TY - JOUR
T1 - Modeling Ocean Cooling Induced by Tropical Cyclone Wind Pump Using Explainable Machine Learning Framework
AU - Cui, Hongxing
AU - Tang, Danling
AU - Liu, Huizeng
AU - Liu, Hongbin
AU - Sui, Yi
AU - Lai, Yangchen
AU - Gu, Xiaowei
N1 - This work was supported in part by the Key Special Supporting Talent Team Project of Guangdong under Grant 2019BT02H594, in part by PI Project of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) under Grant GML2021GD0810, in part by the Major Project of National Social Science Foundation of China under Grant 21ZDA097, and in part by the Remote Sensing Monitoring Study of Atmospheric Methane in the Northern South China Sea under Grant 2022ZD003.
Publisher Copyright:
© 2024 IEEE.
PY - 2024/1/25
Y1 - 2024/1/25
N2 - 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).
AB - 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).
KW - Explainable machine learning
KW - sea surface temperature cooling (SSTC)
KW - tropical cyclone (TC)
KW - wind pump
UR - http://www.scopus.com/inward/record.url?scp=85183940736&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2024.3358374
DO - 10.1109/TGRS.2024.3358374
M3 - Journal article
AN - SCOPUS:85183940736
SN - 0196-2892
VL - 62
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
M1 - 4202317
ER -