Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/joc.5494 |
Using all data to improve seasonal sea surface temperature predictions: A combination-based model forecast with unequal observation lengths | |
Khan, Mohammad Zaved Kaiser; Sharma, Ashish; Mehrotra, Rajeshwar | |
2018-06-30 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY |
ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2018 |
卷号 | 38期号:8页码:3215-3223 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia |
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. |
英文关键词 | model combination seasonal prediction sea surface temperature unequal data length |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000439793900001 |
WOS关键词 | RAINFALL FORECASTS ; GCM ENSEMBLES ; ENSO ; PRECIPITATION ; SKILL ; STATE |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/37074 |
专题 | 气候变化 |
作者单位 | Univ New South Wales, Sch Civil & Environm Engn, Sydney, NSW, Australia |
推荐引用方式 GB/T 7714 | Khan, Mohammad Zaved Kaiser,Sharma, Ashish,Mehrotra, Rajeshwar. Using all data to improve seasonal sea surface temperature predictions: A combination-based model forecast with unequal observation lengths[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2018,38(8):3215-3223. |
APA | Khan, Mohammad Zaved Kaiser,Sharma, Ashish,&Mehrotra, Rajeshwar.(2018).Using all data to improve seasonal sea surface temperature predictions: A combination-based model forecast with unequal observation lengths.INTERNATIONAL JOURNAL OF CLIMATOLOGY,38(8),3215-3223. |
MLA | Khan, Mohammad Zaved Kaiser,et al."Using all data to improve seasonal sea surface temperature predictions: A combination-based model forecast with unequal observation lengths".INTERNATIONAL JOURNAL OF CLIMATOLOGY 38.8(2018):3215-3223. |
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