气候变化领域集成服务门户(CCPortal): Graph-guided regularized regression of pacific ocean climate variables to increase predictive skill of southwestern U.S. Winter precipitation

CCPortal

DOI	10.1175/JCLI-D-20-0079.1
	Graph-guided regularized regression of pacific ocean climate variables to increase predictive skill of southwestern U.S. Winter precipitation
	Stevens A.; Willett R.; Mamalakis A.; Foufoula-Georgiou E.; Tejedor A.; Randerson J.T.; Smyth P.; Wright S.
发表日期	2021
ISSN	0894-8755
起始页码	737
结束页码	754
卷号	34 期号:2
英文摘要	Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. Physics-based deterministic models show limited ability to predict precipitation as the lead time increases, due to imperfect representation of physical processes and incomplete knowledge of initial conditions. Similarly, statistical methods drawing upon established climate teleconnections have low prediction skill due to the complex nature of the climate system. Recently, promising data-driven approaches have been proposed, but they often suffer from overparameterization and overfitting due to the short observational record, and they often do not account for spatiotemporal dependencies among covariates (i.e., predictors such as sea surface temperatures). This study addresses these challenges via a predictive model based on a graph-guided regularizer that simultaneously promotes similarity of predictive weights for highly correlated covariates and enforces sparsity in the covariate domain. This approach both decreases the effective dimensionality of the problem and identifies the most predictive features without specifying them a priori. We use large ensemble simulations from a climate model to construct this regularizer, reducing the structural uncertainty in the estimation. We apply the learned model to predict winter precipitation in the southwestern United States using sea surface temperatures over the entire Pacific basin, and demonstrate its superiority compared to other regularization approaches and statistical models informed by known teleconnections. Our results highlight the potential to combine optimally the space-time structure of predictor variables learned from climate models with new graph-based regularizers to improve seasonal prediction. © 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
英文关键词	Atmospheric temperature; Forecasting; Graphic methods; Oceanography; Predictive analytics; Submarine geophysics; Surface properties; Surface waters; Environmental implications; Hydroclimatic variability; Overparameterization; Regularization approach; Sea surface temperature (SST); Spacetime structures; Spatio-temporal dependencies; Structural uncertainty; Climate models; climate modeling; climate prediction; graphical method; machine learning; precipitation (climatology); regression; seasonal variation; winter; Pacific Ocean; United States
语种	英语
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/170948
作者单位	Department of Statistics, University of Chicago, Chicago, IL, United States; Department of Computer Science, University of Chicago, Chicago, IL, United States; Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA, United States; Department of Earth System Science, University of California, Irvine, Irvine, CA, United States; Max Planck Institute for the Physics of Complex Systems, Dresden, Germany; Department of Computer Science, University of California, Irvine, Irvine, CA, United States; Department of Statistics, University of California, Irvine, Irvine, CA, United States; Computer Sciences Department, University of Wisconsin-Madison, Madison, WI, United States
推荐引用方式 GB/T 7714	Stevens A.,Willett R.,Mamalakis A.,et al. Graph-guided regularized regression of pacific ocean climate variables to increase predictive skill of southwestern U.S. Winter precipitation[J],2021,34(2).
APA	Stevens A..,Willett R..,Mamalakis A..,Foufoula-Georgiou E..,Tejedor A..,...&Wright S..(2021).Graph-guided regularized regression of pacific ocean climate variables to increase predictive skill of southwestern U.S. Winter precipitation.Journal of Climate,34(2).
MLA	Stevens A.,et al."Graph-guided regularized regression of pacific ocean climate variables to increase predictive skill of southwestern U.S. Winter precipitation".Journal of Climate 34.2(2021).