DOI: 10.1002/joc.5366
论文题名: Improved seasonal prediction skill of rainfall for the Primera season in Central America
作者: Alfaro E.J. ; Chourio X. ; Muñoz Á.G. ; Mason S.J.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38 起始页码: e255
结束页码: e268
语种: 英语
英文关键词: canonical correlation analysis
; Central America
; MOS predictive schemes
; precipitation
; seasonal climate prediction
; statistical models
Scopus关键词: Climatology
; Correlation methods
; Forecasting
; Potential energy
; Precipitation (chemical)
; Precipitation (meteorology)
; Rain
; Uncertainty analysis
; Canonical correlation analysis
; Central America
; Convective available potential energies
; Hydrological services
; Model output statistics
; Rainfall characteristics
; Seasonal climate prediction
; Seasonal prediction
; Climate models
; canonical analysis
; climate modeling
; climate prediction
; convective system
; correlation
; potential energy
; rainfall
; seasonality
; weather forecasting
; Central America
英文摘要: This study explores the predictive skill of seasonal rainfall characteristics for the first rainy (and planting) season, May–June, in Central America. Statistical predictive models were built using a Model Output Statistics (MOS) technique based on canonical correlation analysis, in which variables that forecast with the Climate Forecast System version 2 (CFSv2) were used as candidate predictors for the observed total precipitation, frequency of rainy days and mean number of extremely dry and wet events in the season. CFSv2 initializations from February to April were explored. The CFSv2 variables used in the study consist of rainfall, as in a typical MOS technique, and a combination of low-level winds and convective available potential energy (CAPE), a blend that has been previously shown to be a good predictor for convective activity. The highest predictive skill was found for the seasonal frequency of rainy days, followed by the mean frequency of dry events. In terms of candidate predictors, the zonal transport of CAPE (uCAPE) at 925 hPa offers higher skill across Central America than rainfall, which is attributed in part to the high model uncertainties associated with precipitation in the region. As expected, dynamical model predictors initialized in February provide lower skill than those initialized later. Nonetheless, the skill is comparable for March and April initializations. These results suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May–June rainfall characteristics than previously stated. © 2017 Royal Meteorological Society Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/117020
Appears in Collections: 气候减缓与适应
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作者单位: Center for Geophysical Research, University of Costa Rica, San Pedro, Costa Rica; School of Physics, University of Costa Rica, San Pedro, Costa Rica; Center for Research in Marine Sciences and Limnology, University of Costa Rica, San Pedro, Costa Rica; Observatorio Latinoamericano de Eventos Extraordinarios, Centro de Modelado Científico, Universidad del Zulia, Maracaibo, Venezuela; Atmospheric and Oceanic Sciences, Princeton UniversityNJ, United States; International Research Institute for Climate and Society, The Earth Institute, Columbia University, New York, NY, United States
Recommended Citation:
Alfaro E.J.,Chourio X.,Muñoz Á.G.,et al. Improved seasonal prediction skill of rainfall for the Primera season in Central America[J]. International Journal of Climatology,2018-01-01,38