Prediction of the summertime Northwest Pacific subtropical high based on ConvLSTM

The Northwest Pacific subtropical high (NWPSH) significantly affects East Asian weather and climate, rendering the prediction of its intensity and location critically important. This study aims to evaluate the performance of the Convolutional Long and Short-Term Memory (ConvLSTM) model for predicting the summertime 500 hPa geopotential height and NWPSH intensity and area at a lead time of three months, and to compare it with the dynamical models of the Nanjing University of Information Science and Technology Climate Forecast System (NUIST-CFS1.0) and the Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2). The mean latitude-weighted RMSE (RMSE w ), anomaly correlation coefficient (ACC), and NWPSH indices are used as evaluation metrics. For both summer mean and monthly prediction, the ConvLSTM model outperforms the two dynamical models in terms of RMSE w and ACC for the 500 hPa geopotential height over the western Pacific region. The correlation coefficients between the NWPSH intensity index predicted by the ConvLSTM model and the observations are higher than those obtained from the two dynamical models. Regarding the NWPSH area index, the ConvLSTM model shows more stable performance. Particularly in August, the improvement of the ConvLSTM model compared to the two dynamical models is more significant, indicating the robust capability in capturing late-summer circulation patterns. Therefore, the ConvLSTM model demonstrates significant potential for summer NWPSH prediction, offering a new perspective and approach for climate prediction in this region. 西北太平洋副热带高压 (NWPSH) 对东亚天气和气候具有重要影响, 其强度和位置的预测至关重要.本研究旨在评估卷积长短期记忆 (ConvLSTM) 模型提前3个月对夏季500 hPa位势高度及NWPSH强度和面积的预测性能, 并将其与动力模式南京信息工程大学气候预测系统1.0版 (NUIST-CFS1.0) 和加拿大季节–年际预测系统第2版 (CanSIPSv2) 预测结果对比.评估指标为纬度加权均方根误差 (RMSEw) , 异常相关系数 (ACC) 和NWPSH指数.在夏季平均和逐月预测中, ConvLSTM模型对西太平洋地区500 hPa位势高度的RMSEw和ACC预测技巧均优于动力模式.ConvLSTM模型预测的NWPSH强度指数与观测值的相关系数高于动力模式结果, 预测的NWPSH面积指数也表现出更稳定的性能.与两个动力学模式相比, ConvLSTM模型在8月的改进更为显著, 表明其对夏季末期环流形势具有更强的捕捉能力.因此, ConvLSTM模型在夏季NWPSH的预测中具有较好的应用潜力, 为该区域的气候预测提供了新的视角和方法.