气候变化领域集成服务门户(CCPortal): Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

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DOI	10.1007/s00382-019-04640-4
	Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset
	Manzanas R.; Gutiérrez J.M.; Bhend J.; Hemri S.; Doblas-Reyes F.J.; Torralba V.; Penabad E.; Brookshaw A.
发表日期	2019
ISSN	0930-7575
起始页码	1287
结束页码	1305
卷号	53 期号:2020-03-04
英文摘要	This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)—e.g. quantile mapping—to more sophisticated ensemble recalibration (RC) methods—e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value—with respect to the raw model outputs—beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Bias adjustment; C3S; Ensemble recalibration; Ensemble size; Forecast quality; Hindcast length; Reliability; Seasonal forecasting
语种	英语
scopus关键词	climate modeling; data set; ensemble forecasting; hindcasting; regression analysis; reliability analysis; seasonal variation
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/146148
作者单位	Meteorology Group, Institute of Physics of Cantabria (IFCA), CSIC-University of Cantabria, Santander, 39005, Spain; Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland; Barcelona Supercomputing Center (BSC), Barcelona, Spain; ICREA, Pg. Lluís Companys, Barcelona, 23 08010, Spain; European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom
推荐引用方式 GB/T 7714	Manzanas R.,Gutiérrez J.M.,Bhend J.,et al. Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset[J],2019,53(2020-03-04).
APA	Manzanas R..,Gutiérrez J.M..,Bhend J..,Hemri S..,Doblas-Reyes F.J..,...&Brookshaw A..(2019).Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset.Climate Dynamics,53(2020-03-04).
MLA	Manzanas R.,et al."Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset".Climate Dynamics 53.2020-03-04(2019).