融合气象与海洋数据，用深度学习提升厄尔尼诺预测精度和提前量
El Nino Prediction Based on Weather Forecast and Geographical Time-series Data
- 结合天气预报、海温异常与大气压数据，多源信息融合建模
- 采用CNN-LSTM混合结构，捕捉空间特征与时间演化规律
- 适合气候预测、防灾减灾领域研究人员参考
本文提出一种新框架，旨在提高厄尔尼诺事件的预测精度与提前时间，以减轻其对全球气候、经济和社会的影响。传统模型依赖海洋与大气指数，可能缺乏细粒度或动态相互作用的捕捉能力。本框架整合了实时全球天气预报数据、异常值、次表层海洋热含量及不同时空分辨率下的大气压力数据。利用卷积神经网络（CNN）提取空间特征，长短期记忆网络（LSTM）建模时间依赖性，构建混合深度学习架构，以识别厄尔尼诺事件的复杂前兆信号与演化模式。
原文摘要 · Abstract (English)
This paper proposes a novel framework for enhancing the prediction accuracy and lead time of El Niño events, crucial for mitigating their global climatic, economic, and societal impacts. Traditional prediction models often rely on oceanic and atmospheric indices, which may lack the granularity or dynamic interplay captured by comprehensive meteorological and geographical datasets. Our framework integrates real-time global weather forecast data with anomalies, subsurface ocean heat content, and atmospheric pressure across various temporal and spatial resolutions. Leveraging a hybrid deep learning architecture that combines a Convolutional Neural Network (CNN) for spatial feature extraction and a Long Short-Term Memory (LSTM) network for temporal dependency modeling, the framework aims to identify complex precursors and evolving patterns of El Niño events.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。