What question did this study set out to answer?

The study aims to develop a nowcasting algorithm for better detection of convective initiation using satellite data.

April 11, 2026Open Access

A Convective Initiation Nowcasting Algorithm Based on FY-4B Satellite AGRI and GHI Data

ZYZongxin YangCivil Aviation Administration of China ZCZhigang ChengCivil Aviation Administration of China WSWenjun SangCivil Aviation Administration of China

Key Points

The study aims to develop a nowcasting algorithm for better detection of convective initiation using satellite data.
Utilized AGRI and GHI data from FY-4B satellite.
Optimized satellite reflectance through multi-channel brightness differences.
Employed Farneback optical flow to track cloud motion and cooling.
Developed and validated using daytime CI events in Sichuan Province.
Achieved a probability of detection (POD) of 83.1%.
Reported a false alarm ratio (FAR) of 33.0%.
Critical success index (CSI) reached 58.9%.
Average lead time for CI detection was 34.2 minutes, increasing from previous methods.

Abstract

Based on the Advanced Geostationary Radiation Imager (AGRI) and Geostationary High-speed Imager (GHI) information in the Fengyun-4B (FY-4B) satellite, we propose a convective initiation (CI) nowcasting algorithm for Sichuan Province, China. The algorithm optimizes satellite reflectance by considering multi-channel brightness differences, visible reflectance, and cloud-top cooling by exploiting the Farneback optical flow, where the cloud is followed by false cooling due to cloud motion. Moreover, the high temporal resolution of GHI enables the detection of early cumulus cloud growth. The algorithm was developed using daytime CI events in the coverage area of Mianyang radar station from 22 July to 9 August 2023, and the remaining areas in the Chengdu scan area were used for validation. The results showed that the proposed method achieves a probability of detection (POD) of 83.1%, a false alarm ratio (FAR) of 33.0%, and a critical success index (CSI) of 58.9%. Compared with the AGRI-only method and the SATCAST algorithm, the POD increases by 5.4% and 8.4%, respectively, while the CSI improves by 1.3% and 2.3%. The average lead time reaches 34.2 min, which is 4.6 min longer than AGRI-only and 7.9 min longer than SATCAST. This suggests that AGRI and GHI data improve the spatiotemporal resolution of CI nowcasting. This approach improves the early detection of convective initiation under the climatic background of warm cloud convection in Sichuan, offering new insights for short-term warnings of regional convective weather.

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