2026-10-12T01:55:45Z https://www.scidb.cn/oai

10.57760/sciencedb.j00264.00013 2026-04-20T09:39:49Z

2026-04-20 FYSATTY v1.0 doi:10.57760/sciencedb.j00264.00013 en FYSATTY is a comprehensive tropical cyclone (TC) dataset derived from China's FENGYUN (FY) series geostationary satellite observations over the Western North Pacific (WNP). This innovative dataset integrates high-resolution geostationary meteorological satellite data with best-track information, offering a unique resource for TC monitoring and analysis in the region. FYSATTY is built from infrared, water vapor, and visible channel imagery collected by the FY-2 and FY-4 satellites. The data are processed using an equal-interval latitude-longitude grid system within a 1500-km radius centered on each TC. The best-track data, sourced from the China Meteorological Administration (CMA), are interpolated to 3-hour intervals to ensure temporal consistency with satellite observations. The dataset comprises 99,714 observations from 614 TCs spanning 2005 to 2024, representing the most comprehensive collection of TC satellite imagery derived from the FY series geostationary satellites operated by the National Satellite Meteorological Center (NSMC). The imagery has been meticulously cropped and georeferenced based on CMA best-track data, with fundamental geographic spatial calibration completed. Comparative analyses with analogous elements from other TC geostationary satellite datasets reveal a high degree of consistency. This dataset is suitable for reanalyzing TC best-track data, assessing the climatic characteristics of TC satellite observations, and improving TC forecasting capabilities. It is particularly valuable for TC research, forecasting, and machine learning applications. tropical cyclone; geostationary satellite; FENGYUN series; wWestern North Pacific Xiaoqin Lu Johnny C. L. CHAN Jie Tang Hui Yu Feng Lu Yue Wang Ming Ying Runling Yu Lan Wei Longping Hu PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1007/s00376-025-5573-4 Science Data Bank