Articles | Volume 13, issue 5
https://doi.org/10.5194/amt-13-2257-2020
© 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-13-2257-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
11 May 2020
Research article | Highlight paper |
| 11 May 2020
| 11 May 2020
A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations
Joint Center for Earth Systems Technology, University of Maryland
Baltimore County, Baltimore, MD, USA
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt,
MD, USA
Steven Platnick
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt,
MD, USA
Kerry Meyer
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt,
MD, USA
Zhibo Zhang
Department of Physics, University of Maryland, Baltimore County,
Baltimore, MD, USA
Yaping Zhou
Joint Center for Earth Systems Technology, University of Maryland
Baltimore County, Baltimore, MD, USA
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt,
MD, USA
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- Retrieval of cloud properties from thermal infrared radiometry using convolutional neural network Q. Wang et al. 10.1016/j.rse.2022.113079
- Enhanced Multi-Dimensional and Multi-Grained Cascade Forest for Cloud/Snow Recognition Using Multispectral Satellite Remote Sensing Imagery M. Xia et al. 10.1109/ACCESS.2021.3114185
- Detecting Supercooled Water Clouds Using Passive Radiometer Measurements G. Zhou et al. 10.1029/2021GL096111
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- The Deep‐Learning‐Based Fast Efficient Nighttime Retrieval of Thermodynamic Phase From Himawari‐8 AHI Measurements X. Tong et al. 10.1029/2022GL100901
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- Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model Y. Yang et al. 10.1029/2020EA001310
- A Unified Cloud Detection Method for Suomi-NPP VIIRS Day and Night PAN Imagery J. Li et al. 10.1109/TGRS.2024.3426649
- 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
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- A hybrid cloud detection and cloud phase classification algorithm using classic threshold-based tests and extra randomized tree model H. Shang et al. 10.1016/j.rse.2023.113957
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- Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers M. Sandford et al. 10.5194/amt-13-7047-2020
- Improving aerosol optical depth retrievals from Himawari-8 with ensemble learning enhancement: Validation over Asia D. Fu et al. 10.1016/j.atmosres.2023.106624
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- Evaluating the degree of cloudiness using machine learning techniques based on different atmospheric conditions N. Borna & M. Rahman 10.1007/s00704-024-05062-x
- Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods J. Haynes et al. 10.1175/JTECH-D-21-0084.1
- The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals F. Romano et al. 10.3390/rs16142578
- Cloud Classification by Machine Learning for Geostationary Radiation Imager B. Guo et al. 10.1109/TGRS.2024.3353373
- Development of an Algorithm for the Simultaneous Retrieval of Cloud-Top Height and Cloud Optical Thickness Combining Radiative Transfer and Multisource Satellite Information From O₄ Hyperspectral Measurements W. Wang et al. 10.1109/TGRS.2024.3385030
- The NASA MODIS-VIIRS Continuity Cloud Optical Properties Products S. Platnick et al. 10.3390/rs13010002
- 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
40 citations as recorded by crossref.
- Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark Target Algorithm: 2. Nonspherical Dust Model Y. Zhou et al. 10.1029/2020EA001222
- A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements S. Das et al. 10.3390/rs14153631
- Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation J. Lee et al. 10.3390/rs13030456
- CACM-Net: Daytime Cloud Mask for AGRI Onboard the FY-4A Satellite J. Yang et al. 10.3390/rs16142660
- 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
- Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data D. Robbins et al. 10.5194/amt-15-3031-2022
- A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1 S. Larosa et al. 10.3390/rs15071798
- Toward Physics-Informed Neural Networks for 3-D Multilayer Cloud Mask Reconstruction Y. Wang et al. 10.1109/TGRS.2023.3329649
- An improvement of snow/cloud discrimination from machine learning using geostationary satellite data D. Jin et al. 10.1080/17538947.2022.2152886
- Retrieval of cloud properties from thermal infrared radiometry using convolutional neural network Q. Wang et al. 10.1016/j.rse.2022.113079
- Enhanced Multi-Dimensional and Multi-Grained Cascade Forest for Cloud/Snow Recognition Using Multispectral Satellite Remote Sensing Imagery M. Xia et al. 10.1109/ACCESS.2021.3114185
- Detecting Supercooled Water Clouds Using Passive Radiometer Measurements G. Zhou et al. 10.1029/2021GL096111
- A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image C. Liu et al. 10.1007/s00376-021-0366-x
- Accuracy of Cirrus Detection by Surface-Based Human Observers A. Kotarba & Z. Huu 10.1175/JCLI-D-21-0430.1
- Cloud Detection and Classification Algorithms for Himawari-8 Imager Measurements Based on Deep Learning W. Li et al. 10.1109/TGRS.2022.3153129
- The Deep‐Learning‐Based Fast Efficient Nighttime Retrieval of Thermodynamic Phase From Himawari‐8 AHI Measurements X. Tong et al. 10.1029/2022GL100901
- Fengyun-3D/MERSI-II Cloud Thermodynamic Phase Determination Using a Machine-Learning Approach D. Zhao et al. 10.3390/rs13122251
- Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains T. Su & Y. Zhang 10.5194/gmd-17-6319-2024
- Reproducible and Portable Big Data Analytics in the Cloud X. Wang et al. 10.1109/TCC.2023.3245081
- Spaceborne lidar measurement of global cloud properties through machine learning K. Hu & X. Lu 10.3389/frsen.2024.1477503
- VIIRS Edition 1 Cloud Properties for CERES, Part 2: Evaluation with CALIPSO C. Yost et al. 10.3390/rs15051349
- Cloud detection in East Asian urban areas in VIIRS day/night band by texture analysis J. PARK et al. 10.4287/jsprs.61.317
- Quantifying and Mitigating Errors in Estimating Downward Surface Shortwave Radiation Caused by Cloud Mask Data L. Wang et al. 10.1109/TGRS.2024.3438879
- Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model Y. Yang et al. 10.1029/2020EA001310
- A Unified Cloud Detection Method for Suomi-NPP VIIRS Day and Night PAN Imagery J. Li et al. 10.1109/TGRS.2024.3426649
- 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
- Application of Shape Moments for Cloudiness Assessment in Marine Environmental Research M. Paszkuta et al. 10.3390/rs14040883
- A hybrid cloud detection and cloud phase classification algorithm using classic threshold-based tests and extra randomized tree model H. Shang et al. 10.1016/j.rse.2023.113957
- Comparison of the Novel Probabilistic Self-Optimizing Vectorized Earth Observation Retrieval Classifier with Common Machine Learning Algorithms J. Musial & J. Bojanowski 10.3390/rs14020378
- Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 1: Develop AI-Based Clear-Sky Mask X. Liang & Q. Liu 10.3390/rs13020222
- Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers M. Sandford et al. 10.5194/amt-13-7047-2020
- Improving aerosol optical depth retrievals from Himawari-8 with ensemble learning enhancement: Validation over Asia D. Fu et al. 10.1016/j.atmosres.2023.106624
- Spatial Differentiation Characteristics of Rural Areas Based on Machine Learning and GIS Statistical Analysis—A Case Study of Yongtai County, Fuzhou City Z. Wang 10.3390/su15054367
- Evaluating the degree of cloudiness using machine learning techniques based on different atmospheric conditions N. Borna & M. Rahman 10.1007/s00704-024-05062-x
- Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods J. Haynes et al. 10.1175/JTECH-D-21-0084.1
- The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals F. Romano et al. 10.3390/rs16142578
- Cloud Classification by Machine Learning for Geostationary Radiation Imager B. Guo et al. 10.1109/TGRS.2024.3353373
- Development of an Algorithm for the Simultaneous Retrieval of Cloud-Top Height and Cloud Optical Thickness Combining Radiative Transfer and Multisource Satellite Information From O₄ Hyperspectral Measurements W. Wang et al. 10.1109/TGRS.2024.3385030
- The NASA MODIS-VIIRS Continuity Cloud Optical Properties Products S. Platnick et al. 10.3390/rs13010002
- 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
Latest update: 04 Nov 2024
Short summary
A machine-learning (ML)-based approach that can be used for cloud mask and phase detection is developed. An all-day model that uses infrared (IR) observations and a daytime model that uses shortwave and IR observations from a passive instrument are trained separately for different surface types. The training datasets are selected by using reference pixel types from collocated space lidar. The ML approach is validated carefully and the overall performance is better than traditional methods.
A machine-learning (ML)-based approach that can be used for cloud mask and phase detection is...
