GOES ABI-derived hailstorm polygons and tracking dataset for Brazil

Area of Science:

• Meteorology and Atmospheric Science
• Remote Sensing
• Climate Science

Background:

• Hailstorms pose significant risks to Brazil, necessitating better understanding of convective systems (CSs) driving these events.
• Existing datasets may lack the spatial and temporal resolution required for detailed analysis of hail-producing CSs.

Purpose of the Study:

• To create a comprehensive dataset of CSs associated with hailstorms over Brazil.
• To facilitate advanced spatial and temporal analyses of severe weather phenomena.

Main Methods:

• Utilized GOES-16 Advanced Baseline Imager (ABI) measurements and the Tracking and Analysis of Thunderstorms (TATHU) tool to identify and track CSs.
• Employed a brightness temperature threshold of 235 K for CS detection and a 10% overlap criterion for life cycle tracking.
• Integrated verified ground reports of hail to filter and identify CSs directly linked to hail events, storing data in a SpatiaLite-enabled database.

Main Results:

• Developed a five-year dataset (2018-2023) of tracked CSs associated with hailstorms in Brazil.
• The dataset includes geographic boundaries, raster statistics (e.g., brightness temperature), and life cycle information for each CS.
• Successfully linked satellite-tracked CSs with verified ground-truth hail reports.

Conclusions:

• The created dataset provides a valuable resource for meteorological and climate research in Brazil.
• Enables detailed analysis of hail-producing CSs, supporting climate model validation and weather prediction improvements.
• Offers potential for studying the climatological impact of severe weather events.