The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Ex...The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields.展开更多
基于 NCEP/NCAR 逐日再分析资料，应用小波分析方法研究讨论了西太平洋、南海、阿拉伯海等近赤道季风区 850 hPa 纬向风场低频振荡与 El Nino 发生发展的相关特征。研究发现，在 El Nino 发生前期，30～50 d 低频振荡会出现显著的增强，...基于 NCEP/NCAR 逐日再分析资料，应用小波分析方法研究讨论了西太平洋、南海、阿拉伯海等近赤道季风区 850 hPa 纬向风场低频振荡与 El Nino 发生发展的相关特征。研究发现，在 El Nino 发生前期，30～50 d 低频振荡会出现显著的增强，El Nino 发生后，30～50 d 低频振荡明显减小，120～150 d 准定常波动加强。分析表明，近赤道季风区显著低频振荡是 El Nino 发生发展的一个重要前兆，低频振荡能量向甚低频段的转移和输送可能是 El Nino 发生的一个重要机理。展开更多
文摘The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields.
文摘基于 NCEP/NCAR 逐日再分析资料，应用小波分析方法研究讨论了西太平洋、南海、阿拉伯海等近赤道季风区 850 hPa 纬向风场低频振荡与 El Nino 发生发展的相关特征。研究发现，在 El Nino 发生前期，30～50 d 低频振荡会出现显著的增强，El Nino 发生后，30～50 d 低频振荡明显减小，120～150 d 准定常波动加强。分析表明，近赤道季风区显著低频振荡是 El Nino 发生发展的一个重要前兆，低频振荡能量向甚低频段的转移和输送可能是 El Nino 发生的一个重要机理。
