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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- An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing A. Doicu et al. 10.3390/rs13245061
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- Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques N. Mevada & R. Srivastava 10.2166/wcc.2024.511
- CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023 K. Karlsson et al. 10.5194/essd-15-4901-2023
- 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
- Operational application of Fengyun geostationary meteorological satellites to cloud observation products M. Du et al. 10.1038/s41598-024-68593-3
- Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers C. White et al. 10.1175/AIES-D-21-0001.1
- Decadal Stability and Trends in the Global Cloud Amount and Cloud Top Temperature in the Satellite-Based Climate Data Records A. Devasthale & K. Karlsson 10.3390/rs15153819
- CERES MODIS Cloud Product Retrievals for Edition 4—Part II: Comparisons to CloudSat and CALIPSO C. Yost et al. 10.1109/TGRS.2020.3015155
- Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI A. Pirinen et al. 10.3390/rs16040694
- Machine learning-based retrieval of day and night cloud macrophysical parameters over East Asia using Himawari-8 data Y. Yang et al. 10.1016/j.rse.2022.112971
- 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
- Combining Passive Infrared and Microwave Satellite Observations to Investigate Cloud Microphysical Properties: A Review M. Viggiano et al. 10.3390/rs17020337
- Comparisons of AGRI/FY-4A Cloud Fraction and Cloud Top Pressure with MODIS/Terra Measurements over East Asia T. Wang et al. 10.1007/s13351-019-8160-8
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- Cloud identification and property retrieval from Himawari-8 infrared measurements via a deep neural network X. Wang et al. 10.1016/j.rse.2022.113026
- A Method for Retrieving Cloud-Top Height Based on a Machine Learning Model Using the Himawari-8 Combined with Near Infrared Data Y. Dong et al. 10.3390/rs14246367
- Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach A. Di Noia et al. 10.5194/amt-12-1697-2019
- 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
- Global Cloudiness and Cloud Top Information from AVHRR in the 42-Year CLARA-A3 Climate Data Record Covering the Period 1979–2020 K. Karlsson et al. 10.3390/rs15123044
- Difference between WMO Climate Normal and Climatology: Insights from a Satellite-Based Global Cloud and Radiation Climate Data Record A. Devasthale et al. 10.3390/rs15235598
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Latest update: 03 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...
