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A dataset for machine learning model to convective initiation detection and nowcasting over southeastern China
Yujia Liu1, Anyuan Xiong2, Na Liu1
1National Meteorological Information Center, CMA, Beijing, China.
Scientific Data
|March 1, 2026
Summary
This study introduces the Convective Initiation Dataset (CIDS) for artificial intelligence (AI) models to improve severe weather forecasting. The CIDS aids in identifying and predicting convective initiation (CI) events with high accuracy.
Area of Science:
- Meteorology and Atmospheric Science
- Artificial Intelligence in Weather Forecasting
- Remote Sensing for Severe Weather
Background:
- Effective identification and forecasting of Convective Initiation (CI) are critical for severe convective weather early warning systems.
- Artificial intelligence (AI) presents a promising avenue for enhancing CI forecasting and warning capabilities.
Purpose of the Study:
- To introduce the Convective Initiation Dataset (CIDS), a novel dataset specifically designed for AI models to identify and forecast CI.
- To provide comprehensive feature data and labels for intense convective weather events in southeastern China.
Main Methods:
- The CIDS dataset was compiled using radar mosaic products and FY-4A satellite radiance data from 2018 to 2023.
- An algorithm utilizing radar composite reflectance factors was developed to identify incipient storm cells and assign CI category labels based on 30-minute evolution.
- Data includes 10 radar mosaic products and satellite radiance across nine spectral bands at 10-minute intervals.
Main Results:
- The CIDS dataset contains 136,728 samples, identifying over 4.1 million CIs, with nearly 1.8 million classified as developing CIs.
- The dataset captures short-duration heavy precipitation, thunderstorm winds, and hail events, offering detailed spatial and temporal information.
Conclusions:
- The CIDS dataset provides a valuable resource for training AI models to improve the accuracy and timeliness of convective initiation forecasting.
- This dataset facilitates advancements in early warning systems for severe convective weather, leveraging AI and multi-source meteorological data.
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