气候变化领域集成服务门户(CCPortal): A monitoring and prediction system for compound dry and hot events

CCPortal

DOI	10.1088/1748-9326/ab4df5
	A monitoring and prediction system for compound dry and hot events
	Hao Z.; Hao F.; Xia Y.; Singh V.P.; Zhang X.
发表日期	2019
ISSN	17489318
卷号	14 期号:11
英文摘要	Compound dry and hot events (i.e. concurrent or consecutive occurrences of dry and hot events), which may cause larger impacts than those caused by extreme events occurring in isolation, have attracted wide attention in recent decades. Increased occurrences of compound dry and hot events in different regions around the globe highlight the importance of improved understanding and modeling of these events so that they can be tracked and predicted ahead of time. In this study, a monitoring and prediction system of compound dry and hot events at the global scale is introduced. The monitoring component consists of two indicators (standardized compound event indicator and a binary variable) that incorporate both dry and hot conditions for characterizing the severity and occurrence. The two indicators are shown to perform well in depicting compound dry and hot events during June-July-August 2010 in western Russia. The prediction component consists of two statistical models, including a conditional distribution model and a logistic regression model, for predicting compound dry and hot events based on El Niño-Southern Oscillation, which is shown to significantly affect compound events of several regions, including northern South America, southern Africa, southeast Asia, and Australia. These models are shown to perform well in predicting compound events in large regions (e.g. northern South America and southern Africa) during December-January-February 2015-2016. This monitoring and prediction system could be useful for providing early warning information of compound dry and hot events. © 2019 The Author(s). Published by IOP Publishing Ltd.
英文关键词	compound event; dry and hot; monitoring, prediction
语种	英语
scopus关键词	Atmospheric pressure; Forecasting; Logistic regression; Binary variables; compound event; Conditional distribution; Hot conditions; Logistic Regression modeling; Prediction systems; Southeast Asia; Southern oscillation; Monitoring; atmospheric feature; climate prediction; El Nino-Southern Oscillation; environmental modeling; environmental monitoring; global change; regression analysis; Australia; Russian Federation; South Africa; South America; Southeast Asia
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/154738
作者单位	College of Water Sciences, Beijing Normal University, Beijing, 100875, China; I.M. System Group at Environmental Modeling Center, National Centers for Environmental Prediction, College Park, MD, United States; Department of Biological and Agricultural Engineering and Zachry, Texas AandM University, College Station, TX 77843-2117, United States
推荐引用方式 GB/T 7714	Hao Z.,Hao F.,Xia Y.,et al. A monitoring and prediction system for compound dry and hot events[J],2019,14(11).
APA	Hao Z.,Hao F.,Xia Y.,Singh V.P.,&Zhang X..(2019).A monitoring and prediction system for compound dry and hot events.Environmental Research Letters,14(11).
MLA	Hao Z.,et al."A monitoring and prediction system for compound dry and hot events".Environmental Research Letters 14.11(2019).