Global and regional evaluation of over-land spectral aerosol optical depth retrievals from SeaWiFS

Sayer, A. M.; Hsu, N. Christina; Bettenhausen, C.; Jeong, Manhee; Holben, B. N.; Zhang, J.

doi:10.5194/amt-5-1761-2012

Cited by 121 publications

(95 citation statements)

References 52 publications

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“…A detailed validation of the global SeaWiFS AOD using the collocated v003 Level 2 products against cloud-screened and quality-assured Level 2 AERONET measurements (Holben et al, 1998;Smirnov et al, 2000) and other satellite products is presented for land in Sayer et al (2012a) and for ocean in Sayer et al (2012b). Briefly, on a global basis, over one standard deviation (68 %) of AOD matchups were found to agree within an absolute expected error of 0.03 + 15 % over ocean and 0.05 + 20 % over land at 550 nm.…”

Section: Satellite Data and Their Uncertainties For Trend Analysismentioning

confidence: 99%

Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010

et al. 2012

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Abstract. Both sensor calibration and satellite retrieval algorithm play an important role in the ability to determine accurately long-term trends from satellite data. Owing to the unprecedented accuracy and long-term stability of its radiometric calibration, SeaWiFS measurements exhibit minimal uncertainty with respect to sensor calibration. In this study, we take advantage of this well-calibrated set of measurements by applying a newly-developed aerosol optical depth (AOD) retrieval algorithm over land and ocean to investigate the distribution of AOD, and to identify emerging patterns and trends in global and regional aerosol loading during its 13-yr mission. Our correlation analysis between climatic indices (such as ENSO) and AOD suggests strong relationships for Saharan dust export as well as biomass-burning activity in the tropics, associated with large-scale feedbacks. The results also indicate that the averaged AOD trend over global ocean is weakly positive from 1998 to 2010 and comparable to that observed by MODIS but opposite in sign to that observed by AVHRR during overlapping years. On regional scales, distinct tendencies are found for different regions associated with natural and anthropogenic aerosol emission and transport. For example, large upward trends are found over the Arabian Peninsula that indicate a strengthening of the seasonal cycle of dust emission and transport processes over the whole region as well as over downwind oceanic regions. In contrast, a negative-neutral tendency is observed over the desert/arid Saharan region as well as in the associated dust outflow over the north Atlantic. Additionally, we found decreasing trends over the eastern US and Europe, and increasing trends over countries such as China and India that are experiencing rapid economic development. In general, these results are consistent with those derived from ground-based AERONET measurements.

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Section: Satellite Data and Their Uncertainties For Trend Analysismentioning

confidence: 99%

Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010

et al. 2012

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“…The over-land retrieval uses the "Deep Blue" algorithm [Hsu et al, 2004[Hsu et al, , 2006Sayer et al, 2012b], which is part of the MODIS processing. The accuracy of the AOD retrieval at 550 nm is reported to be 0.05 ± 0.2× (AERONET AOD) over land [Sayer et al, 2012b] and 0.03 ± 0.15× (AERONET AOD) over ocean [Sayer et al, 2012a].…”

Section: Seawifsmentioning

confidence: 99%

“…The correlation between the PCs is well above 0.9 and those between the EOFs are mostly above 0.8 (Tables 1 and 2) for the two MODIS and MISR. The relatively lower correlation between SeaWiFS and the other data sets may arise from the AOD threshold value specified in its retrieval algorithm [Sayer et al, 2012b] and may be related to some particular regions and aerosol types as discussed in section 4.2.…”

Section: Analysis Of Aod Datamentioning

confidence: 99%

Application of spectral analysis techniques in the intercomparison of aerosol data: 1. An EOF approach to analyze the spatial‐temporal variability of aerosol optical depth using multiple remote sensing data sets

Carlson

Lacis

2013

JGR Atmospheres

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Many remote sensing techniques and passive sensors have been developed to measure global aerosol properties. While instantaneous comparisons between pixel‐level data often reveal quantitative differences, here we use Empirical Orthogonal Function (EOF) analysis, also known as Principal Component Analysis, to demonstrate that satellite‐derived aerosol optical depth (AOD) data sets exhibit essentially the same spatial and temporal variability and are thus suitable for large‐scale studies. Analysis results show that the first four EOF modes of AOD account for the bulk of the variance and agree well across the four data sets used in this study (i.e., Aqua MODIS, Terra MODIS, MISR, and SeaWiFS). Only SeaWiFS data over land have slightly different EOF patterns. Globally, the first two EOF modes show annual cycles and are mainly related to Sahara dust in the northern hemisphere and biomass burning in the southern hemisphere, respectively. After removing the mean seasonal cycle from the data, major aerosol sources, including biomass burning in South America and dust in West Africa, are revealed in the dominant modes due to the different interannual variability of aerosol emissions. The enhancement of biomass burning associated with El Niño over Indonesia and central South America is also captured with the EOF technique.

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“…Aerosols are also linked to respiratory illnesses (e.g., Pope and Dockery, 2006) and meningitis epidemics (e.g., Deroubaix et al, 2013). Since the global aerosol distribution shows high spatial and temporal variability, many studies have developed aerosol retrieval algorithms utilizing both low Earth orbit satellite measurements (Hsu et al, 2004;Kim et al, 2007;Torres et al, 2007;Kahn et al, 2010;Lyapustin et al, 2011b;von HoyningenHuene et al, 2011;Wong et al, 2010;Bevan et al, 2012;Sayer et al, 2012;Levy et al, 2013) and geostationary orbit (GEO) satellite measurements (Knapp et al, 2002(Knapp et al, , 2005Wang et al, 2003;Urm and Sohn, 2005;Yoon et al, 2007;Kim et al, 2008;Lee et al, 2010;Zhang et al, 2011;. These studies have typically adopted an inversion approach, using a precalculated look-up table (LUT) based on assumed aerosol optical properties (AOPs) to retrieve aerosol information from the measured visible reflectance at the top of the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS)

Kim

Jeong

et al. 2016

Atmos. Chem. Phys.

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Abstract. An aerosol model optimized for northeast Asia is updated with the inversion data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-northeast (NE) Asia campaign which was conducted during spring from March to May 2012. This updated aerosol model was then applied to a single visible channel algorithm to retrieve aerosol optical depth (AOD) from a Meteorological Imager (MI) on-board the geostationary meteorological satellite, Communication, Ocean, and Meteorological Satellite (COMS). This model plays an important role in retrieving accurate AOD from a single visible channel measurement. For the single-channel retrieval, sensitivity tests showed that perturbations by 4 % (0.926 ± 0.04) in the assumed single scattering albedo (SSA) can result in the retrieval error in AOD by over 20 %. Since the measured reflectance at the top of the atmosphere depends on both AOD and SSA, the overestimation of assumed SSA in the aerosol model leads to an underestimation of AOD. Based on the AErosol RObotic NETwork (AERONET) inversion data sets obtained over East Asia before 2011, seasonally analyzed aerosol optical properties (AOPs) were categorized by SSAs at 675 nm of 0.92 ± 0.035 for spring (March, April, and May). After the DRAGON-NE Asia campaign in 2012, the SSA during spring showed a slight increase to 0.93 ± 0.035. In terms of the volume size distribution, the mode radius of coarse particles was increased from 2.08 ± 0.40 to 2.14 ± 0.40. While the original aerosol model consists of volume size distribution and refractive indices obtained before 2011, the new model is constructed by using a total data set after the DRAGON-NE Asia campaign. The large volume of data in high spatial resolution from this intensive campaign can be used to improve the representative aerosol model for East Asia. Accordingly, the new AOD data sets retrieved from a single-channel algorithm, which uses a precalculated look-up table (LUT) with the new aerosol model, show an improved correlation with the measured AOD during the DRAGON-NE Asia campaign. The correlation between the new AOD and AERONET value shows a regression slope of 1.00, while the comparison of the original AOD data retrieved using the original aerosol model shows a slope of 1.08. The change of y-offset is not significant, and the correlation coefficients for the comparisons of the original and new AOD are 0.87Published by Copernicus Publications on behalf of the European Geosciences Union. M. Kim et al.: AOD retrieval using GEO measurement during DRAGON-NE Asia 2012and 0.85, respectively. The tendency of the original aerosol model to overestimate the retrieved AOD is significantly improved by using the SSA values in addition to size distribution and refractive index obtained using the new model.

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Global and regional evaluation of over-land spectral aerosol optical depth retrievals from SeaWiFS

Cited by 121 publications

References 52 publications

Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010

Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010

Application of spectral analysis techniques in the intercomparison of aerosol data: 1. An EOF approach to analyze the spatial‐temporal variability of aerosol optical depth using multiple remote sensing data sets

Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS)

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