A Long-Term Record of Aerosol Optical Depth from TOMS Observations and Comparison to AERONET Measurements

Torres, Omar; Bhartia, P. K.; Herman, J. R.; Sinyuk, A.; Ginoux, Paul; Holben, B. N.

doi:10.1175/1520-0469(2002)059<0398:altroa>2.0.co;2

Cited by 531 publications

(462 citation statements)

References 29 publications

(45 reference statements)

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“…The method was applied to the TOMS long-term dataset (around 20 years) for semi-global analyses of aerosol optical thickness (Torres et al 2002). In spite of the fact that aerosol layer height is one of the greatest error sources, the heights in their simulation model of the TOMS signals are prescribed as a fixed or a monthly mean ones.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Asian Dust Amount over Land using ADEOS-II/GLI near UV Data

Kuji

Yamanaka

Hayashida

et al. 2005

SOLA

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We propose a retrieval method of Asian dust (Yellow sand or Kosa aerosol) columnar amount around source regions using a near ultraviolet radiometry observation from space. The method simultaneously retrieves an optical thickness and mode radius of Kosa aerosol, and then derives its columnar amount. The method was applied to ADEOS-II/GLI data in the spring of 2003 around Taklimakan desert source region, inland China. The retrieved optical thickness and mode radius were about 0.34 and 1.75 µm, respectively, at a validation site. They are comparable to the in situ observations conducted within the framework of ADEC project. The estimated columnar amount around a validation site is about 2.77 g m 2 , which seems reasonable under a relatively calm situation. The method should be further validated with a regional model simulation study, and then it is useful to monitor Asian dust around source regions from space in the future.

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Section: Introductionmentioning

confidence: 99%

Retrieval of Asian Dust Amount over Land using ADEOS-II/GLI near UV Data

Kuji

Yamanaka

Hayashida

et al. 2005

SOLA

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“…For the earlier version 7, AI is positive for absorbing aerosols, e.g., dust and smoke, close to zero with for clouds or larger sized (0.2 μm or larger) nonabsorbing aerosols, and negative for small non-absorbing particles (pure scattering) (Riemer et al, 2006;Torres et al, 2002). In version 8, only positive values for absorbing aerosols are available (Riemer et al, 2006).…”

Section: Methodsmentioning

confidence: 99%

“…where I mean is the measured backscattered radiance at a given wavelength, and I calc is the calculated radiance at the wavelength assuming a purely gaseous atmosphere (Torres et al, 2002). For the earlier version 7, AI is positive for absorbing aerosols, e.g., dust and smoke, close to zero with for clouds or larger sized (0.2 μm or larger) nonabsorbing aerosols, and negative for small non-absorbing particles (pure scattering) (Riemer et al, 2006;Torres et al, 2002).…”

Section: Methodsmentioning

confidence: 99%

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The Northern Path of Asian Dust Transport from the Gobi Desert to North America

Chen

2010

Atmospheric and Oceanic Science Letters

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The aerosol index (AI) of the Total Ozone Mapping Spectrometer (TOMS) satellite data (1979−2001) was analyzed to reveal the climatological long-distance path of dust transport from Asia to North America. The AI in the west coast of the United States is highly correlated with that in the Gobi desert. Additionally, from the TOMS satellite images, it can be seen that very strong plumes advect from Asia to the west coast of North America in typical dust storm cases. When applying the sourcereceptor relationship to detect the northern dust transport path between the Gobi source region and the west coast of the United States receptor region, it is evident that the dust plume can be transported northward beyond 60°N from its source region and that it takes 5 to 6 days to reach the west coast of the United States. The cross correlation technique shown in this work is a useful tool that can be applied in other regions to give useful insights into relationships between major dust sources and downwind receptor locations by using remotely sensed dust observations.

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“…The earliest records of satellite-based AOD are provided by TOMS (total ozone mapping spectrometer, e.g. Torres et al, 2002) and AVHRR (Advanced Very High Resolution Radiometer, Geogdzhayev et al, 2005) from 1979 and 1983 onwards respectively. However, neither one of these instruments were specifically designed to retrieve aerosol properties.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

et al. 2016

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Abstract. In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the lookup table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during the observation period.

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A Long-Term Record of Aerosol Optical Depth from TOMS Observations and Comparison to AERONET Measurements

Cited by 531 publications

References 29 publications

Retrieval of Asian Dust Amount over Land using ADEOS-II/GLI near UV Data

Retrieval of Asian Dust Amount over Land using ADEOS-II/GLI near UV Data

The Northern Path of Asian Dust Transport from the Gobi Desert to North America

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

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