El Nino Prediction Based on Weather Forecast and Geographical Time-series Data

TL;DR

This paper introduces a hybrid deep learning framework combining CNN and LSTM to improve El Niño prediction accuracy and lead time by integrating diverse meteorological and geographical data.

Contribution

It presents a novel approach that leverages real-time weather forecasts and complex data integration for more effective El Niño prediction.

Findings

Enhanced prediction accuracy demonstrated over traditional models

Increased lead time for early El Niño detection

Effective identification of complex precursors and patterns

Abstract

This paper proposes a novel framework for enhancing the prediction accuracy and lead time of El Ni\~no 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 Ni\~no events.