.png){kind=icon}
返回
Modeling Ocean Cooling Induced by Tropical Cyclone Wind Pump Using Explainable Machine Learning Framework
.png){kind=icon}
OA
AI
DOI：10.1109/TGRS.2024.3358374.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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).
Keyword:
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
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
Chronosequence and direct observation approaches reveal complementary community dynamics in a novel ecosystem时间序列和直接观察方法揭示了新颖生态系统中互补的群落动态.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model
PREAI
分享收藏EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimatesEN4: 质量控制的海洋温度和盐度剖面以及具有不确定性估计的每月客观分析
PREAI
分享收藏The ERA-Interim reanalysis: configuration and performance of the data assimilation systemERA-过渡再分析: 数据同化系统的配置和性能OAAI
OAAI分享收藏
查看更多内容