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 et al.
Total article views: 2,185 (including HTML, PDF, and XML)Cumulative views and downloads (calculated since 25 Jan 2016)Views and downloads (calculated since 25 Jan 2016)
10 citations as recorded by crossref.
- Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network W. Qin et al. 10.3390/rs10071022
- MODIS aerosol optical depth retrieval based on random forest approach T. Liang et al. 10.1080/2150704X.2020.1842540
- 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
- A Neural Network Correction to the Scalar Approximation in Radiative Transfer P. Castellanos & A. da Silva 10.1175/JTECH-D-18-0003.1
- 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
- Identification of new particle formation events with deep learning J. Joutsensaari et al. 10.5194/acp-18-9597-2018
- Calculating the aerosol asymmetry factor based on measurements from the humidified nephelometer system G. Zhao et al. 10.5194/acp-18-9049-2018
- 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
- Reconstruction of historical aerosol optical depth time series over Romania during summertime A. Dumitrescu et al. 10.1002/joc.5118
Latest update: 21 Apr 2021