Articles | Volume 16, issue 13
https://doi.org/10.5194/acp-16-8181-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-16-8181-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
07 Jul 2016
Research article |
| 07 Jul 2016
Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table
Jani Huttunen
Finnish Meteorological Institute (FMI), Atmospheric
Research Centre of Eastern Finland, Kuopio, Finland
Department of Applied Physics, University of Eastern
Finland, Kuopio, Finland
Harri Kokkola
Finnish Meteorological Institute (FMI), Atmospheric
Research Centre of Eastern Finland, Kuopio, Finland
Tero Mielonen
Finnish Meteorological Institute (FMI), Atmospheric
Research Centre of Eastern Finland, Kuopio, Finland
Mika Esa Juhani Mononen
Independent researcher, Kuopio, Finland
Antti Lipponen
Finnish Meteorological Institute (FMI), Atmospheric
Research Centre of Eastern Finland, Kuopio, Finland
Department of Applied Physics, University of Eastern
Finland, Kuopio, Finland
Juha Reunanen
Tomaattinen Oy, Helsinki, Finland
Anders Vilhelm Lindfors
Finnish Meteorological Institute (FMI), Atmospheric
Research Centre of Eastern Finland, Kuopio, Finland
Santtu Mikkonen
Department of Applied Physics, University of Eastern
Finland, Kuopio, Finland
Kari Erkki Juhani Lehtinen
Finnish Meteorological Institute (FMI), Atmospheric
Research Centre of Eastern Finland, Kuopio, Finland
Department of Applied Physics, University of Eastern
Finland, Kuopio, Finland
Natalia Kouremeti
Physikalisch-Meteorologisches Observatorium Davos,
Dorfstrasse 33, 7260 Davos Dorf, Switzerland
Aristotle University of Thessaloniki, Laboratory of
Atmospheric Physics, Thessaloniki, 54124, Greece
Alkiviadis Bais
Aristotle University of Thessaloniki, Laboratory of
Atmospheric Physics, Thessaloniki, 54124, Greece
Harri Niska
Department of Environmental and Biological Sciences,
University of Eastern Finland, Kuopio, Finland
Finnish Meteorological Institute (FMI), Atmospheric
Research Centre of Eastern Finland, Kuopio, Finland
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Cited
22 citations as recorded by crossref.
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- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020
- Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method X. Chen et al. 10.1016/j.rse.2020.112006
- Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia E. Kang et al. 10.1016/j.atmosenv.2023.119951
- Retrieval of Atmospheric Aerosol Optical Depth From AVHRR Over Land With Global Coverage Using Machine Learning Method X. Tian et al. 10.1109/TGRS.2021.3129853
- A Two-Stage Machine Learning Algorithm for Retrieving Multiple Aerosol Properties Over Land: Development and Validation M. Cao et al. 10.1109/TGRS.2023.3307934
- Calculating the aerosol asymmetry factor based on measurements from the humidified nephelometer system G. Zhao et al. 10.5194/acp-18-9049-2018
- MODIS aerosol optical depth retrieval based on random forest approach T. Liang et al. 10.1080/2150704X.2020.1842540
- A Machine Learning Approach to Derive Aerosol Properties from All-Sky Camera Imagery F. Scarlatti et al. 10.3390/rs15061676
- A Machine Learning Approach to Retrieving Aerosol Optical Depth Using Solar Radiation Measurements S. Logothetis et al. 10.3390/rs16071132
- MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach M. Pashayi et al. 10.1016/j.apr.2024.102128
- A Neural Network Correction to the Scalar Approximation in Radiative Transfer P. Castellanos & A. da Silva 10.1175/JTECH-D-18-0003.1
- Retrieving High Temporal Resolution Aerosol Layer Height From EPIC/DSCOVR Using Machine Learning Method X. Tian et al. 10.1109/TGRS.2024.3405186
- Inversion of Aerosol Optical Depth: Incorporating Multimodel Approach X. Sun et al. 10.1109/TGRS.2024.3397315
- Identification of new particle formation events with deep learning J. Joutsensaari et al. 10.5194/acp-18-9597-2018
- Two-stage estimation of hourly diffuse solar radiation across China using end-to-end gradient boosting with sequentially boosted features L. Chen et al. 10.1016/j.rse.2024.114445
- Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia Y. Kang et al. 10.1016/j.isprsjprs.2021.11.016
- Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network L. Qi et al. 10.3390/rs14246341
- Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements X. Chen et al. 10.3390/rs14040980
- Short-term aerosol radiative effects and their regional difference during heavy haze episodes in January 2013 in China X. Cheng et al. 10.1016/j.atmosenv.2017.06.040
- Estimation of Aerosol Optical Depth at 30 m Resolution Using Landsat Imagery and Machine Learning T. Liang et al. 10.3390/rs14051053
- Reconstruction of historical aerosol optical depth time series over Romania during summertime A. Dumitrescu et al. 10.1002/joc.5118
22 citations as recorded by crossref.
- A hybrid method for reconstructing the historical evolution of aerosol optical depth from sunshine duration measurements W. Wandji Nyamsi et al. 10.5194/amt-13-3061-2020
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020
- Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method X. Chen et al. 10.1016/j.rse.2020.112006
- Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia E. Kang et al. 10.1016/j.atmosenv.2023.119951
- Retrieval of Atmospheric Aerosol Optical Depth From AVHRR Over Land With Global Coverage Using Machine Learning Method X. Tian et al. 10.1109/TGRS.2021.3129853
- A Two-Stage Machine Learning Algorithm for Retrieving Multiple Aerosol Properties Over Land: Development and Validation M. Cao et al. 10.1109/TGRS.2023.3307934
- Calculating the aerosol asymmetry factor based on measurements from the humidified nephelometer system G. Zhao et al. 10.5194/acp-18-9049-2018
- MODIS aerosol optical depth retrieval based on random forest approach T. Liang et al. 10.1080/2150704X.2020.1842540
- A Machine Learning Approach to Derive Aerosol Properties from All-Sky Camera Imagery F. Scarlatti et al. 10.3390/rs15061676
- A Machine Learning Approach to Retrieving Aerosol Optical Depth Using Solar Radiation Measurements S. Logothetis et al. 10.3390/rs16071132
- MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach M. Pashayi et al. 10.1016/j.apr.2024.102128
- A Neural Network Correction to the Scalar Approximation in Radiative Transfer P. Castellanos & A. da Silva 10.1175/JTECH-D-18-0003.1
- Retrieving High Temporal Resolution Aerosol Layer Height From EPIC/DSCOVR Using Machine Learning Method X. Tian et al. 10.1109/TGRS.2024.3405186
- Inversion of Aerosol Optical Depth: Incorporating Multimodel Approach X. Sun et al. 10.1109/TGRS.2024.3397315
- Identification of new particle formation events with deep learning J. Joutsensaari et al. 10.5194/acp-18-9597-2018
- Two-stage estimation of hourly diffuse solar radiation across China using end-to-end gradient boosting with sequentially boosted features L. Chen et al. 10.1016/j.rse.2024.114445
- Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia Y. Kang et al. 10.1016/j.isprsjprs.2021.11.016
- Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network L. Qi et al. 10.3390/rs14246341
- Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements X. Chen et al. 10.3390/rs14040980
- Short-term aerosol radiative effects and their regional difference during heavy haze episodes in January 2013 in China X. Cheng et al. 10.1016/j.atmosenv.2017.06.040
- Estimation of Aerosol Optical Depth at 30 m Resolution Using Landsat Imagery and Machine Learning T. Liang et al. 10.3390/rs14051053
- Reconstruction of historical aerosol optical depth time series over Romania during summertime A. Dumitrescu et al. 10.1002/joc.5118
Saved (preprint)
Latest update: 23 Nov 2024
Short summary
For a good estimate of the current forcing by anthropogenic aerosols, knowledge in past is needed. One option to lengthen time series is to retrieve aerosol optical depth from solar radiation measurements. We have evaluated several methods for this task. Most of the methods produce aerosol optical depth estimates with a good accuracy. However, machine learning methods seem to be the most applicable not to produce any systematic biases, since they do not need constrain the aerosol properties.
For a good estimate of the current forcing by anthropogenic aerosols, knowledge in past is...
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