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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- Operational application of Fengyun geostationary meteorological satellites to cloud observation products M. Du et al. 10.1038/s41598-024-68593-3
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- CERES MODIS Cloud Product Retrievals for Edition 4—Part II: Comparisons to CloudSat and CALIPSO C. Yost et al. 10.1109/TGRS.2020.3015155
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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
- 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
- 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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- Estimation of all-sky all-wave daily net radiation at high latitudes from MODIS data J. Chen et al. 10.1016/j.rse.2020.111842
- Measurement of Cloud Top Height: Comparison of MODIS and Ground-Based Millimeter Radar J. Huo et al. 10.3390/rs12101616
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Latest update: 17 Jan 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...
