Comparative analysis of data fusion and merging algorithms for the prediction of aerosol optical depth

2019 ◽  
Author(s):  
Ni-Bin Chang ◽  
Xiaoli Wei ◽  
Kaixu Bai ◽  
Wei Gao
Keyword(s):  
Comparative Analysis ◽  
Data Fusion ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Merging Algorithms

scholarly journals Integration of GOCI and AHI Yonsei Aerosol Optical Depth Products During the 2016 KORUS-AQ and 2018 EMeRGe Campaigns

2020 ◽  
Author(s):  
Hyunkwang Lim ◽  
Sujung Go ◽  
Jhoon Kim ◽  
Myungje Choi ◽  
Seoyoung Lee ◽  
...  
Keyword(s):  
Data Fusion ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Near Infrared ◽  
Likelihood Estimation ◽  
Low Earth Orbit ◽  
Estimation Methods ◽  
Bias Error ◽  
Low Earth Orbit Satellites ◽  
Shortwave Infrared

Abstract. The Yonsei AErosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 minutes AHI or 1 hour GOCI data at 6 km × 6 km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), and bias correction based on normalized difference vegetation indexes; and (2) estimation of the fused product using ensemble-mean and maximum-likelihood estimation methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error, median bias error than the retrieved result for each product.

scholarly journals Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns

2021 ◽  
Vol 14 (6) ◽  
pp. 4575-4592
Author(s):  
Hyunkwang Lim ◽  
Sujung Go ◽  
Jhoon Kim ◽  
Myungje Choi ◽  
Seoyoung Lee ◽  
...  
Keyword(s):  
Data Fusion ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Near Infrared ◽  
Likelihood Estimation ◽  
Low Earth Orbit ◽  
Bias Error ◽  
Ensemble Mean ◽  
Low Earth Orbit Satellites ◽  
Shortwave Infrared

Abstract. The Yonsei Aerosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 min AHI or 1 h GOCI data at 6 km×6 km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-Earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), as well as bias correction based on normalized difference vegetation indexes, and (2) compilation of the fused product using ensemble-mean and maximum-likelihood estimation (MLE) methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error (RMSE), and median bias error than the retrieved result for each product. If the RMSE and mean AOD bias values used for MLE fusion are correct, the MLE fused products show better accuracy, but the ensemble-mean products can still be useful as MLE.

The Impact of Dust Storms on Aerosol Optical Depth and Radiative Forcing

2020 ◽  
Vol 16 (1) ◽  
pp. 1-14
Author(s):  
Monim Jiboori ◽  
Nadia Abed ◽  
Mohamed Abdel Wahab
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Radiative Forcing ◽  
Dust Storms ◽  
The Impact

Aerosol optical depth at ALOMAR Observatory (Andøya, Norway) in summer 2002 and 2003

Tellus B ◽  
2006 ◽  
Vol 58 (3) ◽  
Author(s):  
Carlos Toledano ◽  
Victoria Cachorro ◽  
Alberto Berjón ◽  
Mar Sorribas ◽  
Ricardo Vergaz ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth

Impact of aerosol on air temperature in Baghdad

2017 ◽  
Vol 2 (6) ◽  
Author(s):  
Yaseen Kadhim Abbas Al-Timimi ◽  
Ali Challob Khraibet
Keyword(s):  
Temperature Variation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Air Temperature ◽  
Strong Correlation ◽  
Imaging Spectrometer ◽  
Moderate Resolution ◽  
Resolution Imaging

Aerosol Optical Depth (AOD) is the measure of aerosol distributed with a Column of air from earth’s surface to the top of atmosphere, in this study, temperature variation of aerosol optical depth (AOD) in Baghdad was analyzed Moderate Resolution Imaging Spectrometer (MODIS) from Terra and its relationship with temperature for the period 2003 – 2015 were examined. The highest values for mean seasonal AOD were observed in spring and summer and the maximum AOD values ranged from 0.50 to 0.58 by contrast minimum AOD values ranging from 0.30 to 0.41 were found in winter and autumn. Results of study also showed that the temperature (max., min., mean air temperature and DTR) have a strong correlation with AOD (0.82, 0.83, 0.82 and 0.65) respectively.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

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