Regional and local variations in atmospheric aerosols using ground-based sun photometry during Distributed Regional Aerosol Gridded Observation Networks (DRAGON) in 2012

Sano, Itaru; Mukai, Sonoyo; Nakata, Makiko; Holben, B. N.

doi:10.5194/acp-16-14795-2016

Cited by 13 publications

(15 citation statements)

References 23 publications

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“…Osaka is a megacity of very dense urban development that is bounded by low mountains on three sides and Osaka Bay to the south (see Sano et al, 2016). Industry and transportation emissions are sources of the dominant background aerosol loading, and, as in Seoul, episodic coarsemode dust and transported fine-mode industrial aerosols were observed during the 4-month intensive measurement period.…”

Section: Dragon-japanmentioning

confidence: 99%

“…The long-range trans-boundary aerosols during this period were shown to follow the NCEPderived 700 to 850 mb wind vectors. Sano et al (2016) investigated the variability in AOD under clean and polluted days in Osaka using DRAGON network measurements. They also detailed aerosol transportation over the city using high spatial-and temporal-resolution measurements by DRAGON-Osaka.…”

Section: Observations Of Aerosols Above Clouds and Their Interactionsmentioning

confidence: 99%

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An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks

et al. 2018

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Abstract. Over the past 24 years, the AErosol RObotic NETwork (AERONET) program has provided highly accurate remote-sensing characterization of aerosol optical and physical properties for an increasingly extensive geographic distribution including all continents and many oceanic island and coastal sites. The measurements and retrievals from the AERONET global network have addressed satellite and model validation needs very well, but there have been challenges in making comparisons to similar parameters from in situ surface and airborne measurements. Additionally, with improved spatial and temporal satellite remote sensing of aerosols, there is a need for higher spatial-resolution ground-based remote-sensing networks. An effort to address these needs resulted in a number of field campaign networks called Distributed Regional Aerosol Gridded Observation Networks (DRAGONs) that were designed to provide a database for in situ and remote-sensing comparison and analysis of local to mesoscale variability in aerosol properties. This paper describes the DRAGON deployments that will continue to contribute to the growing body of research related to meso-and microscale aerosol features and processes. The research presented in this special issue illustrates the diversity of topics that has resulted from the application of data from these networks.

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Section: Dragon-japanmentioning

confidence: 99%

Section: Observations Of Aerosols Above Clouds and Their Interactionsmentioning

confidence: 99%

An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks

et al. 2018

Self Cite

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“…They analyzed the spatial representativeness of individual AERONET sites and found that this depends on the season and the dominant aerosol type. Lee and Son (2016) and Sano et al (2016) studied the variability of AERONET aerosol optical properties during the DRAGON-Asia campaign in 2012. Sano et al (2016) concluded that due to the high variability in AOD, the ground-based measurements should be more frequent, and the satellite retrievals should have a finer resolution for a proper comparison.…”

Section: T H Virtanen Et Al: Collocation Mismatch Uncertaintiesmentioning

confidence: 99%

“…Lee and Son (2016) and Sano et al (2016) studied the variability of AERONET aerosol optical properties during the DRAGON-Asia campaign in 2012. Sano et al (2016) concluded that due to the high variability in AOD, the ground-based measurements should be more frequent, and the satellite retrievals should have a finer resolution for a proper comparison. Xiao et al (2016) compared AOD retrievals from several satellites to data from the DRAGON-Asia campaign and handheld sun photometers and concluded that the satellite products are better at tracking the day-to-day variability than tracking the spatial variability.…”

Section: T H Virtanen Et Al: Collocation Mismatch Uncertaintiesmentioning

confidence: 99%

Collocation mismatch uncertainties in satellite aerosol retrieval validation

et al. 2018

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Abstract. Satellite-based aerosol products are routinely validated against ground-based reference data, usually obtained from sun photometer networks such as AERONET (AEROsol RObotic NETwork). In a typical validation exercise a spatial sample of the instantaneous satellite data is compared against a temporal sample of the point-like ground-based data. The observations do not correspond to exactly the same column of the atmosphere at the same time, and the representativeness of the reference data depends on the spatiotemporal variability of the aerosol properties in the samples. The associated uncertainty is known as the collocation mismatch uncertainty (CMU). The validation results depend on the sampling parameters. While small samples involve less variability, they are more sensitive to the inevitable noise in the measurement data. In this paper we study systematically the effect of the sampling parameters in the validation of AATSR (Advanced Along-Track Scanning Radiometer) aerosol optical depth (AOD) product against AERONET data and the associated collocation mismatch uncertainty. To this end, we study the spatial AOD variability in the satellite data, compare it against the corresponding values obtained from densely located AERONET sites, and assess the possible reasons for observed differences.We find that the spatial AOD variability in the satellite data is approximately 2 times larger than in the ground-based data, and the spatial variability correlates only weakly with that of AERONET for short distances. We interpreted that only half of the variability in the satellite data is due to the natural variability in the AOD, and the rest is noise due to retrieval errors. However, for larger distances (∼ 0.5 • ) the correlation is improved as the noise is averaged out, and the day-to-day changes in regional AOD variability are well captured. Furthermore, we assess the usefulness of the spatial variability of the satellite AOD data as an estimate of CMU by comparing the retrieval errors to the total uncertainty estimates including the CMU in the validation. We find that accounting for CMU increases the fraction of consistent observations.

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“…They analyzed the spatial representativeness of individual AERONET sites, and found that this depends on the season 5 and the dominant aerosol type. Lee and Son (2016) and Sano et al (2016) studied the variability of AERONET aerosol optical properties during the DRAGON-Asia campaign in 2012. Sano et al (2016) concluded that due to the high variability in AOD, the ground based measurements should be more frequent, and the satellite retrievals should have a finer resolution for a proper comparison.…”

mentioning

confidence: 99%

Collocation Mismatch Uncertainties in Satellite Aerosol Retrieval Validation

Virtanen¹,

Kolmonen²,

Sogacheva³

et al. 2017

Preprint

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Abstract. Satellite based aerosol products are routinely validated against ground based reference data, usually obtained from sunphotometer networks such as AERONET (AEROsol Robotic Network). In a typical validation exercise a spatial sample of the instantaneous satellite data is compared against a temporal sample of the point-like ground based data. The observations do not correspond to exactly the same column of the atmosphere at the same time, and the representativiness of the reference data depends on the spatiotemporal variability of the aerosol properties in the samples. The associated uncertainty is known as 5 the collocation mismatch uncertainty (CMU). The validation results depend on the sampling parameters. While small samples involve less variability, they are more sensitive to the inevitable noise in the measurement data. In this paper we study systematically the effect of the sampling parameters in the validation of AATSR (Advanced Along Track Scanning Radiometer) aerosol optical depth (AOD) product against AERONET data and the associated collocation mismatch uncertainty. To this end, we study the spatial AOD variability in the satellite data, compare it against the corresponding values obtained from densely 10 located AERONET sites, and assess the possible reasons for observed differences.We find that the spatial AOD variability in the satellite data is approximately two times larger than in the ground based data, and the local AOD variability values correlate only weakly for short distances. We interprete that only half of the variability in the satellite data is due to the natural variability in the AOD, and the rest is noise due to retrieval errors. However, for larger distances (∼ 0.5• ) the correlation is improved as the noise is averaged out, and the day to day changes in regional AOD 15 variability are well captured. Furthermore, we assess the usefulness of the spatial variability of the satellite AOD data as an estimate of CMU by comparing the retrieval errors to the total uncertainty estimates in the validation. We find that accounting for CMU increases the fraction of consistent observations.

show abstract

Regional and local variations in atmospheric aerosols using ground-based sun photometry during Distributed Regional Aerosol Gridded Observation Networks (DRAGON) in 2012

Cited by 13 publications

References 23 publications

An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks

An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks

Collocation mismatch uncertainties in satellite aerosol retrieval validation

Collocation Mismatch Uncertainties in Satellite Aerosol Retrieval Validation

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