DOI: 10.1002/joc.5494
论文题名: Using all data to improve seasonal sea surface temperature predictions: A combination-based model forecast with unequal observation lengths
作者: Khan M.Z.K. ; Sharma A. ; Mehrotra R.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38, 期: 8 起始页码: 3215
结束页码: 3223
语种: 英语
英文关键词: model combination
; sea surface temperature
; seasonal prediction
; unequal data length
Scopus关键词: Atmospheric temperature
; Climatology
; Forecasting
; Oceanography
; Submarine geophysics
; Surface properties
; Surface waters
; Uncertainty analysis
; Additional datum
; Climate prediction
; Data length
; Model combination
; Model uncertainties
; Multi-model combination
; Sea surface temperature (SST)
; Seasonal prediction
; Climate models
; climate modeling
; climate prediction
; observational method
; sea surface temperature
; seasonality
; temporal analysis
; uncertainty analysis
英文摘要: One way to reduce model uncertainty in climate predictions is to combine forecasts from several models. Recent multi-model combination approaches combine model forecasts by pooling data for a time period, common across all the models, thus ignoring the additional data available or discarding altogether the models with the shorter time period. This results in the loss of some information which could otherwise be used while combining the models to possibly improve forecast skill. Our research explores this issue in the context of multi-model sea surface temperature (SST) models predictions and proposes a novel concept that allows a framework for combining models with unequal time period. Here, the unequal time periods imply different range of start and end dates of available model forecasts. A qualitative standpoint of our multi-model forecasting strategy is to reduce the uncertainty and improve the forecast skill. The utility of the approach is demonstrated by combining the global seasonal NDJ (November–January) SST predictions of two models and also as many as eight models, obtained using both equal and unequal time periods. The proposed approach shows improvement over 62–69% grid cells around the entire globe over the case when the common period of data length across the models is considered. © 2018 Royal Meteorological Society Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116868
Appears in Collections: 气候减缓与适应
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作者单位: School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, Australia
Recommended Citation:
Khan M.Z.K.,Sharma A.,Mehrotra R.. Using all data to improve seasonal sea surface temperature predictions: A combination-based model forecast with unequal observation lengths[J]. International Journal of Climatology,2018-01-01,38(8)