气候变化领域集成服务门户(CCPortal): Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia

CCPortal

DOI	10.1088/1748-9326/ab9e98
	Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia
	Feng P.; Wang B.; Liu D.L.; Ji F.; Niu X.; Ruan H.; Shi L.; Yu Q.
发表日期	2020
ISSN	17489318
卷号	15 期号:8
英文摘要	Probabilistic seasonal rainfall forecasting is of great importance for stakeholders such as farmers and policymakers to assist in developing risk management strategies and to inform decisions. In practice, there are two kinds of commonly used tools, dynamical models and statistical models, to provide probabilistic seasonal rainfall forecasts. Dynamical models are based on physical processes but are usually expensive to operate and implement, and rely overly on initial conditions. Statistical models are easy to implement but are usually based on simple or linear relationships between observed variables. Recently, machine learning techniques have been widely used in climate projection and perform well in reproducing historical climate. For these reasons, we conducted a case study in Australia by developing a machine learning-based probabilistic seasonal rainfall forecasting model using multiple large-scale climate indices from the Pacific, Indian and Southern Oceans. Rainfall probabilities of exceeding the climatological median for upcoming seasons from 2011 to 2018 were successively forecasted using multiple climate indices of precedent six months. The performance of the model was evaluated by comparing it with an officially used forecasting model, the SOI (Southern Oscillation Index) phase model (SP) operated by Queensland government in Australia. Results indicated that the random forest (RF) model outperformed the SP model in terms of both distinct forecasts and forecasting accuracy. The RF model increased the percentages of distinct forecasts to 64.9% for spring, to 71.5% for summer, to 65.8% for autumn, and to 63.9% for winter, 1.4 ∼ 3.2 times of the values from the SP model. Forecasting accuracy was also greatly increased by 28%, 167%, 219%, and 76% for four seasons respectively, compared to the SP model. The proposed rainfall forecasting model is based on readily available data, and we believe it can be easily extended to other regions to provide seasonal rainfall outlooks. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	climate drivers; random forest; seasonal rainfall forecasting
语种	英语
scopus关键词	Atmospheric pressure; Decision trees; Machine learning; Rain; Risk management; Turing machines; Weather forecasting; Forecasting accuracy; Forecasting modeling; Initial conditions; Linear relationships; Machine learning techniques; Rainfall forecasting; Risk management strategies; Southern oscillation index; Climate models; climate effect; climate prediction; forecasting method; integrated approach; machine learning; probability; rainfall; seasonal variation; Australia
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153862
作者单位	State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest AandF University, Yangling, Shaanxi, 712100, China; NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia; Climate Change Research Centre, ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW 2052, Australia; Department of Planning, Industry and Environment, Queanbeyan, NSW 2620, Australia; College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan, 471000, China; Technology and Key Laboratory of Beibu Gulf Environment Change and Resources Use Utilization, Ministry of Education, Nanning Normal University, Nanning, 530001, China; School of Life Sciences, Faculty of Science, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia; College of Resources and Environment, University of Chinese Academy of Science, Beijing, 100049, China
推荐引用方式 GB/T 7714	Feng P.,Wang B.,Liu D.L.,et al. Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia[J],2020,15(8).
APA	Feng P..,Wang B..,Liu D.L..,Ji F..,Niu X..,...&Yu Q..(2020).Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia.Environmental Research Letters,15(8).
MLA	Feng P.,et al."Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia".Environmental Research Letters 15.8(2020).