Using Simple, Explainable Neural Networks to Predict the Madden‐Julian Oscillation

Journal of Advances in Modeling Earth Systems

Eric Maloney¹  Zane K. Martin¹  Elizabeth A. Barnes¹ 

[1] Department of Atmospheric Science Colorado State University Fort Collins CO USA;

关键词: Madden‐Julian oscillation;    subseasonal‐to‐seasonal prediction;    artificial neural network;    explainable artificial intelligence;    statistical modeling;    tropical meteorology;   

DOI  :  10.1029/2021MS002774

来源: DOAJ

【 摘 要 】

Abstract Few studies have utilized machine learning techniques to predict or understand the Madden‐Julian oscillation (MJO), a key source of subseasonal variability and predictability. Here, we present a simple framework for real‐time MJO prediction using shallow artificial neural networks (ANNs). We construct two ANN architectures, one deterministic and one probabilistic, that predict a real‐time MJO index using maps of tropical variables. These ANNs make skillful MJO predictions out to ∼18 days in October‐March and ∼11 days in April‐September, outperforming conventional linear models and efficiently capturing aspects of MJO predictability found in more complex, dynamical models. The flexibility and explainability of simple ANN frameworks are highlighted through varying model input and applying ANN explainability techniques that reveal sources and regions important for ANN prediction skill. The accessibility, performance, and efficiency of this simple machine learning framework is more broadly applicable to predict and understand other Earth system phenomena.

【 授权许可】

Unknown