文摘要: Correlation networks identified from financial, genomic, ecological, epidemiological, social, and climatic data are being used to provide useful topological insights into the structure of high-dimensional data. Strong convection over the oceans and the atmospheric moisture transport and flow convergence indicated by atmospheric pressure fieldsmay determine where and when extreme precipitation occurs. Here, the spatiotemporal relationship among sea surface temperature (SST), sea level pressure (SLP), and extreme global precipitation is explored using a graph-based approach that uses the concept of reciprocity to generate cluster pairs of locations with similar spatiotemporal patterns at any time lag. A global time-lagged relationship between pentad SST anomalies and pentad SLP anomalies is investigated to understand the linkages and influence of the slowly changing oceanic boundary conditions on the development of the global atmospheric circulation. This study explores the use of this correlation network to predict extreme precipitation globally over the next 30 days, using a logistic principal component regression on the strong global dipoles found between SST and SLP. Predictive skill under cross validation and blind prediction for the occurrence of 30-day precipitation that is higher than the 90th percentile of days in the wet season is indicated for the selected global regions considered. © 2016 American Meteorological Society. 资助项目: NOAA, National Oceanic and Atmospheric Administration
; NOAA, National Oceanic and Atmospheric Administration
; NOAA, National Oceanic and Atmospheric Administration
; NRF, National Oceanic and Atmospheric Administration
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50065
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Earth and Environmental Engineering, Columbia Water Center, Columbia University, New York, NY, United States; Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, United States; Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN, United States
Recommended Citation:
Lu M.,Lall U.,Kawale J.,et al. Exploring the predictability of 30-day extreme precipitation occurrence using a global SST-SLP correlation network[J]. Journal of Climate,2016-01-01,29(3)