El Nino Prediction Based on Weather Forecast and Geographical Time-series Data
This work addresses the need for better El Niño forecasts to mitigate global climatic, economic, and societal impacts, representing an incremental improvement over traditional models.
The paper tackled the problem of improving El Niño prediction accuracy and lead time by integrating weather forecast and geographical time-series data, achieving enhanced predictions through a hybrid CNN-LSTM architecture.
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.