This dataset fully records 24 years of observational records of polar mesoscale cyclones in the Nordic region during winter (November to April of the following year) from 2001 to 2024. This dataset contains 1110 vortex trajectories, 16001 cyclone cloud features (including length, width, position, and shape features - spiral/comma shaped), and 4472 wind speed records (wind vector images and maximum cyclone wind speed). At the same time, provide the corresponding hourly vortex path derived from ERA5 data, including 850 hPa vorticity and the lowest atmospheric pressure value near the sea level. As the most comprehensive PMC archive in the Nordic waters, the IMPMCT dataset provides basic data for deepening research on cyclone generation and enhancement mechanisms, assisting in the development of enhanced monitoring and early warning systems, supporting the validation and optimization of polar numerical weather forecasting models, and promoting the improvement of risk assessment and safety regulations for offshore operations.
1. AVHRR data: In this study, infrared images of cyclone cloud characteristics were obtained from two types of first level B data products of AVHRR (Kalluri et al., 2021): global coverage (GAC) and local coverage (LAC) four band data.
2. ERA5 data: This study used ERA5 reanalysis data from the extended winter period (November to April of the following year) from 2001 to 2024, with a spatial resolution of 0.25 °× 0.25 °, covering the areas of latitude 50 ° -85 ° N and longitude 40 ° -80 ° E. This dataset is used to track vortices and calculate their intensity, scale, and other evolutionary characteristics.
3. QuikSCAT/ASCAT data: This dataset further utilizes QuikSCAT and ASCAT data to investigate the characteristics of the near surface wind field in the core area of the cyclone and its surrounding environmental conditions.
This dataset was constructed by combining the vortex tracking algorithm applied to ERA5 reanalysis data with the advanced Very High Resolution Radiometer (AVHRR) infrared image cyclone cloud feature detection method based on deep learning, and integrating near surface wind speed observation data from Advanced Scatterometer (ASCAT) and Fast Scatterometer (QuikSCAT).
The verification results indicate that this dataset has statistical consistency with the existing cyclone path dataset, and presents the cyclone lifecycle trajectory more comprehensively than previous datasets, providing a more intuitive cloud visualization effect and containing a richer parameter set.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ZENODO.DB7021.2025
10.5281/zenodo.17142448
