Improving Seasonal Prediction of California Winter Precipitation Using Canonical Correlation Analysis

Wang，Gaoyun1,2,3; Zhuang，Yizhou2; Fu，Rong2; Zhao，Siyu2; Wang，Hongqing1

2021-09-16

10.1029/2021JD034848

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES   影响因子和分区

2169-897X

2169-8996

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).

California winter rainfall canonical correlation analysis seasonal forecast statistical model

SCI

英语

通讯

China Scholarship Council (CSC)[201806010052] ; National Oceanic and Atmospheric Administration Climate Program Office (NOAA-CPO) Modeling, Analysis, Prediction and Projection (MAPP) Program[NA170AR4310123] ; California Department of Water Resources Grant[4600013129] ; China Special Fund for Meteorological Research in the Public Interest[GYHY201306047]

Meteorology & Atmospheric Sciences

Meteorology & Atmospheric Sciences

WOS:000694671900008

AMER GEOPHYSICAL UNION

GEOSCIENCES

2-s2.0-85114733852

Scopus

1.Department of Atmospheric and Oceanic Sciences,School of Physics,Peking University,Beijing,China
2.Department of Atmospheric and Oceanic Sciences,University of California,Los Angeles,Los Angeles,United States
3.The High School Affiliated to Southern University of Science and Technology,Shenzhen,China

Wang，Gaoyun,Zhuang，Yizhou,Fu，Rong,et al. Improving Seasonal Prediction of California Winter Precipitation Using Canonical Correlation Analysis[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2021,126(17).

Wang，Gaoyun,Zhuang，Yizhou,Fu，Rong,Zhao，Siyu,&Wang，Hongqing.(2021).Improving Seasonal Prediction of California Winter Precipitation Using Canonical Correlation Analysis.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,126(17).

Wang，Gaoyun,et al."Improving Seasonal Prediction of California Winter Precipitation Using Canonical Correlation Analysis".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 126.17(2021).