Surface air temperature anomaly prediction over Japan using artificial neural networks

Venkata Ratnam Jayanthi, Masami Nonaka, Swadhin Behera

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-AS Atmospheric Sciences, Meteorology & Atmospheric Environment

[A-AS04] Machine Learning Techniques in Weather, Climate, Hydrology and Disease Predictions

Fri. Jun 4, 2021 3:30 PM - 5:00 PM Ch.10 (Zoom Room 10)

convener:Venkata Ratnam Jayanthi(Application Laboratory, JAMSTEC), Rajib Maity(Indian Institute of Technology Kharagpur), Swadhin Behera(Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001), Takeshi Doi(JAMSTEC), Chairperson:Pascal Oettli(Japan Agency for Marine-Earth Science and Technology), Venkata Ratnam Jayanthi(Application Laboratory, JAMSTEC), Takeshi Doi(JAMSTEC), Swadhin Behera(Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001)

4:45 PM - 5:00 PM

[AAS04-12] Surface air temperature anomaly prediction over Japan using artificial neural networks

*Venkata Ratnam Jayanthi¹, Masami Nonaka¹, Swadhin Behera¹ (1.Application Laboratory, JAMSTEC)

Keywords:Artificial Neural Network, NMME

Prediction of surface air temperature anomalies over Japan using the well-known technique of artificial neural networks (ANN) is carried out for the winter season (December, January and February). The period of study is from 1949/50 to 2019/20. The input attributes to the ANN model are derived using lag correlation analysis. The ANN predictions are compared with the predictions of the North American Multi-Model Ensemble (NMME) models. The results indicate the ANN to outperform the NMME predictions. The ANN predictions have higher anomaly correlation skill score along with higher hit rate and lower false alarm rates. The results indicate the ANN to be a promising tool to predict the winter temperature anomalies over Japan.