Multisensor Data Product Fusion for Aerosol Research

Gupta, Pawan; Patadia, Falguni; Christopher, S. A.

doi:10.1109/tgrs.2008.916087

Cited by 26 publications

(17 citation statements)

References 20 publications

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“…No point spread function (PSF) weighting is assigned but the uncertainties due to that may be small since the spatial resolution of MISR aerosol product at nadir (17.6 km 2 ) is close to that of CERES footprint at nadir (20 km 2 ) and the narrow swath width of MISR limits the change of respective pixel sizes away from nadir. However, the change in CERES footprint size is accounted for in the collocation algorithm [ Gupta et al , 2008]. Once the data sets are collocated, the mean and standard deviations of τ MODC , τ MISRC , the fractionated optical depths, AE and the CERES SSF data are stored for further analysis.…”

Section: Methodsmentioning

confidence: 99%

“…However, 36 spectral channels of MODIS facilitate good cloud clearing capabilities. Thus we use the coregistered MODIS cloud‐cover information to identify clear sky CERES TOA shortwave fluxes (SWF) by eliminating cloud‐contaminated pixels and we use the collocated MISR AOT ( τ MISRC ) to identify CERES pixels containing aerosol particles [ Gupta et al , 2008]. The TOA SWARF is then calculated by differencing the TOA clear and aerosol sky fluxes.…”

Section: Introductionmentioning

confidence: 99%

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A Multisensor satellite‐based assessment of biomass burning aerosol radiative impact over Amazonia

et al. 2008

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[1] Using spatially and temporally collocated multispectral, multiangle and broadband data sets from the Terra satellite, the role of biomass burning (BB) smoke particles on cloud-free top of atmosphere (TOA) direct shortwave aerosol radiative forcing (SWARF) is examined. A 5-year analysis during the peak biomass burning months of August and September is presented over South America (0°-20°S and 45°W-65°W). Our results indicate that over 5 years, the TOA diurnally averaged SWARF (DSWARF) from the Clouds and the Earth's Radiant Energy System (CERES) scanner ranges between À5.2 Wm À2 and À9.4 Wm À2 with a mean value of À7.6 Wm À2 and an estimated uncertainty of ±1.4 Wm À2 . The corresponding Multi Angle Spectroradiometer (MISR) aerosol optical thickness (AOT at 0.558 mm) ranged from 0.15 to 0.36 with a mean value of 0.24. The estimated mean TOA aerosol radiative forcing efficiency (E t ) is À44.2 Wm À2 t À1 and is in good agreement with previous studies. We also examined the beta versions of the MISR data products such as the angstrom exponent (AE) and fraction of AOT in different size bins to assess the role of BB aerosol particle properties on SWARF. Our analysis indicates that the MISR retrieved 5 year mean AE is 1.54. Contribution to total AOT from small, medium and large particles is 66%, 16% and 18% respectively. This is the first multiyear assessment of SWARF for biomass burning aerosol particles using satellite observations alone and should serve as a useful constraint for numerical modeling simulations that estimate SWARF.Citation: Patadia, F., P. Gupta, S. A Christopher, and J. S. Reid (2008), A Multisensor satellite-based assessment of biomass burning aerosol radiative impact over Amazonia,

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multisensor satellite‐based assessment of biomass burning aerosol radiative impact over Amazonia

et al. 2008

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“…Singh et al, 2008;Gupta et al, 2008;Nastos et al, 2010;Maghrabi et al, 2011;Samoli et al, 2011). Various aerosol types have distinct effects on the sign and magnitude of aerosol radiative forcing (Satheesh and Krishnamoorthy, 2005), while the presence of UV-absorbing dust aerosols over highly-reflected desert surfaces may change the sign of the forcing from negative to positive (Hatzianastassiou et al, 2005;Patadia et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability of dust aerosols over the Sistan region in Iran based on satellite observations

et al. 2013

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Satellite remote sensing provides important observational constraints for monitoring dust life cycle and improving the understanding of its effects on local to global scales. The present work analyses the dust-aerosol patterns over the arid environment of the Sistan region-in southeastern Iran, by means of multiple satellite platforms aiming to reveal the spatiotemporal distribution and trends. The dataset includes records of Aerosol Index (AI) from Total Ozone Mapping Spectrometer (TOMS) and 6-year AI records from the Ozone Monitoring Instrument (OMI) aboard Aura. Moreover, the aerosol optical depth (AOD) is analyzed through 11-year records from Multi-angle Imaging Spectro-Radiometer (MISR) aboard Terra (2000Terra ( -2010 and from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra (2000Terra ( -2007 and Aqua (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). The main focus is to determine similarities and differences in dust variability over southwest Asia, in general, and the Sistan region, in particular. The results show a marked seasonal cycle with high aerosol loading during summer and lower in winter, while MISR, MODIS and TOMS/OMI observations agree in both terms of monthly and seasonally mean spatial and temporal patterns. The higher aerosol concentrations during summer are interpreted as a result of the combined effect of the seasonal drying of the Hamoun lakes and the strong northerly Levar winds favoring dust erosion from the alluvial deposits in Sistan . After prolonged drought period, the dust-aerosol load over the area has increased in the beginning of the 2000s and decreased after 2004, 2 thereby leading to an overall declining trend during the last decade. Such a trend is absent during the winter period when dust emission over the region is minimal.

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“…Gupta et al (2008) evaluate satellite data fusion for level 2 aerosol products from multi-angle imagining spectro radiometer (MISR) and moderate resolution imaging spectrometer (MODIS) with cloud & earth's radiant energy systems (CERES) spatial scanner footprint (SSF) data and assess their data fusion scheme through case studies. Nirala (2008) use maximum likelihood approach and spatial interpolation techniques to fuse gridded level 3 MODIS aerosol products with each other.…”

Section: Introductionmentioning

confidence: 99%

Statistical data fusion of multi-sensor AOD over the Continental United States

Puttaswamy

Nguyen

Braverman

et al. 2013

Geocarto International

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This article illustrates two techniques for merging daily aerosol optical depth (AOD) measurements from satellite and ground-based data sources to achieve optimal data quality and spatial coverage. The first technique is a traditional Universal Kriging (UK) approach employed to predict AOD from multi-sensor aerosol products that are aggregated on a reference grid with AERONET as ground truth. The second technique is spatial statistical data fusion (SSDF); a method designed for massive satellite data interpolation. Traditional kriging has computational complexity O(N 3 ), making it impractical for large datasets. Our version of UK accommodates massive data inputs by performing kriging locally, while SSDF accommodates massive data inputs by modelling their covariance structure with a low-rank linear model. In this study, we use aerosol data products from two satellite instruments: the moderate resolution imaging spectrometer and the geostationary operational environmental satellite, covering the Continental United States.

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Multisensor Data Product Fusion for Aerosol Research

Cited by 26 publications

References 20 publications

A Multisensor satellite‐based assessment of biomass burning aerosol radiative impact over Amazonia

A Multisensor satellite‐based assessment of biomass burning aerosol radiative impact over Amazonia

Spatio-temporal variability of dust aerosols over the Sistan region in Iran based on satellite observations

Statistical data fusion of multi-sensor AOD over the Continental United States

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