Skillful prediction of Atlantic Niño index using machine learning models

Venkata Ratnam Jayanthi; Ingo Richter

講演情報

[E] ポスター発表

セッション記号 A (大気水圏科学) » A-TT 計測技術・研究手法

[A-TT35] Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions

2025年5月30日(金) 17:15 〜 19:15 ポスター会場 (幕張メッセ国際展示場 7・8ホール)

コンビーナ:Jayanthi Venkata Ratnam(Application Laboratory, JAMSTEC)、Martineau Patrick(Japan Agency for Marine-Earth Science and Technology)、土井威志(JAMSTEC)、Behera Swadhin(Climate Variation Predictability and Applicability Research Group, Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001)

17:15 〜 19:15

[ATT35-P04] Skillful prediction of Atlantic Niño index using machine learning models

*Venkata Ratnam Jayanthi¹、Ingo Richter¹ (1.Application Laboratory, JAMSTEC)

キーワード：ATL3, ANN

Several machine learning models were evaluated for their skill in predicting the June-Aug averaged Atlantic Niño index (ATL3) at long lead time of 1-6 months. The input attributes to the models were derived based on a lag correlation analysis between the observed ATL3 index and sea surface temperature, sea surface height, vertically averaged (0-100m) potential temperature. Of all the models evaluated, the model based on artificial neural networks (ANN) had better skill in predicting the ATL3 index at all the lead times of 1-6 months, with a correlation coefficient of about 0.6. It is found that the machine learning model based on ANN outperforms the state-of the art dynamical models in predicting the ATL3 index.