Detection of convective initiation (CI) is an important step of early warning of strong convective weather. This study proposes a machine learning approach for CI detection using 18 interesting fields extracted from weather radar, Himawari-8 Advanced Himawari Imager (AHI), and the variational Doppler radar analysis system (VDRAS). The collected features are used to train the machine learning model for CI detection. Therein, the support vector machine (SVM) is used to identify the CI and non-CI. It is concluded that a better result can be achieved by using multiple-source data compared to using satellite data only. The atmospheric boundary layer thermal dynamic information retrieved by VDRAS is proved to be useful for CI detection.
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A Machine Learning Approach for Convective Initiation Detection Using Multi-source Data
Semantic Scholar · Environmental Science · 2022
Abstract
Detection of convective initiation (CI) is an important step of early warning of strong convective weather. This study proposes a machine learning approach for CI detection using 18 interesting fields extracted from weather radar, Himawari-8 Advanced Himawari Imager (AHI), and the variational Doppler radar analysis system (VDRAS). The collected features are used to train the machine learning model for CI detection. Therein, the support vector machine (SVM) is used to identify the CI and non-CI. It is concluded that a better result can be achieved by using multiple-source data compared to using satellite data only. The atmospheric boundary layer thermal dynamic information retrieved by VDRAS is proved to be useful for CI detection.