A semi-empirical optical data fusion technique for merging aerosol optical depth over China

2012 ◽  
Author(s):  
Hui Xu ◽  
Yong Xue ◽  
Jie Guang ◽  
Yingjie Li ◽  
Leiku Yang ◽  
...  
Keyword(s):  
Data Fusion ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Optical Data ◽  
Fusion Technique ◽  
Semi Empirical

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 Aerosol characterization from active and passive ground measurements and satellite data

2018 ◽  
Vol 176 ◽  
pp. 08014
Author(s):  
Dandocsi Alexandru ◽  
Sapartoc Georgiana ◽  
Preda Liliana ◽  
Stan Cristina ◽  
Radu Cristian
Keyword(s):  
Time Series ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Satellite Data ◽  
Satellite Observations ◽  
Optical Data ◽  
Sun Photometer ◽  
Ground Based Lidar ◽  
One Year ◽  
Aerosol Characterization

One year records of AErosolROboticNEtwork (AERONET) sun photometer measurements were analyzed to investigate the seasonal and daily variations of columnar aerosol optical depth. Some irregularities of this time series are associated with aerosol intrusions. The aerosol layers indicated by these irregularities are identified and characterized using the extensive optical data from coincident CALIPSO satellite observations and ground based LIDAR.

scholarly journals A Simple Semi-Empirical Model for the Estimation of Photosynthetically Active Radiation from Satellite Data in the Tropics

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
S. Janjai ◽  
A. Sripradit ◽  
R. Wattan ◽  
S. Buntoung ◽  
S. Pattarapanitchai ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Empirical Model ◽  
Photosynthetically Active Radiation ◽  
Total Ozone ◽  
Data Set ◽  
Active Radiation ◽  
Total Ozone Column ◽  
Semi Empirical ◽  
Ozone Column

This paper presents a simple semi-empirical model for estimating global photosynthetically active radiation (PAR) under all sky conditions. The model expresses PAR as a function of cloud index, aerosol optical depth, total ozone column, solar zenith angle, and air mass. The formulation of the model was based on a four-year period (2008–2011) of PAR data obtained from the measurements at four solar monitoring stations in a tropical environment of Thailand. These are Chiang Mai (18.78°N, 98.98°E), Ubon Ratchathani (15.25°N, 104.87°E), Nakhon Pathom (13.82°N, 100.04°E), and Songkhla (7.20°N, 100.60°E). The cloud index was derived from MTSAT-1R satellite, whereas the aerosol optical depth was obtained from MODIS/Terra satellite. For the total ozone column, it was retrieved from OMI/Aura satellite. The model was validated against independent data set from the four stations. It was found that hourly PAR estimated from the proposed model and that obtained from the measurements were in reasonable agreement, with the root mean square difference (RMSD) and mean bias difference (MBD) of 14.3% and −5.8%, respectively. In addition, for the case of monthly average hourly PAR, RMSD and MBD were reduced to 11.1% and −5.1%, respectively.

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.

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