气候变化领域集成服务门户(CCPortal): Improving Seasonal Prediction of California Winter Precipitation Using Canonical Correlation Analysis

CCPortal

DOI	10.1029/2021JD034848
	Improving Seasonal Prediction of California Winter Precipitation Using Canonical Correlation Analysis
	Wang G.; Zhuang Y.; Fu R.; Zhao S.; Wang H.
发表日期	2021
ISSN	2169-897X
卷号	126 期号:17
英文摘要	We have developed a canonical correlation analysis (CCA) model for improving seasonal winter rainfall prediction. It uses the anomalies of sea surface temperature (SST), vertically integrated vapor transport (IVT), and geopotential height at 250 hPa (Z250) in October and November, respectively, as the predictors for winter rainfall prediction. These predictors represent the processes that influence winter rainfall over California as documented in the literature, but their potential for improving predictability was previously unclear. This statistical model shows prediction skills higher than those of the baseline autoregressive model, the CCA-based prediction model using only the SST anomalies, and the dynamic predictions by the North American Multi-Model Ensemble (NMME). Averaged over California, the Pearson correlation (R) is 0.64, root mean squared error (RMSE) is 0.65, and Heidke skill score (HSS) is 0.42 when the CCA-based model is initialized by the three predictor fields (SST, IVT, and Z250) in November. These skills are higher than those of the NMME predictions initialized in November (R, RMSE, and HSS are 0.30, 0.83, and 0.15, respectively) and those of the autoregressive baseline (R, RMSE, and HSS are 0.10, 0.79, and 0.08, respectively). Hindcasts of winter rainfall initialized by October observations show R, RMSE, and HSS of 0.53, 0.81, and 0.39, respectively, also higher than those of the NMME seasonal prediction initialized in October (0.32, 0.79, and 0.22 for R, RMSE, and HSS, respectively) and the autoregressive model (0.30, 0.75, and 0.16 for R, RMSE, and HSS, respectively). © 2021. American Geophysical Union. All Rights Reserved.
英文关键词	California winter rainfall; canonical correlation analysis; seasonal forecast; statistical model
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/237027
作者单位	Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China; Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, United States; The High School Affiliated to Southern University of Science and Technology, Shenzhen, China
推荐引用方式 GB/T 7714	Wang G.,Zhuang Y.,Fu R.,et al. Improving Seasonal Prediction of California Winter Precipitation Using Canonical Correlation Analysis[J],2021,126(17).
APA	Wang G.,Zhuang Y.,Fu R.,Zhao S.,&Wang H..(2021).Improving Seasonal Prediction of California Winter Precipitation Using Canonical Correlation Analysis.Journal of Geophysical Research: Atmospheres,126(17).
MLA	Wang G.,et al."Improving Seasonal Prediction of California Winter Precipitation Using Canonical Correlation Analysis".Journal of Geophysical Research: Atmospheres 126.17(2021).