Articles | Volume 11, issue 5
Atmos. Meas. Tech., 11, 3177–3196, 2018
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.
Atmos. Meas. Tech., 11, 3177–3196, 2018
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
Neural network cloud top pressure and height for MODIS
Nina Håkansson et al.
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Cited
18 citations as recorded by crossref.
- An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing A. Doicu et al. 10.3390/rs13245061
- Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar J. Huo et al. 10.5194/amt-13-1-2020
- A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems S. Pfreundschuh et al. 10.5194/amt-11-4627-2018
- 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
- 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
- Cloud-Top Height Comparison from Multi-Satellite Sensors and Ground-Based Cloud Radar over SACOL Site X. Yang et al. 10.3390/rs13142715
- Detecting Multilayer Clouds From the Geostationary Advanced Himawari Imager Using Machine Learning Techniques Z. Tan et al. 10.1109/TGRS.2021.3087714
- 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
- Comparison of the Novel Probabilistic Self-Optimizing Vectorized Earth Observation Retrieval Classifier with Common Machine Learning Algorithms J. Musial & J. Bojanowski 10.3390/rs14020378
- 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
- CERES MODIS Cloud Product Retrievals for Edition 4—Part II: Comparisons to CloudSat and CALIPSO C. Yost et al. 10.1109/TGRS.2020.3015155
- 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
- Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model G. Wang et al. 10.3390/atmos12020173
- A simulator for the CLARA-A2 cloud climate data record and its application to assess EC-Earth polar cloudiness S. Eliasson et al. 10.5194/gmd-13-297-2020
- Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms M. Min et al. 10.1016/j.rse.2019.111616
- Estimate of daytime single-layer cloud base height from advanced baseline imager measurements H. Lin et al. 10.1016/j.rse.2022.112970
18 citations as recorded by crossref.
- An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing A. Doicu et al. 10.3390/rs13245061
- Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar J. Huo et al. 10.5194/amt-13-1-2020
- A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems S. Pfreundschuh et al. 10.5194/amt-11-4627-2018
- 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
- 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
- Cloud-Top Height Comparison from Multi-Satellite Sensors and Ground-Based Cloud Radar over SACOL Site X. Yang et al. 10.3390/rs13142715
- Detecting Multilayer Clouds From the Geostationary Advanced Himawari Imager Using Machine Learning Techniques Z. Tan et al. 10.1109/TGRS.2021.3087714
- 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
- Comparison of the Novel Probabilistic Self-Optimizing Vectorized Earth Observation Retrieval Classifier with Common Machine Learning Algorithms J. Musial & J. Bojanowski 10.3390/rs14020378
- 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
- CERES MODIS Cloud Product Retrievals for Edition 4—Part II: Comparisons to CloudSat and CALIPSO C. Yost et al. 10.1109/TGRS.2020.3015155
- 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
- Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model G. Wang et al. 10.3390/atmos12020173
- A simulator for the CLARA-A2 cloud climate data record and its application to assess EC-Earth polar cloudiness S. Eliasson et al. 10.5194/gmd-13-297-2020
- Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms M. Min et al. 10.1016/j.rse.2019.111616
- Estimate of daytime single-layer cloud base height from advanced baseline imager measurements H. Lin et al. 10.1016/j.rse.2022.112970
Latest update: 15 May 2022
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...
