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Modeling Ocean Cooling Induced by Tropical Cyclone Wind Pump Using Explainable Machine Learning Framework

delete2024-01-01
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OA
AI
H
Hongxing Cui
D
Danling Tang *
H
Huizeng Liu
H
Hongbin Liu
Y
Yi Sui
Y
Yangchen Lai
X
Xiaowei Gu
DOI:10.1109/TGRS.2024.3358374delete
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Abstract

Abstract

En 中文
Tropical cyclones (TCs), with an intensive wind pump impact, induce sea surface temperature cooling (SSTC) on the upper ocean. SSTC is a pronounced indicator to reveal TC evolution and oceanic conditions. However, there are few effective methods for accurately approximating the amplitude of the spatial structure of TC-induced SSTC. This study proposes a novel explainable machine learning framework to model and interpret the amplitude of the spatial structure of SSTC over the northwest Pacific (NWP). In particular, 12 predictors related to TC characteristics and pre-storm ocean states are considered as inputs. A composite analysis technique is used to characterize the amplitude of the spatial structure of SSTC across the TC track. Extreme gradient boosting (XGBoost) is utilized to predict the amplitude of SSTC from the 12 predictors. To better interpret the ocean-atmosphere interaction, a SHapely Additive explanations (SHAP) method is further employed to identify the contributions of predictors in determining the amplitude of the TC-induced SSTC, bringing the attribute-oriented explainability to the proposed method. The results showed that the proposed method could accurately predict the amplitude of the spatial structure of SSTC for different TC intensity groups and outperforms a numerical model. The proposed method also serves as an effective tool for reconstructing composite maps of both interannual and seasonal evolutions of SSTC spatial structure. The study offers insight into applying machine learning to model and interpret the responses of oceanic conditions triggered by extreme weather conditions (e.g., TCs).
Keywords:
Oceans
Biological system modeling
Atmospheric modeling
Sea surface
Machine learning
Spatial resolution
Predictive models
Explainable machine learning
sea surface temperature cooling (SSTC)
tropical cyclone (TC)
wind pump

Journal

IEEE Transactions on Geoscience and Remote Sensing cover
IEEE Transactions on Geoscience and Remote Sensing
IF:
8.6
Papers:
2.1W
Citations:
10.7W

Organization

H
Hong Kong Baptist University
Scholars:
6.3K
Papers: 7.5K
Citations: 1.3W
S
D
Dalhousie University
Scholars:
2.0W
Papers: 1.8W
Citations: 2.3W
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