Zhao Siyu, Fu Rong, Anderson Michael L, Chakraborty Sudip, Jiang Jonathan H, Su Hui, Gu Yu
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA USA.
California Department of Water Resources, Sacramento, CA USA.
Clim Dyn. 2023;60(5-6):1815-1829. doi: 10.1007/s00382-022-06422-x. Epub 2022 Jul 21.
This study provides extended seasonal predictions for the Upper Colorado River Basin (UCRB) precipitation in boreal spring using an artificial neural network (ANN) model and a stepwise linear regression model, respectively. Sea surface temperature (SST) predictors are developed taking advantage of the correlation between the precipitation and SST over three ocean basins. The extratropical North Pacific has a higher correlation with the UCRB spring precipitation than the tropical Pacific and North Atlantic. For the ANN model, the Pearson correlation coefficient between the observed and predicted precipitation exceeds 0.45 (-value < 0.01) for a lead time of 12 months. The mean absolute percentage error (MAPE) is below 20% and the Heidke skill score (HSS) is above 50%. Such long-lead prediction skill is probably due to the UCRB soil moisture bridging the SST and precipitation. The stepwise linear regression model shows similar prediction skills to those of ANN. Both models show prediction skills superior to those of an autoregression model (correlation < 0.10) that represents the baseline prediction skill and those of three of the North American Multi-Model Ensemble (NMME) forecast models. The three NMME models exhibit different skills in predicting the precipitation, with the best skills of the correlation ~ 0.40, MAPE < 25%, and HSS > 40% for lead times less than 8 months. This study highlights the advantage of oceanic climate signals in extended seasonal predictions for the UCRB spring precipitation and supports the improvement of the UCRB streamflow prediction and related water resource decisions.
The online version contains supplementary material available at 10.1007/s00382-022-06422-x.
本研究分别使用人工神经网络（ANN）模型和逐步线性回归模型，对北半球春季科罗拉多河上游流域（UCRB）的降水进行了扩展季节预测。利用三个海洋盆地降水与海表温度（SST）之间的相关性开发了海表温度预测因子。与热带太平洋和北大西洋相比，温带北太平洋与UCRB春季降水的相关性更高。对于ANN模型，在提前12个月的情况下，观测降水与预测降水之间的皮尔逊相关系数超过0.45（p值<0.01）。平均绝对百分比误差（MAPE）低于20%，海德克技巧得分（HSS）高于50%。这种长时间提前预测技巧可能是由于UCRB土壤湿度在SST和降水之间起到了桥梁作用。逐步线性回归模型显示出与ANN模型相似的预测技巧。两种模型的预测技巧均优于代表基线预测技巧的自回归模型（相关性<0.10）以及北美多模式集合（NMME）的三个预测模型。这三个NMME模型在预测降水方面表现出不同的技巧，在提前时间小于8个月时，最佳技巧的相关性约为0.40，MAPE<25%，HSS>40%。本研究突出了海洋气候信号在UCRB春季降水扩展季节预测中的优势，并支持改进UCRB径流预测和相关水资源决策。
在线版本包含可在10.1007/s00382-022-06422-x获取的补充材料。
