该数据集完整记录了2001-2024年间北欧海域冬季（11月至次年4月）极地中尺度气旋的24年观测记录。该数据集包含1110条涡旋轨迹、16001个气旋云特征（含长度、宽度、位置及形态特征——螺旋状/逗号状），以及4472条风速记录（风矢量图像与气旋最大风速）。同时提供基于ERA5数据推导的对应每小时涡旋路径，包含850 hPa涡度及邻近海平面最低气压值。作为北欧海域最全面的PMC档案库，IMPMCT数据集为深化气旋生成与增强机制研究提供基础数据，助力开发增强型监测预警系统，支持极地数值天气预报模型的验证与优化，并促进海上作业风险评估与安全规程的改进。
", "ds_source": "1、AVHRR数据：本研究用观测气旋云特征的红外影像源自AVHRR的两类一级B数据产品（Kalluri et al., 2021）：全球覆盖（GAC）与局部覆盖（LAC）四波段数据。\n
2、ERA5数据：本研究采用2001-2024年扩展冬季时段（11月至次年4月）的ERA5再分析数据，空间分辨率为0.25°×0.25°，覆盖北纬50°-85°、西经40°-东经80°区域。该数据集用于追踪涡旋并计算其强度、尺度等演化特征。\n
3、QuikSCAT/ASCAT数据：本数据集进一步利用QuikSCAT和ASCAT数据，考察气旋核心区域近地表风场特性及其周边环境条件。", "ds_process_way": "
该数据集通过以下方法构建：将应用于ERA5再分析数据的涡旋追踪算法，与基于深度学习的先进极高分辨率辐射计（AVHRR）红外图像气旋云特征检测方法相结合，同时整合了先进散射计（ASCAT）和快速散射计（QuikSCAT）的近地表风速观测数据。
", "ds_quality": "验证结果表明，该数据集与现有气旋路径数据集具有统计学一致性，同时较以往数据集更完整地呈现气旋生命周期轨迹，提供更直观的云图可视化效果，并包含更丰富的参数集。
", "ds_acq_start_time": "2001-02-01 00:00:00", "ds_acq_end_time": "2024-04-30 00:00:00", "ds_acq_place": "极地", "ds_acq_lon_east": 80.0, "ds_acq_lat_south": 50.0, "ds_acq_lon_west": -40.0, "ds_acq_lat_north": 85.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 6104973863, "ds_files_count": 2, "ds_format": "png", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "3157e99c-ebed-46c0-a945-505b90b1a78c.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2025-11-27 15:50:56", "last_updated": "2026-01-14 11:01:25", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB7021.2025", "i18n": { "en": { "title": "IMPMCT: a dataset of Integrated Multi-source Polar Mesoscale Cyclone Tracks in the Nordic Seas(2001-2024)", "ds_format": "png", "ds_source": "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.\n
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.\n
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.", "ds_quality": "
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.
", "ds_ref_way": "", "ds_abstract": "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.
", "ds_time_res": "", "ds_acq_place": "polar region", "ds_space_res": "", "ds_projection": "", "ds_process_way": "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).
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "极地", "中尺度", "气旋路径" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "极地" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "丁锦锋", "email": "dingjinfeng@nudt.edu.cn", "work_for": "国防科技大学气象海洋学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "丁锦锋", "email": "dingjinfeng@nudt.edu.cn", "work_for": "国防科技大学气象海洋学院", "country": "中国" } ], "ds_managers": [ { "true_name": "丁锦锋", "email": "dingjinfeng@nudt.edu.cn", "work_for": "国防科技大学气象海洋学院", "country": "中国" } ], "category": "极地" }