Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci)

Popp, Thomas; Leeuw, G. de; Bingen, Christine; Brühl, Christoph; Capelle, V.; Chédin, A.; Clarisse, Lieven; Dubovik, Оleg; Grainger, Roy G.; Griesfeller, Jan; Heckel, A.; Kinne, Stefan; Klüser, Lars; Kosmale, Miriam; Kolmonen, Pekka; Lelli, Luca; Litvinov, Pavel; Mei, Linlu; North, Peter; Pinnock, Simon; Povey, Adam C.; Robert, Charles; Schulz, Mıchael; Sogacheva, Larisa; Stebel, Kerstin; Zweers, Deborah Stein; Thomas, G. E.; Tilstra, L. G.; Vandenbussche, Sophie; Veefkind, J. P.; Vountas, Marco; Xue, Yong

doi:10.3390/rs8050421

Cited by 184 publications

(176 citation statements)

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“…The main output variables are dust optical depth at 10 and 11 15 µm (and 550 nm). Initial results and validation performance are provided in (Popp et al, 2016).…”

Section: Iasi (Infrared Atmospheric Sounding Interferometer)mentioning

confidence: 99%

“…A climatology (Kinne et al, 2006) is used to constrain chemical composition of the aerosol components at 1° x 1° latitude-longitude grid, while the method retrieves aerosol size and optical thickness on a 10 km grid. The algorithm has been developed from initial 25 prototype (Bevan et al, 2012) under the Aerosol CCI program, and results and validation performance for version 4.21 are provided in Popp et al (2016). The version used here (V4.3) differs from that summarized in Popp et al (2016) by improvements in retrieval of coarse/fine mode fraction, and improved cloud screening over ocean in the region of dense plumes, resulting in approximately 10% greater coverage, with small improvement in correlation against AERONET (AErosol RObotic NETwork) values.…”

Section: (A)atsr ((Advanced) Along Track Scanning Radiometer)mentioning

confidence: 99%

“…The IASI dust dataset of the Université Libre de Bruxelles (ULB) was generated in the context of ESA CCI's project 10 (Popp et al, 2016). It is based on a statistical regression technique and the use of a neural network trained on synthetic IASI data.…”

Section: Iasi (Infrared Atmospheric Sounding Interferometer)mentioning

confidence: 99%

“…The present study contributes to the SPARC/SSIRC initiative (Stratosphere-troposphere Processes And their Role in Climate/ Stratospheric Sulfur and Its Role in Climate). The multiple instrument satellite dataset provided in the Climate Change Initiative (CCI) of the European Space Agency (ESA) (Popp et al, 2016) is very valuable for evaluation and improvement of the treatment of stratospheric and tropospheric aerosols in global chemistry climate models, like for example the 30 one used by Brühl et al (2015), EMAC (ECHAM5/MESSy Atmospheric Chemistry). The datasets providing extinctions or total optical depth at wavelengths from ultraviolet (UV) to terrestrial infra-red (IR) are very useful to validate and optimize assumptions on the size distribution and on the composition of aerosol in the model, but also on aerosol sources.…”

Section: Introductionmentioning

confidence: 99%

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Stratospheric aerosol radiative forcing simulated by the chemistry climate model EMAC using aerosol CCI satellite data

Brühl¹,

Schallock²,

Klingmüller³

et al. 2018

Preprint

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Abstract. This paper presents decadal simulations of stratospheric and tropospheric aerosol by the chemistry general circulation model EMAC constrained with satellite observations in the framework of the ESA-Aerosol-CCI project like GOMOS lower stratosphere, and of radiative forcing at the tropopause. To explain the observations, desert dust and organic and black carbon, transported to the lowermost stratosphere by the Asian summer monsoon and tropical convection, are also important. This holds also for radiative heating by aerosol in the lowermost stratosphere. Comparison with (A)ATSR total aerosol optical depth at different wavelengths and IASI dust optical depth shows that the model is able to represent stratospheric and tropospheric aerosol in a consistent way. 20

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“…The main output variables are dust optical depth at 10 and 11 15 µm (and 550 nm). Initial results and validation performance are provided in (Popp et al, 2016).…”

Section: Iasi (Infrared Atmospheric Sounding Interferometer)mentioning

confidence: 99%

Section: (A)atsr ((Advanced) Along Track Scanning Radiometer)mentioning

confidence: 99%

Section: Iasi (Infrared Atmospheric Sounding Interferometer)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stratospheric aerosol radiative forcing simulated by the chemistry climate model EMAC using aerosol CCI satellite data

Brühl¹,

Schallock²,

Klingmüller³

et al. 2018

Preprint

Self Cite

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“…The aerosol data came from the monthly means of the Aerosol Climate Change Initiative (CCI) v4.21 Advanced Along-Track Scanning Radiometer (AATSR) Climate Data Record (CDR), processed with the Swansea algorithm [42], provided at a spatial resolution of one degree. The water vapor data was obtained from the monthly means of the Satellite Application Facility on Climate Monitoring (CM SAF) CDR, based on the Advanced Television Infrared Observation Satellite (TIROS)-N Operational Vertical Sounder (ATOVS) [43], provided at a spatial resolution of 90 km.…”

Section: Atmospheric Correctionmentioning

confidence: 99%

A Multisensor Approach to Global Retrievals of Land Surface Albedo

et al. 2018

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Satellite-based retrievals offer the most cost-effective way to comprehensively map the surface albedo of the Earth, a key variable for understanding the dynamics of radiative energy interactions in the atmosphere-surface system. Surface albedo retrievals have commonly been designed separately for each different spaceborne optical imager. Here, we introduce a novel type of processing framework that combines the data from two polar-orbiting optical imager families, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). The goal of the paper is to demonstrate that multisensor albedo retrievals can provide a significant reduction in the sampling time required for a robust and comprehensive surface albedo retrieval, without a major degradation in retrieval accuracy, as compared to state-of-the-art single-sensor retrievals. We evaluated the multisensor retrievals against reference in situ albedo measurements and compare them with existing datasets. The results show that global land surface albedo retrievals with a sampling period of 10 days can offer near-complete spatial coverage, with a retrieval bias mostly comparable to existing single sensor datasets, except for bright surfaces (deserts and snow) where the retrieval framework shows degraded performance because of atmospheric correction design compromises. A level difference is found between the single sensor datasets and the demonstrator developed here, pointing towards a need for further work in the atmospheric correction, particularly over bright surfaces, and inter-sensor radiance homogenization. The introduced framework is expandable to include other sensors in the future.

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An enhanced VIIRS aerosol optical thickness (AOT) retrieval algorithm over land using a global surface reflectance ratio database

et al. 2016

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The Visible/Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite has been retrieving aerosol optical thickness (AOT), operationally and globally, over ocean and land since shortly after S‐NPP launch in 2011. However, the current operational VIIRS AOT retrieval algorithm over land has two limitations in its assumptions for land surfaces: (1) it only retrieves AOT over the dark surfaces and (2) it assumes that the global surface reflectance ratios between VIIRS bands are constants. In this work, we develop a surface reflectance ratio database over land with a spatial resolution 0.1° × 0.1° using 2 years of VIIRS top of atmosphere reflectances. We enhance the current operational VIIRS AOT retrieval algorithm by applying the surface reflectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOT over both dark and bright surfaces. Over bright surfaces, the VIIRS AOT retrievals from the enhanced algorithm have a correlation of 0.79, mean bias of −0.008, and standard deviation (STD) of error of 0.139 when compared against the ground‐based observations at the global AERONET (Aerosol Robotic Network) sites. Over dark surfaces, the VIIRS AOT retrievals using the surface reflectance ratio database improve the root‐mean‐square error from 0.150 to 0.123. The use of the surface reflectance ratio database also increases the data coverage of more than 20% over dark surfaces. The AOT retrievals over bright surfaces are comparable to MODIS Deep Blue AOT retrievals.

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Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci)

Abstract: Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information

Cited by 184 publications

References 69 publications

Stratospheric aerosol radiative forcing simulated by the chemistry climate model EMAC using aerosol CCI satellite data

Stratospheric aerosol radiative forcing simulated by the chemistry climate model EMAC using aerosol CCI satellite data

A Multisensor Approach to Global Retrievals of Land Surface Albedo

An enhanced VIIRS aerosol optical thickness (AOT) retrieval algorithm over land using a global surface reflectance ratio database

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