DOI: 10.1175/2011JCLI4126.1
Scopus记录号: 2-s2.0-84856976125
论文题名: Improved seasonal precipitation forecasts for the Asian monsoon using 16 atmosphere-ocean coupled models. Part II: Anomaly
作者: Krishnamurti T.N. ; Kumar V.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2012
卷: 25, 期: 1 起始页码: 65
结束页码: 88
语种: 英语
Scopus关键词: Anomalies
; Asian monsoon
; Coupled models
; Cross validation
; Current modeling
; Data length
; Down-scaling
; Equitable threat score
; Model data
; Monsoon rainfall
; Monsoons
; Multi-model
; Multi-model superensemble
; Post processing
; Precipitation anomalies
; Probabilistic Skill
; Rain gauge networks
; Rainfall anomaly
; RMS errors
; Seasonal climate forecast
; Seasonal forecasting
; Seasonal precipitations
; Seasonal prediction
; Seasonal rainfall
; Skill metric
; Skill Score
; Spin-up
; Training phase
; Climatology
; Errors
; Precipitation (chemical)
; Rain
; Weather forecasting
; Atmospheric thermodynamics
; atmosphere-ocean coupling
; correlation
; ensemble forecasting
; error analysis
; monsoon
; numerical model
; precipitation (climatology)
; weather forecasting
英文摘要: This is the second part of a paper on the improved seasonal precipitation forecasts for the Asianmonsoon using 16 atmosphere-ocean coupled models. This study utilizes a large suite of coupled atmosphere-ocean models; this second part largely addresses the skill of rainfall anomaly forecasts. These include both deterministic and probabilistic skill measures such as the RMS errors, anomaly correlations, equitable threat scores, and the Brier skill score. It was possible to improve the skills of rainfall climatology from the use of a downscaled multimodel superensemble to very high levels, and it is of interest to ask how far this methodology would go toward improving the skills of seasonal rainfall anomaly forecasts. It is possible to go through a sequence of multimodel post processing to improve upon these skills by using a dense rain gauge network over Asia, downscaling forecasts for each member model, and constructing a multimodel superensemble that benefits from the persistence of errors of the member models. This paper addresses the spinup issues of the downscaling and the superensemble results where the number of years of model data needed for training phase, for the downscaling, and for the construction of the superensemble, is addressed. In the context of cross validation, the training phase includes 14 seasons ofmonsoon data. The forecast phase is only one season; it is this season that was not included in the training phase each time. The relationship between data length and the number of models needed for enhanced skills is another issue that is addressed. Seasonal climate forecasts over the larger monsoon Asia domain and over the regional belts are evaluated. The superensemble forecasts invariably have the highest skill compared to the member models globally and regionally. This is largely due to the presence of large systematic errors in models that carry low seasonal prediction skills. Such models carry persistent signatures of systematic errors, and their errors are recognized by the multimodel superensemble. The probabilistic skills show that the superensemble-based forecasts carry amuch higher reliability score compared to the member models. This implies that the superensemblebased forecasts are the most reliable among all the member models. It is possible to examine the performance of models and of the superensemble during periods of heavy monsoon rainfall versus those for deficient monsoon rainfall seasons. One of the conclusions of this study is that given the uncertainties in current modeling for seasonal rainfall forecasts, post processing ofmultimodel forecasts, using the superensemblemethodology, seems to provide the most promising results for the rainfall anomaly forecasts. These results are confirmed by an additional skill metric where the RMS errors and the correlations of forecast skills are evaluated using a normalized precipitation anomaly for the forecasts and the observed estimates. © 2012 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/52619
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Earth, Ocean and Atmospheric Science, The Florida State University, Tallahassee, FL, United States
Recommended Citation:
Krishnamurti T.N.,Kumar V.. Improved seasonal precipitation forecasts for the Asian monsoon using 16 atmosphere-ocean coupled models. Part II: Anomaly[J]. Journal of Climate,2012-01-01,25(1)