The GRAPE aerosol retrieval algorithm

Thomas, G. E.; Poulsen, C.; Sayer, A. M.; Marsh, S.; Dean, S. M.; Carboni, Elisa; Siddans, Richard; Grainger, Roy G.; Lawrence, Bryan

doi:10.5194/amt-2-679-2009

Cited by 76 publications

(70 citation statements)

References 35 publications

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“…Potential contributions include errors in co-registration between channels, error in absolute radiometric calibration, errors in the assumed atmospheric and surface temperature profiles, errors in the planeparallel assumption (e.g., cloud vertical structure, 3-D effects), errors in the ice scattering model, impact of aerosol, error in modelling surface reflectance/emissivity, etc. The impact of these terms has been investigated in studies such as Watts et al (1998) and Siddans et al (2009). In general, the errors depend on the context of a particular retrieval (including cloud-type, clear-sky atmospheric state etc).…”

Section: Measurement Vector and Covariancementioning

confidence: 99%

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Poulsen

Siddans

Thomas³

et al. 2012

Atmos. Meas. Tech.

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Abstract. Clouds play an important role in balancing the Earth's radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud) which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase.The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick, greater than 50 optical depths, where the cloud begins to saturate. The cost proved a good indicator of multi-layer scenarios. Both the retrieval cost and the error need to be considered together in order to evaluate the quality of the retrieval. This algorithm in the configuration described here has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation) project to produce a 14 yr consistent record for climate research.

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Section: Measurement Vector and Covariancementioning

confidence: 99%

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Poulsen

Siddans

Thomas³

et al. 2012

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In satellite AOD retrieval algorithms these are often parametrised as a combination of aerosol components, each with specified size distribution and spectral refractive index, whose total abundance and relative weight are varied in order to best match the top-of-atmosphere (TOA) radiance observed by the sensor (e.g. Martonchik et al, 1998;Mishchenko et al, 1999;Remer et al, 2009;Thomas et al, 2009;Sayer et al, 2012a).…”

Section: A M Sayer Et Al: Smoke Aerosol Propertiesmentioning

confidence: 99%

AERONET-based models of smoke-dominated aerosol near source regions and transported over oceans, and implications for satellite retrievals of aerosol optical depth

et al. 2014

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Abstract. Smoke aerosols from biomass burning are an important component of the global aerosol system. Analysis of Aerosol Robotic Network (AERONET) retrievals of aerosol microphysical/optical parameters at 10 sites reveals variety between biomass burning aerosols in different global source regions, in terms of aerosol particle size and single scatter albedo (SSA). Case studies of smoke observed at coastal/island AERONET sites also mostly lie within the range of variability at the near-source sites. Differences between sites tend to be larger than variability at an individual site, although optical properties for some sites in different regions can be quite similar. Across the sites, typical midvisible SSA ranges from ∼ 0.95-0.97 (sites dominated by boreal forest or peat burning, typically with larger fine-mode particle radius and spread) to ∼ 0.88-0.9 (sites most influenced by grass, shrub, or crop burning, typically smaller finemode particle radius and spread). The tropical forest site Alta Floresta (Brazil) is closer to this second category, although with intermediate SSA ∼ 0.92. The strongest absorption is seen in southern African savannah at Mongu (Zambia), with average midvisible SSA ∼ 0.85. Sites with stronger absorption also tend to have stronger spectral gradients in SSA, becoming more absorbing at longer wavelengths. Microphysical/optical models are presented in detail so as to facilitate their use in radiative transfer calculations, including extension to UV (ultraviolet) wavelengths, and lidar ratios. One intended application is to serve as candidate optical models for use in satellite aerosol optical depth (AOD) retrieval algorithms. The models presently adopted by these algorithms over ocean often have insufficient absorption (i.e. too high SSA) to represent these biomass burning aerosols. The underestimates in satellite-retrieved AOD in smoke outflow regions, which have important consequences for applications of these satellite data sets, are consistent with the level of underestimated absorption.

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“…As a result RAY meets the speed requirements for including RT computations in the AOT retrieval processing. Validation of the RAY code has demonstrated (Tynes et al, 2001) that it provides highly accurate data in a fraction of the time required by the Monte Carlo and other methods. For instance, the difference between RAY and SCIATRAN computations for aerosol atmosphere is smaller than 0.5% for all Stokes vector components (see benchmark results at http://www.iup.physik.uni-bremen.de/ ∼ alexk).…”

Section: Merismentioning

confidence: 99%

“…Such approach can be applied operationally only if the accurate and extremely fast radiative transfer code is used. The RAY code (Tynes et al, 2001;Katsev et al, 2009) for simulation of the radiative transfer in the atmosphere-underlying surface system was used. This code is fast.…”

Section: Merismentioning

confidence: 99%

The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light

et al. 2010

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Abstract. Remote sensing of aerosol from space is a challenging and typically underdetermined retrieval task, requiring many assumptions to be made with respect to the aerosol and surface models. Therefore, the quality of a priori information plays a central role in any retrieval process (apart from the cloud screening procedure and the forward radiative transfer model, which to be most accurate should include the treatment of light polarization and molecular-aerosol coupling). In this paper the performance of various algorithms with respect to the of spectral aerosol optical thickness determination from optical spaceborne measurements is studied. The algorithms are based on various types of measurements (spectral, angular, polarization, or some combination of these). It is confirmed that multiangular spectropolarimetric measurements provide more powerful constraints compared to spectral intensity measurements alone, particularly those acquired at a single view angle and which rely on a priori assumptions regarding the particle phase function in the retrieval process.

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The GRAPE aerosol retrieval algorithm

Cited by 76 publications

References 35 publications

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

AERONET-based models of smoke-dominated aerosol near source regions and transported over oceans, and implications for satellite retrievals of aerosol optical depth

The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light

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