Articles | Volume 11, issue 5
https://doi.org/10.5194/amt-11-3177-2018
© Author(s) 2018. 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-11-3177-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jun 2018
Research article |
| 01 Jun 2018
| 01 Jun 2018
Neural network cloud top pressure and height for MODIS
Swedish Meteorological and Hydrological Institute (SMHI), Norrköping,
Sweden
Claudia Adok
Regional Cancer Center Western Sweden, Gothenburg, Sweden
Anke Thoss
Swedish Meteorological and Hydrological Institute (SMHI), Norrköping,
Sweden
Ronald Scheirer
Swedish Meteorological and Hydrological Institute (SMHI), Norrköping,
Sweden
Sara Hörnquist
Swedish Meteorological and Hydrological Institute (SMHI), Norrköping,
Sweden
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- Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation H. Huang et al. 10.5194/amt-17-7129-2024
- Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks C. White et al. 10.5194/amt-14-3371-2021
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- Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI A. Pirinen et al. 10.3390/rs16040694
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- Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network S. Sun-Mack et al. 10.5194/amt-17-3323-2024
- Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products M. Haarig et al. 10.5194/amt-16-5953-2023
- First Release of the Optimal Cloud Analysis Climate Data Record from the EUMETSAT SEVIRI Measurements 2004–2019 A. Bozzo et al. 10.3390/rs16162989
- On estimation of cloudiness characteristics and parameters of DOAS retrieval from spectral measurements using a neural network O. Postylyakov et al. 10.1088/1755-1315/489/1/012031
- Estimation of Surface Downward Longwave Radiation and Cloud Base Height Based on Infrared Multichannel Data of Himawari-8 J. Shao et al. 10.3390/atmos14030493
- CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations N. Benas et al. 10.5194/essd-15-5153-2023
- From trees to rain: enhancement of cloud glaciation and precipitation by pollen J. Kretzschmar et al. 10.1088/1748-9326/ad747a
- Cloud-Top Height Comparison from Multi-Satellite Sensors and Ground-Based Cloud Radar over SACOL Site X. Yang et al. 10.3390/rs13142715
- Retrieval of cloud properties from thermal infrared radiometry using convolutional neural network Q. Wang et al. 10.1016/j.rse.2022.113079
- Detecting Multilayer Clouds From the Geostationary Advanced Himawari Imager Using Machine Learning Techniques Z. Tan et al. 10.1109/TGRS.2021.3087714
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- Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height M. Wang et al. 10.5194/acp-24-14239-2024
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Latest update: 25 Jul 2025
Short summary
In this paper a new algorithm for cloud top height retrieval from imager instruments like MODIS is presented. It uses artificial neural networks and reduces the mean absolute error by 32 % compared to two other operational cloud height algorithms. This means that improved cloud height retrieval for nowcasting, as input to models and in cloud climatologies is possible.
In this paper a new algorithm for cloud top height retrieval from imager instruments like MODIS...
