Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1175/JCLI-D-18-0765.1 |
Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe | |
Kamarainen, Matti1; Uotila, Petteri2; Karpechko, Alexey Yu.3; Hyvarinen, Otto1; Lehtonen, Ilari1; Raisanen, Jouni2 | |
2019-09-01 | |
发表期刊 | JOURNAL OF CLIMATE
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ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2019 |
卷号 | 32期号:17页码:5363-5379 |
文章类型 | Article |
语种 | 英语 |
国家 | Finland |
英文摘要 | A statistical learning approach to produce seasonal temperature forecasts in western Europe and Scandinavia was implemented and tested. The leading principal components (PCs) of sea surface temperature (SST) and the geopotential at the 150-hPa level (GPT) were derived from reanalysis datasets and used at different lags (from one to five seasons) as predictors. Random sampling of both the fitting years and the potential predictors together with the Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to create a large ensemble of statistical models. Applying the models to independent test years shows that the ensemble performs well over the target areas and that the ensemble mean is more accurate than the best individual ensemble member on average. Skillful results were especially found for summer and fall, with the anomaly correlation coefficient values ranging between 0.41 and 0.68 for these seasons. The correct simulation of decadal trends, using sufficiently long time series for fitting (70 years), and the use of lagged predictors increased the prediction skill. The decadal-scale variability of SST, most importantly the Atlantic multidecadal oscillation (AMO), and different PCs of GPT are the most important individual predictors among all predictors. Both SST and GPT bring equally much predictive power, although their importance is different in different seasons. |
英文关键词 | Europe Principal components analysis Forecast verification skill Seasonal forecasting Statistical forecasting Decadal variability |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000477657300002 |
WOS关键词 | NORTH-ATLANTIC OSCILLATION ; EURASIAN SNOW COVER ; SEA-ICE ; SUMMER TEMPERATURE ; CLIMATE ; PREDICTION ; WINTER ; VARIABILITY ; WEATHER ; MODES |
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/186746 |
专题 | 气候变化 |
作者单位 | 1.Finnish Meteorol Inst, Weather & Climate Change Impact Res, Helsinki, Finland; 2.Univ Helsinki, Inst Atmospher & Earth Syst Res, Helsinki, Finland; 3.Finnish Meteorol Inst, Meteorol Res, Helsinki, Finland |
推荐引用方式 GB/T 7714 | Kamarainen, Matti,Uotila, Petteri,Karpechko, Alexey Yu.,et al. Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe[J]. JOURNAL OF CLIMATE,2019,32(17):5363-5379. |
APA | Kamarainen, Matti,Uotila, Petteri,Karpechko, Alexey Yu.,Hyvarinen, Otto,Lehtonen, Ilari,&Raisanen, Jouni.(2019).Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe.JOURNAL OF CLIMATE,32(17),5363-5379. |
MLA | Kamarainen, Matti,et al."Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe".JOURNAL OF CLIMATE 32.17(2019):5363-5379. |
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