Understanding and Predicting Sea Surface Temperature Responses to Tropical Cyclone Wind Pump Using Explainable Machine Learning

This study develops a predictive model for the sea surface temperature (SST) cooling generated by tropical cyclones (TCs) in the northwest Pacific (NWP), leveraging machine learning techniques such as random forest and extreme gradient boosting (XGBoost). By incorporating 12 predictors related to the characteristics of TCs and the pre-storm ocean states, the model adeptly predicts the spatial structure and temporal evolutions of SST cooling and surpasses a conventional numerical model in accuracy. Important predictors include TC intensity, speed, size, and pre-storm ocean conditions like mixed layer depth and SST. These predictors were identified through feature importance assessments and SHapely Additive exPlanations (SHAP), which contribute to attribute-oriented explainability in the proposed method. The model has the ability to accurately simulate the spatial structure and temporal evolution of SST cooling for different TC intensities, along with its explanatory power regarding the ocean-atmosphere interactions during TCs. This makes the model an insightful tool for analyzing the complex responses of oceanic conditions to TC wind pump effects.

Primary Presenter: Hongxing Cui, Guangdong Remote Sensing Center for Marine Ecology and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou); Hong Kong University of Science and Technology (hcuiaf@connect.ust.hk)

Authors:

Hongxing Cui, Guangdong Remote Sensing Center for Marine Ecology and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou); Hong Kong University of Science and Technology  (hcuiaf@connect.ust.hk)

Dangling Tang, Guangdong Remote Sensing Center for Marine Ecology and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou); Hong Kong University of Science and Technology  (lingzistdl@126.com)

Hongbin Liu, Hong Kong University of Science and Technology; Guangdong Remote Sensing Center for Marine Ecology and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)  (liuhb@ust.hk)