DOI: 10.1175/JCLI-D-17-0805.1
Scopus记录号: 2-s2.0-85052818754
论文题名: Heavy rainfall in paraguay during the 2015/16 austral summer: Causes and subseasonal-to-seasonal predictive skill
作者: Doss-Gollin J. ; Muñoz A.G. ; Mason S.J. ; Pastén M.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2018
卷: 31, 期: 17 起始页码: 6669
结束页码: 6695
语种: 英语
英文关键词: Atmosphere
; Flood events
; Forecast verification/skill
; South America
; Statistical forecasting
; Statistical techniques
Scopus关键词: Climatology
; Decision making
; Earth atmosphere
; Floods
; Forecasting
; Oceanography
; Principal component analysis
; Wind effects
; Flood event
; Forecast verification/skill
; South America
; Statistical forecasting
; Statistical techniques
; Rain
英文摘要: During the austral summer 2015/16, severe flooding displaced over 170 000 people on the Paraguay River system in Paraguay, Argentina, and southern Brazil. These floods were driven by repeated heavy rainfall events in the lower Paraguay River basin. Alternating sequences of enhanced moisture inflow from the South American low-level jet and local convergence associated with baroclinic systems were conducive to mesoscale convective activity and enhanced precipitation. These circulation patterns were favored by cross-time-scale interactions of a very strong El Niño event, an unusually persistent Madden-Julian oscillation in phases 4 and 5, and the presence of a dipole SST anomaly in the central southern Atlantic Ocean. The simultaneous use of seasonal and subseasonal heavy rainfall predictions could have provided decision-makers with useful information about the start of these flooding events from two to four weeks in advance. Probabilistic seasonal forecasts available at the beginning of November successfully indicated heightened probability of heavy rainfall (90th percentile) over southern Paraguay and Brazil for December-February. Raw subseasonal forecasts of heavy rainfall exhibited limited skill at lead times beyond the first two predicted weeks, but a model output statistics approach involving principal component regression substantially improved the spatial distribution of skill for week 3 relative to other methods tested, including extended logistic regressions. A continuous monitoring of climate drivers impacting rainfall in the region, and the use of statistically corrected heavy precipitation seasonal and subseasonal forecasts, may help improve flood preparedness in this and other regions. © 2018 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/110705
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: Columbia Water Center, Columbia University, New York, NY, United States; Department of Earth and Environmental Engineering, Columbia University, New York, NY, United States; Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, United States; International Research Institute for Climate and Society, The Earth Institute, Columbia University, Palisades, NY, United States; Dirección de Meteorología e Hidrología, Asunción, Paraguay; Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo, Paraguay
Recommended Citation:
Doss-Gollin J.,Muñoz A.G.,Mason S.J.,et al. Heavy rainfall in paraguay during the 2015/16 austral summer: Causes and subseasonal-to-seasonal predictive skill[J]. Journal of Climate,2018-01-01,31(17)