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
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Cited
16 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
- 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
- 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
16 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
- 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
- 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
Latest update: 25 Apr 2024
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...
