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

Hongxing Cui, Danling Tang*, Huizeng Liu, Hongbin Liu, Yi Sui, Yangchen Lai, Xiaowei Gu

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    3 Citations (Scopus)

    Abstract

    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).

    Original languageEnglish
    Article number4202317
    Number of pages17
    JournalIEEE Transactions on Geoscience and Remote Sensing
    Volume62
    DOIs
    Publication statusPublished - 25 Jan 2024

    Scopus Subject Areas

    • Electrical and Electronic Engineering
    • General Earth and Planetary Sciences

    User-Defined Keywords

    • Explainable machine learning
    • sea surface temperature cooling (SSTC)
    • tropical cyclone (TC)
    • wind pump

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