Articles | Volume 13, issue 1
https://doi.org/10.5194/amt-13-1-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-1-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jan 2020
Research article |
| 02 Jan 2020
| 02 Jan 2020
Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar
Key Laboratory for Atmosphere and Global Environment Observation, Chinese
Academy of Sciences, Bejing, 100029, China
Daren Lu
Key Laboratory for Atmosphere and Global Environment Observation, Chinese
Academy of Sciences, Bejing, 100029, China
Shu Duan
Key Laboratory for Atmosphere and Global Environment Observation, Chinese
Academy of Sciences, Bejing, 100029, China
Yongheng Bi
Key Laboratory for Atmosphere and Global Environment Observation, Chinese
Academy of Sciences, Bejing, 100029, China
Bo Liu
Key Laboratory for Atmosphere and Global Environment Observation, Chinese
Academy of Sciences, Bejing, 100029, China
Viewed
Total article views: 4,065 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 28 Aug 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,881 | 1,097 | 87 | 4,065 | 89 | 122 |
- HTML: 2,881
- PDF: 1,097
- XML: 87
- Total: 4,065
- BibTeX: 89
- EndNote: 122
Total article views: 2,935 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Jan 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,155 | 698 | 82 | 2,935 | 80 | 113 |
- HTML: 2,155
- PDF: 698
- XML: 82
- Total: 2,935
- BibTeX: 80
- EndNote: 113
Total article views: 1,130 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 28 Aug 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 726 | 399 | 5 | 1,130 | 9 | 9 |
- HTML: 726
- PDF: 399
- XML: 5
- Total: 1,130
- BibTeX: 9
- EndNote: 9
Viewed (geographical distribution)
Total article views: 4,065 (including HTML, PDF, and XML)
Thereof 3,573 with geography defined
and 492 with unknown origin.
Total article views: 2,935 (including HTML, PDF, and XML)
Thereof 2,668 with geography defined
and 267 with unknown origin.
Total article views: 1,130 (including HTML, PDF, and XML)
Thereof 905 with geography defined
and 225 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
21 citations as recorded by crossref.
- Precipitation Microphysics of Tropical Cyclones over Northeast China in 2020 A. Zhang et al. 10.3390/rs14092188
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al. 10.5194/amt-17-961-2024
- Retrieving cloud base height from passive radiometer observations via a systematic effective cloud water content table Z. Tan et al. 10.1016/j.rse.2023.113633
- Climatology of Cloud Base Height Retrieved from Long-Term Geostationary Satellite Observations Z. Tan et al. 10.3390/rs15133424
- Assessment of Geostationary Satellite Cloud Effective Radius over East China with In Situ Aircraft Measurements Q. Zhang et al. 10.1007/s13351-025-4164-8
- Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization Y. Cheng et al. 10.3390/s24020541
- Physics-Driven Machine Learning Algorithm Facilitates Multilayer Cloud Property Retrievals From Geostationary Passive Imager Measurements W. Li et al. 10.1109/TGRS.2024.3369621
- Estimating cloud base height from Himawari-8 based on a random forest algorithm Z. Tan et al. 10.1080/01431161.2020.1854891
- Cloud-Top Height Comparison from Multi-Satellite Sensors and Ground-Based Cloud Radar over SACOL Site X. Yang et al. 10.3390/rs13142715
- An Assessment of the Influences of Clouds on the Solar Photovoltaic Potential over China Y. Jiang & B. Yi 10.3390/rs15010258
- Cloud characteristics over the Yunnan–Guizhou plateau as observed by MODIS and Himawari‐8 L. Yu et al. 10.1002/joc.8307
- The Importance of Humidity in the Afternoon Local-Scale Precipitation Intensity over Eastern China and Its Impacts on the Aerosol Effects X. Tang et al. 10.3390/rs17050778
- Unveiling Cloud Vertical Structures over the Interior Tibetan Plateau through Anomaly Detection in Synergetic Lidar and Radar Observations W. Zhao et al. 10.1007/s00376-024-3221-z
- A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar X. Hu et al. 10.5194/amt-14-1743-2021
- Lagrange Point Missions: The Key to next Generation Integrated Earth Observations. DSCOVR Innovation F. Valero et al. 10.3389/frsen.2021.745938
- Classification and characteristic analysis of the clouds and dust in a dust-carrying precipitation process based on multi-source remote sensing observations Y. Chen et al. 10.1016/j.apr.2021.101267
- Comparison of Cloud‐Top Property Retrievals From Advanced Himawari Imager, MODIS, CloudSat/CPR, CALIPSO/CALIOP, and Radiosonde C. Liu et al. 10.1029/2020JD032683
- Fine vertical structures of cloud from a ground‐based cloud radar over the western Tianshan mountains P. Chen et al. 10.1002/met.2105
- Assessment of FY-4A and Himawari-8 Cloud Top Height Retrieval through Comparison with Ground-Based Millimeter Radar at Sites in Tibet and Beijing B. Liu et al. 10.1007/s00376-021-0337-2
- 基于星载多光谱辐射计成像仪的云底高度反演算法研究进展 谭. Tan Zhonghui et al. 10.3788/AOS241024
- Cloud heights retrieval from passive satellite measurements using lapse rate information W. Zhang et al. 10.1016/j.rse.2025.114622
21 citations as recorded by crossref.
- Precipitation Microphysics of Tropical Cyclones over Northeast China in 2020 A. Zhang et al. 10.3390/rs14092188
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al. 10.5194/amt-17-961-2024
- Retrieving cloud base height from passive radiometer observations via a systematic effective cloud water content table Z. Tan et al. 10.1016/j.rse.2023.113633
- Climatology of Cloud Base Height Retrieved from Long-Term Geostationary Satellite Observations Z. Tan et al. 10.3390/rs15133424
- Assessment of Geostationary Satellite Cloud Effective Radius over East China with In Situ Aircraft Measurements Q. Zhang et al. 10.1007/s13351-025-4164-8
- Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization Y. Cheng et al. 10.3390/s24020541
- Physics-Driven Machine Learning Algorithm Facilitates Multilayer Cloud Property Retrievals From Geostationary Passive Imager Measurements W. Li et al. 10.1109/TGRS.2024.3369621
- Estimating cloud base height from Himawari-8 based on a random forest algorithm Z. Tan et al. 10.1080/01431161.2020.1854891
- Cloud-Top Height Comparison from Multi-Satellite Sensors and Ground-Based Cloud Radar over SACOL Site X. Yang et al. 10.3390/rs13142715
- An Assessment of the Influences of Clouds on the Solar Photovoltaic Potential over China Y. Jiang & B. Yi 10.3390/rs15010258
- Cloud characteristics over the Yunnan–Guizhou plateau as observed by MODIS and Himawari‐8 L. Yu et al. 10.1002/joc.8307
- The Importance of Humidity in the Afternoon Local-Scale Precipitation Intensity over Eastern China and Its Impacts on the Aerosol Effects X. Tang et al. 10.3390/rs17050778
- Unveiling Cloud Vertical Structures over the Interior Tibetan Plateau through Anomaly Detection in Synergetic Lidar and Radar Observations W. Zhao et al. 10.1007/s00376-024-3221-z
- A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar X. Hu et al. 10.5194/amt-14-1743-2021
- Lagrange Point Missions: The Key to next Generation Integrated Earth Observations. DSCOVR Innovation F. Valero et al. 10.3389/frsen.2021.745938
- Classification and characteristic analysis of the clouds and dust in a dust-carrying precipitation process based on multi-source remote sensing observations Y. Chen et al. 10.1016/j.apr.2021.101267
- Comparison of Cloud‐Top Property Retrievals From Advanced Himawari Imager, MODIS, CloudSat/CPR, CALIPSO/CALIOP, and Radiosonde C. Liu et al. 10.1029/2020JD032683
- Fine vertical structures of cloud from a ground‐based cloud radar over the western Tianshan mountains P. Chen et al. 10.1002/met.2105
- Assessment of FY-4A and Himawari-8 Cloud Top Height Retrieval through Comparison with Ground-Based Millimeter Radar at Sites in Tibet and Beijing B. Liu et al. 10.1007/s00376-021-0337-2
- 基于星载多光谱辐射计成像仪的云底高度反演算法研究进展 谭. Tan Zhonghui et al. 10.3788/AOS241024
- Cloud heights retrieval from passive satellite measurements using lapse rate information W. Zhang et al. 10.1016/j.rse.2025.114622
Latest update: 13 Jul 2025
Short summary
Cloud top height (CTH) is one of the important cloud parameters providing information about the vertical structure of cloud water content. To better understand the accuracy of CTH derived from passive satellite data, 2 years of ground-based Ka-band radar measurements are compared with CTH inferred from Terra/Aqua MODIS and Himawari AHI. It is found that MODIS and AHI underestimate CTH relative to radar by −1.10 km. Both MODIS and AHI CTH retrieval accuracy depend strongly on cloud depth.
Cloud top height (CTH) is one of the important cloud parameters providing information about the...
