Improved algorithm for MODIS satellite retrievals of aerosol optical thickness over land in dusty atmosphere: Implications for air quality monitoring in China

Wang, Jun; Xu, Xiaoguang; Spurr, Robert; Wang, Yuxuang; Drury, Easan

doi:10.1016/j.rse.2010.05.034

Cited by 90 publications

(63 citation statements)

References 34 publications

(46 reference statements)

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“…Key to this retrieval algorithm are: (a) a database of time-dependent surface 0.65 mm and 2.1 mm reflectance ratios that are derived from samples of the MODIS dark-pixel reflectance data in low AOD conditions (e.g., "dynamic lower envelope" approach) for each GEOS-Chem model gridbox, and (b) an assumption that the simulated GEOS-Chem aerosol is unbiased in composition (but possibly largely biased in total mass), enabling use of the GEOS-Chem aerosol single scattering properties in the retrieval including the wavelengthdependence relationship needed for estimating AOD at 2.1 mm from the AOD at 0.67 mm. With (a), (b), and a linearized radiative transfer model that computes the top-ofatmosphere radiance and the sensitivity of radiance to the column AOD, Wang et al [2010] is able to retrieve two unknowns (AOD at 0.67 mm and surface reflectance at 2.13 mm) from two known quantities (MODIS reflectances at 0.67 and 2.1 mm).…”

Section: Geos-chem Model and Its Constraints From Modismentioning

confidence: 99%

“…Based on assessments of the retrieved AOD (from MODIS) by Wang et al [2010], we set the relative error for c obs as 20% and only consider model grid columns where dust mixing ratios are 0.2 ppbv and above because we are only interested in optimizing dust emissions. Similar to previous studies [Dubovik et al, 2008], S obs is assumed to have zero off-diagonal elements, and its diagonal values are the variance of each observation.…”

Section: Implementation Of Geos-chem Dust Adjoint Modelingmentioning

confidence: 99%

“…Furthermore, while satellite AOD is a better constraint than surface measurements for estimating dust emissions as AOD represents the vertical loading of aerosols and thus the optimization of emission will be less sensitive to uncertainties in the simulated aerosol vertical profile, the discrepancy of aerosol optical properties between model and satellite can pose a challenge to attributing the differences between satellite and modeled dust AOD to errors of dust emissions alone [Drury et al, 2008;Wang et al, 2010].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Top‐down estimate of dust emissions through integration of MODIS and MISR aerosol retrievals with the GEOS‐Chem adjoint model

Wang

Henze

et al. 2012

Geophysical Research Letters

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102

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[1] Predicting the influences of dust on atmospheric composition, climate, and human health requires accurate knowledge of dust emissions, but large uncertainties persist in quantifying mineral sources. This study presents a new method for combined use of satellite-measured radiances and inverse modeling to spatially constrain the amount and location of dust emissions. The technique is illustrated with a case study in May 2008; the dust emissions in Taklimakan and Gobi deserts are spatially optimized using the GEOSChem chemical transport model and its adjoint constrained by aerosol optical depth (AOD) that are derived over the downwind dark-surface region in China from MODIS (Moderate Resolution Imaging Spectroradiometer) reflectance with the aerosol single scattering properties consistent with GEOS-chem. The adjoint inverse modeling yields an overall 51% decrease in prior dust emissions estimated by GEOS-Chem over the Taklimakan-Gobi area, with more significant reductions south of the Gobi Desert. The model simulation with optimized dust emissions shows much better agreement with independent observations from MISR (Multi-angle Imaging SpectroRadiometer) AOD and MODIS Deep Blue AOD over the dust source region and surface PM 10 concentrations. The technique of this study can be applied to global multi-sensor remote sensing data for constraining dust emissions at various temporal and spatial scales, and hence improving the quantification of dust effects on climate, air quality, and human health. Citation: Wang, J., X. Xu, D. K. Henze, J. Zeng, Q. Ji, S.-C. Tsay, and J. Huang (2012), Top-down estimate of dust emissions through integration of MODIS and MISR aerosol retrievals with the GEOS-Chem adjoint model, Geophys. Res. Lett., 39, L08802,

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Section: Geos-chem Model and Its Constraints From Modismentioning

confidence: 99%

Section: Implementation Of Geos-chem Dust Adjoint Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Top‐down estimate of dust emissions through integration of MODIS and MISR aerosol retrievals with the GEOS‐Chem adjoint model

Wang

Henze

et al. 2012

Geophysical Research Letters

Self Cite

102

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“…In addition, although the 1.6 µm and 2.1 µm SWIR bands are not sensitive to fine-mode aerosol particles, under the dust aerosol condition, the atmosphere is no longer transparent. Therefore, use of the current algorithm is restricted to dust aerosols [74,75].…”

Section: Discussionmentioning

confidence: 99%

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

et al. 2017

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Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 µm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI 2.1 )-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI 2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AOD AERONET ).

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“…Determination of the sensitivity of the TOA reflectance to atmospheric aerosol properties and emissions is performed using a coupling of the GEOS-Chem forward and adjoint chemical transport models Henze et al, 2007), a vectorized linear radiative transfer model, VLI-DORT (Spurr, 2006), modified to incorporate MODIS data from the Aqua satellite, MARIA (MODIS AOD Retrieval by an Improved Algorithm) (Wang et al, 2010), and a linearized Mie light scattering algorithm (Spurr et al, 2012). A flow chart of the model components and their inputs and outputs is shown in Fig.…”

Section: B S Meland Et Al: Assessing Remote Polarimetric Measuremementioning

confidence: 99%

Assessing remote polarimetric measurement sensitivities to aerosol emissions using the geos-chem adjoint model

Meland

Henze

et al. 2013

Atmos. Meas. Tech.

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Abstract. Uncertainties in aerosol sources, microphysical properties, and global distributions undermine efforts to evaluate the radiative impacts of atmospheric aerosols. In this work, we investigate the feasibility of using remote polarimetric measurements for constraining aerosol and aerosol precursor emissions in light of these uncertainties. A model that incorporates a radiative transfer model with forward and adjoint chemical transport models has been applied to quantify the sensitivity of the reflectance at the top of atmosphere over land to aerosol emissions and microphysical properties. A set of simulated satellite observations, one intensity based and one capable of polarimetric measurements, are used to illustrate differences in the assimilation potential between the two. It is found that the sensitivity of the polarized reflectance to aerosol and aerosol precursor emissions tends to be significantly higher than that of the intensity for cases of non-absorbing aerosols. This is true even when the polarimetric sampling scheme is spatially sparser than that of the intensity sampling. This framework allows us to quantify upper limits on the uncertainties in the aerosol microphysical properties for which a 50 % change in aerosol emissions is detectable using these simulated observations. It was found that although typical current remote sensing instrumentation provides retrievals of the refractive index and effective radius with accuracies within acceptable limits to detect a 50 % change in emissions, retrievals of the effective variance contain uncertainties too large to detect these changes in emissions. These results may guide new applications of polarimetric measurements to constrain aerosol sources, and thus reduce uncertainty in our broader understanding of the impacts of aerosols on climate.

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Improved algorithm for MODIS satellite retrievals of aerosol optical thickness over land in dusty atmosphere: Implications for air quality monitoring in China

Cited by 90 publications

References 34 publications

Top‐down estimate of dust emissions through integration of MODIS and MISR aerosol retrievals with the GEOS‐Chem adjoint model

Top‐down estimate of dust emissions through integration of MODIS and MISR aerosol retrievals with the GEOS‐Chem adjoint model

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

Assessing remote polarimetric measurement sensitivities to aerosol emissions using the geos-chem adjoint model

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