A Diagnostic Analysis of the 2024 Beijing May 30 Gale Simulation Based on Satellite Observation Products

A gale occurred in Beijing on 30 May 2024, which led to fallen trees and damaged infrastructure. This event was primarily driven by surface divergent winds induced by strong convective downdrafts. During the occurrence and development of this gale, solar shortwave radiation and cloud-related variables played a crucial role in triggering, sustaining, and organizing convection. This study proposes a new diagnostic analysis approach for this gale focusing on shortwave radiation and cloud-related variables involved in the physical processes of gale development, based on the FY-4B L2 products and simulations from the Mesoscale Weather Numerical Forecast System of the China Meteorological Administration (CMA-MESO). The diagnostic analysis results of this case show that before cloud formation, the CMA-MESO simulates stronger shortwave radiation heating in the initial stages, leading to an overestimation of surface temperature rise. Additionally, the simulated cloud formation occurs slightly later than observed, with reduced cloud coverage, shorter cloud duration, and lower cloud top heights, resulting in a weaker convective intensity compared to observations. Furthermore, the CMA-MESO underestimates the temperature gradient between the middle and lower troposphere and predicts lower convective instability, which leads to weaker forecasts of convection organization. Ultimately, this study provides a theoretical basis and technical support for enhancing the ability of the CMA-MESO to simulate this gale by using the FY-4B L2 data products for diagnostic analysis.

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Publication Details

Journal
Remote Sensing
Published
2025-04-12
DOI
https://doi.org/10.3390/rs17081378
Primary Topic
Safety and Risk Management
Type
article
Field-Weighted Citation Impact
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A Diagnostic Analysis of the 2024 Beijing May 30 Gale Simulation Based on Satellite Observation Products

Zhuoya Ni, Jianglin Hu, Chunqiang Wu, Qifeng Lu et al.
Remote Sensing
Safety and Risk Management
article

A Diagnostic Analysis of the 2024 Beijing May 30 Gale Simulation Based on Satellite Observation Products

Zhuoya Ni, Jianglin Hu, Chunqiang Wu, Qifeng Lu, Ruixia Liu, Fu Wang, Xiaoying Xu
article en

Abstract

A gale occurred in Beijing on 30 May 2024, which led to fallen trees and damaged infrastructure. This event was primarily driven by surface divergent winds induced by strong convective downdrafts. During the occurrence and development of this gale, solar shortwave radiation and cloud-related variables played a crucial role in triggering, sustaining, and organizing convection. This study proposes a new diagnostic analysis approach for this gale focusing on shortwave radiation and cloud-related variables involved in the physical processes of gale development, based on the FY-4B L2 products and simulations from the Mesoscale Weather Numerical Forecast System of the China Meteorological Administration (CMA-MESO). The diagnostic analysis results of this case show that before cloud formation, the CMA-MESO simulates stronger shortwave radiation heating in the initial stages, leading to an overestimation of surface temperature rise. Additionally, the simulated cloud formation occurs slightly later than observed, with reduced cloud coverage, shorter cloud duration, and lower cloud top heights, resulting in a weaker convective intensity compared to observations. Furthermore, the CMA-MESO underestimates the temperature gradient between the middle and lower troposphere and predicts lower convective instability, which leads to weaker forecasts of convection organization. Ultimately, this study provides a theoretical basis and technical support for enhancing the ability of the CMA-MESO to simulate this gale by using the FY-4B L2 data products for diagnostic analysis.

Remote SensingVol. 17(8)
China Meteorological Administration (CN), Chinese Academy of Meteorological Sciences (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 92%
Safety and Risk Management
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