Abstract
The magnitude of damage caused by hail depends on its size; however, direct observation or indirect estimation of hail size remains a significant challenge. One primary reason for estimations by proxy, such as through remote sensing methods, is that empirical relationships or statistical models established in one region may not apply to other areas. This study employs a machine learning method to build a hail size estimation model without assuming relations in advance. It uses FY-4A AGRI data to provide cloud-top information and ERA5 data to add vertical environment information. Before training the model, we conducted a principal component analysis (PCA) to analyze the highly influential factors on hail sizes. A total of 18 features, composed of four groups, namely brightness temperature (BT), the difference in BT (BTD), thermodynamics, and dynamics groups, were chosen from 29 original features. Dynamic and BTD features show superior performance in identifying large hail. Although the selected features are more closely correlated to hail sizes than unselected ones, the relationships are complicated and nonlinear. As a result, a two-layer regression back propagation neural network (BPNN) model with powerful fitting ability is trained with selected features to predict maximum hail diameter (MHD). The linear fitting R2 between predicted and observed MHDs is 0.52 on the test set, which signifies that our model performs well compared with other hail size estimation models. We also examine the model concerning all three hail cases in Shanghai, China, between 2019 and 2021. The model attained more satisfactory results than the radar-based maximum estimated hail size (MEHS) method, which overestimates the MHDs, thus further supporting the operational applications of our model.
Highlights
Hailstorms are severe convective weather systems commonly seen in the spring and summer
One reason is that the relationship between maximum hail diameter (MHD) and input variables is nonlinear; the neural network model is suitable for solving complex regression problems
(1:10 m). (b) The histogram shows the frequency distribution of hail events with different MHDs
Summary
Hailstorms are severe convective weather systems commonly seen in the spring and summer. The new generation of geostationary meteorological satellites launched in recent years, such as the Himawari-8, GOES-R, FY-4A/B, etc., has significantly improved our capacity to monitor rapidly developing convective clouds with higher temporal-spatial resolution and multiplied channels [28,29,30,31,32] They have been successfully applied to estimate rainfall rate or typhoon intensity [33,34,35,36,37]. One reason is that the relationship between MHD and input variables is nonlinear; the neural network model is suitable for solving complex regression problems Another reason is that the sample size is small, and the two-layer shallow neural network is sufficient for avoiding overfitting. We organize this paper as follows: Section 2 introduces the data and the model establishment process, including the introduction of the PCA and BPNN methods; Sections 3 and 4 show the results of feature selection and model evaluation, respectively; Section 5 gives the discussion; Section 6 provides the conclusion
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
