A Dataset of Tropical Cyclones in the Western North Pacific from Chinese Fengyun Geostationary Meteorological Satellite Observations over the Past Two Decades - Xiaoqin Lu; Johnny C. L. Chan; Jie Tang; Hui Yu; Feng Lu; Yue Wang; Ming Ying; Runling Yu; Lan Wei; Longping Hu (Advances in Atmospheric Sciences, 2026)
This paper introduces a comprehensive Tropical Cyclone (TC) dataset based on China’s FengYun (FY) series geostationary satellite observations in the Western North Pacific (WNP), designated as FYSATTY. This innovative dataset integrates high-resolution geostationary meteorological satellite observations with best-track data, providing a unique resource for TC monitoring and analysis in the WNP region. FYSATTY is derived from infrared, water vapor, and visible channel imagery captured by the FY-2 and FY-4 satellites, which is 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), is interpolated into 3-h intervals to ensure temporal consistency with the satellite observations. This dataset consists of 99714 observations for 614 TCs from 2005 to 2024 and will be updated annually. It represents the most comprehensive collection of TC satellite imagery derived from the FY series of geostationary meteorological satellites of the National Satellite Meteorological Center (NSMC). It encompasses a synthesis of infrared, water vapor, and visible channel data from the FY-2 and FY-4 satellite series. The dataset has been meticulously cropped and georeferenced using the CMA best-track data, with fundamental geographic spatial calibration completed. Comparative analysis with analogous elements within other TC geostationary meteorological satellite datasets reveals a high degree of consistency. It is suitable for reanalyzing TC best-track data, assessing climatic characteristics of TC satellite observations, and enhancing forecasting capabilities. This dataset is particularly useful for TC research, forecasting, and machine learning, and is publicly available.

Extreme Impacts of Four Landfalling Tropical Cyclones in China during the 2024 Peak Season - Liguang Wu; Runling Yu; Chunyi Xiang; Hui Yu; Yecheng Feng; Xingyang Zhou (Advances in Atmospheric Sciences, 2025)

On the Revival Mechanism of Typhoon Doksuri (2023) Remnants after Its Landfall - Chunyi Xiang; Lin Dong; Yu Lan; Hui Wang; Runling Yu; Xiaoyong Zhuge; Da Liu; Qian Wang (Journal of Meteorological Research, 2024)

Erratum to: Numerical simulation of changes in tropical cyclone intensity using a coupled air-sea model - Yihong Duan; Rongsheng Wu; Runling Yu; Xudong Liang (Journal of Meteorological Research, 2014)