Articles | Volume 16, issue 4
https://doi.org/10.5194/essd-16-1747-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-1747-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
08 Apr 2024
Data description article |
| 08 Apr 2024
| 08 Apr 2024
Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research
Xiaoyong Zhuge
Key Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Xiaolei Zou
CORRESPONDING AUTHOR
Joint Center of Data Assimilation for Research and Application, Nanjing University of Information Science and Technology, Nanjing 210044, China
Lu Yu
Key Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Key Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Mingjian Zeng
Key Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Yilun Chen
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
Bing Zhang
Key Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Bin Yao
Key Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Fei Tang
Key Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Fengjiao Chen
Key Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Wanlin Kan
Key Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
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Cited
15 citations as recorded by crossref.
- On the Revival Mechanism of Typhoon Doksuri (2023) Remnants after Its Landfall C. Xiang et al. https://doi.org/10.1007/s13351-024-3175-1
- Passive satellite hourly precipitation estimation over mainland China by combining cloud and meteorological parameters S. Xu et al. https://doi.org/10.1016/j.atmosres.2025.108112
- Detection of Atmospheric Blocking and Northeast China Cold Vortex Using Satellite Microwave Temperature-Sounding Observations Y. Mao & X. Zou https://doi.org/10.1109/TGRS.2025.3594353
- Retrieving Tropical Cyclone Radius of Maximum Wind From Geostationary Satellite Infrared Imagery With SAR Observations as Ground Truth T. Chen et al. https://doi.org/10.1109/JSTARS.2026.3677609
- Cloud Removal Using Patch-Based Improved Denoising Diffusion Models and High Gray-Value Attention Mechanism Y. Huang et al. https://doi.org/10.1109/LGRS.2025.3560799
- Fusing Instantaneous and Historical Spatial–Contextual Brightness Temperature Differences for Himawari-8/9 Active Fire Detection X. Liu & Y. Ming https://doi.org/10.3390/rs18060907
- Comparison of China Southwest Vortex Structures from AHI Observations, ERA5 Reanalysis, and NCEP GFS All-Sky Simulations Z. Shi & X. Zou https://doi.org/10.1007/s13351-025-5084-3
- A physics-informed machine learning method for short-term solar radiation forecast with satellite-derived cloud optical depth W. Peng et al. https://doi.org/10.1016/j.apenergy.2025.127284
- Interannual Divergence of PM2.5 and O3 Pollution in East-Central China Revealed by Seamless 24-h Retrievals (2017–2023) Y. Su et al. https://doi.org/10.1109/TGRS.2026.3665614
- Observed characteristics of cloud fraction over eastern China: A comparison of ISCCP and Himawari-8/9 Cloud Feature Dataset J. Shen et al. https://doi.org/10.1016/j.aosl.2026.100899
- Dual-Satellite Stereoscopic Retrieval of Cloud Top Height Using FY-4A and FY-4B X. Huang et al. https://doi.org/10.1007/s13351-024-4050-9
- Effects of fine and coarse aerosols on the summer precipitation structure and microphysics over the Yangtze River Delta region H. Peng et al. https://doi.org/10.1016/j.atmosres.2025.108277
- Case study of aircraft icing in-cloud measurements and explicit supercooled water prediction in Eastern China L. Luo et al. https://doi.org/10.1016/j.atmosres.2026.108748
- Multimetrics Assessment of Explicit Hail Size Prediction Using Two‐ and Three‐Moment Microphysical Schemes for a Severe Hailstorm in Northeastern China L. Luo et al. https://doi.org/10.1155/adme/7747775
- Expedient Mid-Wave Infrared Band Generation for AGRI during Stray Light Contamination Periods Using a Deep Learning Model H. Xiao et al. https://doi.org/10.1007/s13351-025-4107-4
15 citations as recorded by crossref.
- On the Revival Mechanism of Typhoon Doksuri (2023) Remnants after Its Landfall C. Xiang et al. https://doi.org/10.1007/s13351-024-3175-1
- Passive satellite hourly precipitation estimation over mainland China by combining cloud and meteorological parameters S. Xu et al. https://doi.org/10.1016/j.atmosres.2025.108112
- Detection of Atmospheric Blocking and Northeast China Cold Vortex Using Satellite Microwave Temperature-Sounding Observations Y. Mao & X. Zou https://doi.org/10.1109/TGRS.2025.3594353
- Retrieving Tropical Cyclone Radius of Maximum Wind From Geostationary Satellite Infrared Imagery With SAR Observations as Ground Truth T. Chen et al. https://doi.org/10.1109/JSTARS.2026.3677609
- Cloud Removal Using Patch-Based Improved Denoising Diffusion Models and High Gray-Value Attention Mechanism Y. Huang et al. https://doi.org/10.1109/LGRS.2025.3560799
- Fusing Instantaneous and Historical Spatial–Contextual Brightness Temperature Differences for Himawari-8/9 Active Fire Detection X. Liu & Y. Ming https://doi.org/10.3390/rs18060907
- Comparison of China Southwest Vortex Structures from AHI Observations, ERA5 Reanalysis, and NCEP GFS All-Sky Simulations Z. Shi & X. Zou https://doi.org/10.1007/s13351-025-5084-3
- A physics-informed machine learning method for short-term solar radiation forecast with satellite-derived cloud optical depth W. Peng et al. https://doi.org/10.1016/j.apenergy.2025.127284
- Interannual Divergence of PM2.5 and O3 Pollution in East-Central China Revealed by Seamless 24-h Retrievals (2017–2023) Y. Su et al. https://doi.org/10.1109/TGRS.2026.3665614
- Observed characteristics of cloud fraction over eastern China: A comparison of ISCCP and Himawari-8/9 Cloud Feature Dataset J. Shen et al. https://doi.org/10.1016/j.aosl.2026.100899
- Dual-Satellite Stereoscopic Retrieval of Cloud Top Height Using FY-4A and FY-4B X. Huang et al. https://doi.org/10.1007/s13351-024-4050-9
- Effects of fine and coarse aerosols on the summer precipitation structure and microphysics over the Yangtze River Delta region H. Peng et al. https://doi.org/10.1016/j.atmosres.2025.108277
- Case study of aircraft icing in-cloud measurements and explicit supercooled water prediction in Eastern China L. Luo et al. https://doi.org/10.1016/j.atmosres.2026.108748
- Multimetrics Assessment of Explicit Hail Size Prediction Using Two‐ and Three‐Moment Microphysical Schemes for a Severe Hailstorm in Northeastern China L. Luo et al. https://doi.org/10.1155/adme/7747775
- Expedient Mid-Wave Infrared Band Generation for AGRI during Stray Light Contamination Periods Using a Deep Learning Model H. Xiao et al. https://doi.org/10.1007/s13351-025-4107-4
Saved (final revised paper)
Latest update: 06 Sep 2026
Short summary
The Himawari-8/9 level-2 operational cloud product has a low spatial resolution and is available only during the daytime. To supplement this official dataset, a new dataset named the NJIAS Himawari-8/9 Cloud Feature Dataset (HCFD) was constructed. The NJIAS HCFD provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the years 2016–2022 by 30 retrieved cloud variables. The NJIAS HCFD has been demonstrated to outperform the official dataset.
The Himawari-8/9 level-2 operational cloud product has a low spatial resolution and is available...
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