Analysis of spatio-temporal variability of aerosol optical depth with empirical orthogonal functions in the Changjiang River Delta, China

2014 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Tianyong Zhai ◽  
Qing Zhao ◽  
Wei Gao ◽  
Runhe Shi ◽  
Weining Xiang ◽  
...  
2022 ◽  
Author(s):  
Samuel E. LeBlanc ◽  
Michal Segal-Rozenhaimer ◽  
Jens Redemann ◽  
Connor J. Flynn ◽  
Roy R. Johnson ◽  
...  

Abstract. Aerosol particles can be emitted, transported, removed, or transformed, leading to aerosol variability at scales impacting the climate (days to years and over hundreds of kilometers) or the air quality (hours to days and from meters to hundreds of kilometers). We present the temporal and spatial scales of changes in AOD (Aerosol Optical Depth), and aerosol size (using Angstrom Exponent; AE, and Fine-Mode-Fraction; FMF) over Korea during the 2016 KORUS-AQ (KORea-US Air Quality) atmospheric experiment. We use measurements and retrievals of aerosol optical properties from airborne instruments for remote sensing (4STAR; Spectrometers for Sky-Scanning Sun Tracking Atmospheric Research) and in situ (LARGE; NASA Langley Aerosol Research Group Experiment) on board the NASA DC-8, geostationary satellite (GOCI; Geostationary Ocean Color Imager; Yonsei aerosol retrieval (YAER) version 2) and reanalysis (MERRA-2; Modern-Era Retrospective Analysis for Research and Applications, version 2). Measurements from 4STAR when flying below 500 m, show an average AOD at 501 nm of 0.43 and an average AE of 1.15 with large standard deviation (0.32 and 0.26 for AOD and AE respectively) likely due to mixing of different aerosol types (fine and coarse mode). The majority of AODs due to fine mode aerosol is observed at altitudes lower than 2 km. Even though there are large variations, for 18 out of the 20 flight days, the column AOD measurements by 4STAR along the NASA DC-8 flight trajectories matches the south-Korean regional average derived from GOCI. We also observed that, contrary to prevalent understanding, AE and FMF are more spatially variable than AOD during KORUS-AQ, even when accounting for potential sampling biases by using Monte Carlo resampling. Averaging between measurements and model for the entire KORUS-AQ period, a reduction in correlation by 15 % is 65.0 km for AOD and shorter at 22.7 km for AE. While there are observational and model differences, the predominant factor influencing spatial-temporal homogeneity is the meteorological period. High spatio-temporal variability occur during the dynamic period (25–31 May), and low spatio-temporal variability occur during blocking Rex pattern (01–07 June). The changes in spatial variability scales between AOD and FMF/AE, while inter-related, indicate that microphysical processes that impact mostly the dominant aerosol size, like aerosol particle formation, growth, and coagulation, vary at shorter scales than the aerosol concentration processes that mostly impact AOD, like aerosol emission, transport, and removal.


2001 ◽  
Vol 44 (S1) ◽  
pp. 87-91 ◽  
Author(s):  
Kazuaki Hori ◽  
Yoshiki Saito ◽  
Quanhong Zhao ◽  
Pinxian Wang ◽  
Congxian Li

2003 ◽  
Vol 13 (4) ◽  
pp. 289-299
Author(s):  
Shu-peng Chen ◽  
Cheng-hu Zhou ◽  
Qiu-xiao Chen

2020 ◽  
Vol 12 (14) ◽  
pp. 2256
Author(s):  
Sang Seo Park ◽  
Sang-Woo Kim ◽  
Chang-Keun Song ◽  
Jong-Uk Park ◽  
Kang-Ho Bae

In this study, the spatio-temporal variability of aerosol optical depth (AOD), total column ozone (TCO), and total column NO2 (TCN) was identified over East Asia using long-term datasets from ground-based and satellite observations. Based on the statistical results, optimized spatio-temporal ranges for the validation study were determined with respect to the target materials. To determine both spatial and temporal ranges for the validation study, we confirmed that the observed datasets can be statistically considered as the same quantity within the ranges. Based on the thresholds of R2>0.95 (temporal) and R>0.95 (spatial), the basic ranges for spatial and temporal scales for AOD validation was within 30 km and 30 min, respectively. Furthermore, the spatial scales for AOD validation showed seasonal variation, which expanded the range to 40 km in summer and autumn. Because of the seasonal change of latitudinal gradient of the TCO, the seasonal variation of the north-south range is a considerable point. For the TCO validation, the north-south range is varied from 0.87° in spring to 1.05° in summer. The spatio-temporal range for TCN validation was 20 min (temporal) and 20–50 km (spatial). However, the nearest value of satellite data was used in the validation because the spatio-temporal variation of TCN is large in summer and autumn. Estimation of the spatio-temporal variability for respective pollutants may contribute to improving the validation of satellite products.


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