IMPMCT combines vortices tracking algorithms from ERA5 reanalysis with deep learning-based detection of cyclonic cloud features in Advanced Very High-Resolution Radiometer (AVHRR) infrared imagery, while incorporating near-surface wind matching by Advanced Scatterometer (ASCAT) and Quick Scatterometer (QUIKSCAT) measurements.
The dataset contains 1,184 vortices tracks, 16,630 cyclonic cloud features, and 4373 wind speed records, with multi-dimensional attributes such as cloud morphology, core wind speed, and environmental advection wind speed.
Data sourced from Zenodo website（ https://zenodo.org/records/15355602 ）.
Adopting a combination of multi-source data fusion and multiple methods, including: 1. Based on ERA5 reanalysis data, apply vortex tracking algorithm for relevant processing; 2. Using deep learning methods to detect cyclonic cloud features in Advanced Very High Resolution Radiometer (AVHRR) infrared images; 3. Incorporate near surface wind field information matched with Advanced Scatterometer (ASCAT) and Quick Scatterometer (QUIKSCAT) observation data to achieve integrated application of multi-source data.
The data quality is good.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ZENODO.DB6982.2025
10.5281/zenodo.15355602
