Predicting maximum temperatures over India 10-days ahead using machine learning models.
J V Ratnam, Swadhin K Behera, Masami Nonaka, Patrick Martineau, Kalpesh R Patil
Author Information
J V Ratnam: Application Laboratory, VAIG, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-Ku, Yokohama, Kanagawa, 236-0001, Japan. jvratnam@jamstec.go.jp.
Swadhin K Behera: Application Laboratory, VAIG, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-Ku, Yokohama, Kanagawa, 236-0001, Japan.
Masami Nonaka: Application Laboratory, VAIG, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-Ku, Yokohama, Kanagawa, 236-0001, Japan.
Patrick Martineau: Application Laboratory, VAIG, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-Ku, Yokohama, Kanagawa, 236-0001, Japan.
Kalpesh R Patil: Application Laboratory, VAIG, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-Ku, Yokohama, Kanagawa, 236-0001, Japan.
In the months of March-June, India experiences high daytime temperatures (Tmax), which sometimes lead to heatwave-like conditions over India. In this study, 10 different machine learning models are evaluated for their ability to predict the daily Tmax anomalies 10 days ahead in the months of March-June. Several model experiments were carried out to identify an optimal model to predict daily Tmax anomalies over India. The results indicate that the AdaBoost regressor with Multi-layer Perceptron as the base estimator is an optimal model to predict the Tmax anomalies over India in the months of March-June. The optimal model predictions are benchmarked against 10-day persistence predictions and the predictions from the Climate Forecast System (CFS) reforecast. The results indicate that the machine learning model skill is higher than persistence and comparable to CFS reforecast 10-day predictions in April and May. In March and June, the machine learning models have low skill scores and perform no better than persistence. These results indicate that the machine learning models are promising tools to predict the surface air maximum temperature anomalies over India in April and May and can complement predictions from more sophisticated numerical models.
