Full text

Turn on search term navigation

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Hail poses a significant meteorological hazard in China, leading to substantial economic and agricultural damage. To enhance the detection of hail and mitigate these impacts, this study presents an ensemble machine learning model (BPNN+Dtree) that combines a backpropagation neural network (BPNN) and a decision tree (Dtree). Using FY-4A satellite and ERA5 reanalysis data, the model is trained on geostationary satellite infrared data and environmental parameters, offering comprehensive, all-day, and large-area hail monitoring over China. The ReliefF method is employed to select 13 key features from 29 physical quantities, emphasizing cloud-top and thermodynamic properties over dynamic ones as input features for the model to enhance its hail differentiation capability. The BPNN+Dtree ensemble model harnesses the strengths of both algorithms, improving the probability of detection (POD) to 0.69 while maintaining a reasonable false alarm ratio (FAR) on the test set. Moreover, the model’s spatial distribution of hail probability more closely matches the observational data, outperforming the individual BPNN and Dtree models. Furthermore, it demonstrates improved regional applicability over overshooting top (OT)-based methods in the China region. The identified high-frequency hail areas correspond to the north-south movement of the monsoon rain belt and are consistent with the northeast-southwest belt distribution observed using microwave-based methods.

Details

Title
Detecting Hailstorms in China from FY-4A Satellite with an Ensemble Machine Learning Model
Author
Wu, Qiong 1 ; Yi-Xuan Shou 2 ; Yong-Guang Zheng 3 ; Wu, Fei 1 ; Chun-Yuan, Wang 1   VIAFID ORCID Logo 

 School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China; [email protected] (F.W.); [email protected] (C.-Y.W.) 
 National Satellite Meteorological Centre, China Meteorological Administration, Beijing 100081, China; [email protected]; Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration (LRCVES/CMA), Beijing 100081, China; FengYun Meteorological Satellite Innovation Center (FY-MSIC), China Meteorological Administration, Beijing 100081, China 
 National Meteorological Centre, Beijing 100081, China; [email protected] 
First page
3354
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3110689676
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.