Articles | Volume 7, issue 5
https://doi.org/10.5194/amt-7-1331-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-7-1331-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
19 May 2014
Research article |
| 19 May 2014
Evaluation of SCIAMACHY Oxygen A band cloud heights using Cloudnet measurements
P. Wang
Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
P. Stammes
Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
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Cited
16 citations as recorded by crossref.
- Evaluation of cloud height, optical thickness, and phase retrievals from the CHROMA algorithm applied to Sentinel-3 OLCI data A. Sayer et al. https://doi.org/10.5194/amt-18-6681-2025
- A Simple Approximation for the Reflectance of a Thick Cloud in Gaseous Absorption Band and Its Application for the Cloud-Top Height Determination A. Kokhanovsky et al. https://doi.org/10.1109/TGRS.2018.2883358
- Cloud parameter retrieval for Environmental trace gases Monitoring Instrument (EMI) using the O2-O2 absorption band T. Yang et al. https://doi.org/10.1016/j.atmosres.2022.106597
- Evaluation of the operational Aerosol Layer Height retrieval algorithm for Sentinel-5 Precursor: application to O2 A band observations from GOME-2A A. Sanders et al. https://doi.org/10.5194/amt-8-4947-2015
- First results of cloud retrieval from the Geostationary Environmental Monitoring Spectrometer B. Kim et al. https://doi.org/10.5194/amt-17-453-2024
- Effects of clouds on the UV Absorbing Aerosol Index from TROPOMI M. Kooreman et al. https://doi.org/10.5194/amt-13-6407-2020
- Cloud parameter retrieval for EMI-series instruments from the O2–O2 band using a deep learning approach (ECRAD) T. Yang et al. https://doi.org/10.1016/j.atmosenv.2026.122272
- Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds J. Yang et al. https://doi.org/10.3390/rs12142252
- Aerosol and cloud retrieval algorithm of TANSO-3/GOSAT-GW: theoretical basis and validations during the 2024 ASIA-AQ campaign using the TROPOMI observations as a testbed H. Lim et al. https://doi.org/10.1186/s40645-026-00820-z
- Improvements to the OMI O2–O2 operational cloud algorithm and comparisons with ground-based radar–lidar observations J. Veefkind et al. https://doi.org/10.5194/amt-9-6035-2016
- FRESCO-B: a fast cloud retrieval algorithm using oxygen B-band measurements from GOME-2 M. Desmons et al. https://doi.org/10.5194/amt-12-2485-2019
- Detection of Water Bodies from AVHRR Data—A TIMELINE Thematic Processor A. Dietz et al. https://doi.org/10.3390/rs9010057
- Evaluation of SCIAMACHY ESA/DLR Cloud Parameters Version 5.02 by Comparisons to Ground-Based and Other Satellite Data L. Lelli et al. https://doi.org/10.3389/fenvs.2016.00043
- Improved aerosol correction for OMI tropospheric NO2 retrieval over East Asia: constraint from CALIOP aerosol vertical profile M. Liu et al. https://doi.org/10.5194/amt-12-1-2019
- Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O2–O2, MODIS, and Suomi-NPP VIIRS S. Compernolle et al. https://doi.org/10.5194/amt-14-2451-2021
- Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach—A Part of the TIMELINE Preprocessor A. Dietz et al. https://doi.org/10.3390/rs9040303
16 citations as recorded by crossref.
- Evaluation of cloud height, optical thickness, and phase retrievals from the CHROMA algorithm applied to Sentinel-3 OLCI data A. Sayer et al. https://doi.org/10.5194/amt-18-6681-2025
- A Simple Approximation for the Reflectance of a Thick Cloud in Gaseous Absorption Band and Its Application for the Cloud-Top Height Determination A. Kokhanovsky et al. https://doi.org/10.1109/TGRS.2018.2883358
- Cloud parameter retrieval for Environmental trace gases Monitoring Instrument (EMI) using the O2-O2 absorption band T. Yang et al. https://doi.org/10.1016/j.atmosres.2022.106597
- Evaluation of the operational Aerosol Layer Height retrieval algorithm for Sentinel-5 Precursor: application to O2 A band observations from GOME-2A A. Sanders et al. https://doi.org/10.5194/amt-8-4947-2015
- First results of cloud retrieval from the Geostationary Environmental Monitoring Spectrometer B. Kim et al. https://doi.org/10.5194/amt-17-453-2024
- Effects of clouds on the UV Absorbing Aerosol Index from TROPOMI M. Kooreman et al. https://doi.org/10.5194/amt-13-6407-2020
- Cloud parameter retrieval for EMI-series instruments from the O2–O2 band using a deep learning approach (ECRAD) T. Yang et al. https://doi.org/10.1016/j.atmosenv.2026.122272
- Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds J. Yang et al. https://doi.org/10.3390/rs12142252
- Aerosol and cloud retrieval algorithm of TANSO-3/GOSAT-GW: theoretical basis and validations during the 2024 ASIA-AQ campaign using the TROPOMI observations as a testbed H. Lim et al. https://doi.org/10.1186/s40645-026-00820-z
- Improvements to the OMI O2–O2 operational cloud algorithm and comparisons with ground-based radar–lidar observations J. Veefkind et al. https://doi.org/10.5194/amt-9-6035-2016
- FRESCO-B: a fast cloud retrieval algorithm using oxygen B-band measurements from GOME-2 M. Desmons et al. https://doi.org/10.5194/amt-12-2485-2019
- Detection of Water Bodies from AVHRR Data—A TIMELINE Thematic Processor A. Dietz et al. https://doi.org/10.3390/rs9010057
- Evaluation of SCIAMACHY ESA/DLR Cloud Parameters Version 5.02 by Comparisons to Ground-Based and Other Satellite Data L. Lelli et al. https://doi.org/10.3389/fenvs.2016.00043
- Improved aerosol correction for OMI tropospheric NO2 retrieval over East Asia: constraint from CALIOP aerosol vertical profile M. Liu et al. https://doi.org/10.5194/amt-12-1-2019
- Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O2–O2, MODIS, and Suomi-NPP VIIRS S. Compernolle et al. https://doi.org/10.5194/amt-14-2451-2021
- Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach—A Part of the TIMELINE Preprocessor A. Dietz et al. https://doi.org/10.3390/rs9040303
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