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Global monthly mean cloud cover from DLR APOLLO AATSR (common nadir cloud mask used in Aerosol cci) for September 2008.
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Within the ESA Climate Change Initiative (CCI) project Aerosol_cci
(2010-2013) algorithms for the production of long-term total column
aerosol optical depth (AOD) datasets from European Earth Observation
sensors are developed. Starting with eight existing pre-cursor
algorithms three analysis steps are conducted to improve and qualify the
algorithms...
Context in source publication
Context 1
... an illustration of spatial aerosol retrieval limitations in the experiments conducted, Fig. 5 presents the global monthly mean cloud fraction for September 2008 obtained from the APOLLO method (adapted to AATSR as described in Holzer-Popp et al., 2008) with AATSR. It is evident that over ocean the cloud fraction is generally higher than over land, especially in the subtropical subsidence regions and only few regions over land ...
Similar publications
Within the ESA Climate Change Initiative (CCI) project Aerosol_cci (2010–2013), algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithm...
Citations
... However, noticeable inconsistencies exist among the aerosol datasets generated from different satellite sensors and aerosol retrieval algorithms. Few studies have focused on exploring the similarities and differences among aerosol datasets (Holzer-Popp et al., 2013;Naba et al., 2013;De Leeuw et al., 2015;Sayer et al., 2018a). The selection of an accurate and appropriate aerosol product to represent the long-term aerosol 85 ...
... Meanwhile, the retrieved errors for aerosol parameters are estimated by propagating the measurement and forward model uncertainties into the state space. The AATSR-EN product is integrated based on different ESA-AATSR aerosol products using likelihood estimate approaches (Holzer-Popp et al., 2013). In this study, the latest versions of the 120 above four ESA-CCI products (Table 1) are collected. ...
Satellite-derived aerosol products provide long-term and large-scale
observations for analysing aerosol distributions and variations,
climate-scale aerosol simulations, and aerosol–climate interactions.
Therefore, a better understanding of the consistencies and differences among
multiple aerosol products is important. The objective of this study is to
compare 11 global monthly aerosol optical depth (AOD) products,
which are the European Space Agency Climate Change Initiative (ESA-CCI)
Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High
Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer
(MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing
Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer
(VIIRS), and POLarization and Directionality of the Earth's Reflectance
(POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0
monthly measurements at 308 sites around the world are selected for
comparison. Our results illustrate that the spatial distributions and
temporal variations of most aerosol products are highly consistent globally
but exhibit certain differences on regional and site scales. In general, the
AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage
with numerous missing values, while the MODIS products can cover most areas
(average of 87 %) of the world. The best performance is observed in
September–October–November (SON) and the worst is in June–July–August (JJA).
All the products perform unsatisfactorily over northern Africa and Middle
East, southern and eastern Asia, and their coastal areas due to the influence from
surface brightness and human activities. In general, the MODIS products show
the best agreement with the AERONET-based AOD values on different spatial
scales among all the products. Furthermore, all aerosol products can capture
the correct aerosol trends at most cases, especially in areas where aerosols
change significantly. The MODIS products perform best in capturing the
global temporal variations in aerosols. These results provide a reference
for users to select appropriate aerosol products for their particular
studies.
... Meanwhile, the retrieved errors for aerosol parameters are estimated by propagating the measurement and forward 115 model uncertainties into the state space. The AATSR-ENSEMBLE (AATSR-EN) product is integrated based on different ESA-AATSR aerosol products using likelihood estimate approaches (Holzerpopp et al., 2013). In this study, the latest versions of the above four ESA-CCI products (Table 1) are collected from the ICARE Data and Services Centre (http://www.icare.univ-lille1.fr/cci). ...
Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol-climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is of great importance. The objective of this study is to evaluate nine operational global aerosol optical depth (AOD) products, including the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging Spectro Radiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) over the land and oceans during the period from 1997 to 2017. Aerosol Robotic Network (AERONET) Version 3 Level 2.0 (cloud screened and quality assured) measurements at 243 sites around the world are selected for validation purposes. Our results illustrate that noticeable inconsistencies exist in spatial coverages, variations, and temporal trends among different aerosol products. Large annual mean AOD differences greater than 0.15 are mainly found in North Africa, Central Africa, the Middle East, and East Asia, as well as in the coastal areas of western North America and Asia. In general, the AATSR Swansea University (SU) product appears to have the best accuracy, the SeaWiFS product obviously underestimates aerosol loadings over the land and ocean, and the MODIS products seriously overestimate aerosol loadings over the land and ocean. For temporal trends, more than 77% and 72% of land and ocean sites, respectively, show the same trends direction as the ground observations. However, numerical differences are non-negligible in North Africa, the Middle East, and South Asia and in most open oceans. MODIS products are generally more accurate in describing the temporal trends over the land and ocean. The aerosol loadings trend changed from positive to negative over the past two decades (2000-2017) in some continental areas. These results suggest that multiple AOD products show significant numerical inconsistencies in describing spatial distributions and temporal trends, which should be carefully considered when users choose aerosol products for their studies.
... Additionally, a newly developed NOAA aerosol retrieval algorithm provides another aerosol product [9]. Instruments with multi-angles yield further possibilities for accurate aerosol retrievals, such as the Multiangle Imaging SpectroRadiometer (MISR) [10] and Advanced Along Track Scanning Radiometer (AATSR) [11][12][13].There are also three current aerosol retrieval algorithms for AATSR: the AATSR Dual View Algorithm (ADV) [14,15], Oxford-RAL Aerosol and Cloud (ORAC) [16], and Swansea University (SU) [17], all of which will be further used with the Sea and Land Surface Temperature Radiometer (SLSTR). Instruments such as the Ozone Monitoring Instrument (OMI) [18] and Polder [19] provide UV channels and/or polarization information to accurately retrieve the aerosol absorbing mode or aerosol fine mode fraction. ...
... For tropical areas such as Africa, the aerosol type is dominated by dust aerosols for Saharan areas [51] (surface reflectances at 550 nm > 0.3) and industrial and dust aerosols for most parts of South Africa [52]. In contrast, absorbing aerosols such as biomass burning dominate in northern South America [12], resulting in a negative AOT difference. Details of the AOT quality relating to aerosol absorption are discussed in Section 4.3.2. ...
The European Space Agency’s (ESA’s) Aerosol Climate Change Initiative (CCI) project intends to exploit the robust, long-term, global aerosol optical thickness (AOT) dataset from Europe’s satellite observations. Newly released Swansea University (SU) aerosol products include AATSR retrieval and synergy between AATSR and MERIS with a spatial resolution of 10 km. In this study, both AATSR retrieval (SU/AATSR) and AATSR/MERIS synergy retrieval (SU/synergy) products are validated globally using Aerosol Robotic Network (AERONET) observations for March, June, September, and December 2008, as suggested by the Aerosol-CCI project. The analysis includes the impacts of cloud screening, surface parameterization, and aerosol type selections for two products under different surface and atmospheric conditions. The comparison between SU/AATSR and SU/synergy shows very accurate and consistent global patterns. The global evaluation using AERONET shows that the SU/AATSR product exhibits slightly better agreement with AERONET than the SU/synergy product. SU/synergy retrieval overestimates AOT for all surface and aerosol conditions. SU/AATSR data is much more stable and has better quality; it slightly underestimates fine-mode dominated and absorbing AOTs yet slightly overestimates coarse-mode dominated and non-absorbing AOTs.
... Key remote sensing data used in this study were the aerosol optical depth and land surface temperature products available from the European Space Agency (ESA) Aerosol_cci [29,30] and ESA DUE GlobTemperature (Available online: http://www.globtemperature.info/) projects, together with ancillary data, such as tropospheric column density of nitrogen dioxide (NO 2 ) and land cover type (Table 1). ...
Satellite data suggest that summertime aerosol optical depth (AOD) over the southeastern USA depends on the air/land surface temperature, but the magnitude of the radiative effects caused by this dependence remains unclear. To quantify these radiative effects, we utilized several remote sensing datasets and ECMWF reanalysis data for the years 2005–2011. In addition, the global aerosol–climate model ECHAM-HAMMOZ was used to identify the possible processes affecting aerosol loads and their dependence on temperature over the studied region. The satellite-based observations suggest that changes in the total summertime AOD in the southeastern USA are mainly governed by changes in anthropogenic emissions. In addition, summertime AOD exhibits a dependence on southerly wind speed and land surface temperature (LST). Transport of sea salt and Saharan dust is the likely reason for the wind speed dependence, whereas the temperature-dependent component is linked to temperature-induced changes in the emissions of biogenic volatile organic compounds (BVOCs) over forested regions. The remote sensing datasets indicate that the biogenic contribution increases AOD with increasing temperature by approximately (7 ± 6) × 10−3 K−1 over the southeastern USA. In the model simulations, the increase in summertime AOD due to temperature-enhanced BVOC emissions is of a similar magnitude, i.e., (4 ± 1) × 10−3 K−1. The largest source of BVOC emissions in this region is broadleaf trees, thus if the observed temperature dependence of AOD is caused by biogenic emissions the dependence should be the largest in the vicinity of forests. Consequently, the analysis of the remote sensing data shows that over mixed forests the biogenic contribution increases AOD by approximately (27 ± 13) × 10−3 K−1, which is over four times higher than the value for over the whole domain, while over other land cover types in the study region (woody savannas and cropland/natural mosaic) there is no clear temperature dependence. The corresponding clear-sky direct radiative effect (DRE) of the observation-based biogenic AOD is −0.33 ± 0.29 W/m2/K for the whole domain and −1.3 ± 0.7 W/m2/K over mixed forests only. The model estimate of the regional clear-sky DRE for biogenic aerosols is similar to the observational estimate for the whole domain: −0.29 ± 0.09 W/m2/K. Furthermore, the model simulations showed that biogenic emissions have a significant effective radiative forcing (ERF) in this region: −1.0 ± 0.5 W/m2/K.
... The nadir view and the forward view at 55º incident angle to the surface allowed for near- 25 simultaneous observation of the same area on the Earth's surface through two different atmospheric columns within ~2 minutes. From the AATSR, AOD data available from the ESA Aerosol_cci project (Holzer-Popp et al., 2013; de Leeuw et al., 2015) and LST data from ESA's DUE GlobTemperature project were used. More specifically, daily Level 3 AOD data with spatial 30 resolution of 1 × 1 degrees from the full mission (2002-2012) were chosen because of the similarity in their spatial resolution Atmos. ...
Previous studies have indicated that summer-time aerosol optical depths (AOD) over the southeastern US are dependent on temperature but the reason for this dependence and its radiative effects have so far been unclear. To quantify these effects we utilized AOD and land surface temperature (LST) products from the Advanced Along-Track Scanning Radiometer (AATSR) with observations of tropospheric nitrogen dioxide (NO2) column densities from the Ozone Monitoring Instrument (OMI). Furthermore, simulations of the global aerosol-climate model ECHAM-HAMMOZ have been used to identify the possible processes affecting aerosol loads and their dependence on temperature over the studied region. Our results showed that the level of AOD in the southeastern US is mainly governed by anthropogenic emissions but the observed temperature dependent behaviour is most likely originating from non-anthropogenic emissions. Model simulations indicated that biogenic emissions of volatile organic compounds (BVOC) can explain the observed temperature dependence of AOD. According to the remote sensing data sets, the non-anthropogenic contribution increases AOD by approximately 0.009 ± 0.018 K−1 while the modelled BVOC emissions increase AOD by 0.022 ± 0.002 K−1. Consequently, the regional direct radiative effect (DRE) of the non-anthropogenic AOD is −0.43 ± 0.88 W/m2/K and −0.17 ± 0.35 W/m2/K for clear- and all-sky conditions, respectively. The model estimate of the regional clear-sky DRE for biogenic aerosols is also in the same range: −0.86 ± 0.06 W/m2/K. These DRE values indicate significantly larger cooling than the values reported for other forested regions. Furthermore, the model simulations showed that biogenic emissions increased the number of biogenic aerosols with radius larger than 100 nm (N100, proxy for cloud condensation nuclei) by 28 % per one degree temperature increase. For the total N100, the corresponding increase was 4 % which implies that biogenic emissions could also have a small effect on indirect radiative forcing in this region.
... The major goal of this activity, which started in 2010, has been to produce aerosol CDRs which satisfy the requirements on data quality and transparent documentation set by GCOS. As first steps towards this goal, several algorithm experiments [19] and a round robin exercise [20] for total AOD were performed. Algorithms for stratospheric aerosol extinction and an absorbing aerosol index were also evaluated. ...
... The successful use of such experimental elements is based on a retrieval expert's experience and can only be justified through validation of the resulting dataset. Sens. 2016, 8, 421 4 of 36 towards this goal, several algorithm experiments [19] and a round robin exercise [20] for total AOD were performed. Algorithms for stratospheric aerosol extinction and an absorbing aerosol index were also evaluated. ...
... A similar concept has been presented in the context of satellite data uncertainty characterization by [21]. Algorithm development can start with preparatory elements: Algorithm experiments identify key sensitivities or areas for harmonization across a set of algorithms (e.g., [19]). As one example, a common definition of optical aerosol components was defined and implemented within eight different AOD algorithms. ...
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 content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e., dust aerosol, aerosol absorption).
... Europe Space Agency's (ESA's) Climate Change Initiative (CCI [68]) is making full use of Europe's Earth observation space assets to exploit robust, long-term global records of essential climate variables, such as greenhouse-gas concentrations, sea-ice extent and thickness, and sea-surface temperature and salinity. This sub-section briefly describes some of the CCI initiatives: - Aerosol CCI: Aerosol CCI [69,70] was a three year intensive algorithm development effort that incorporated sensitivity analysis, validation and inter-comparison activities [71], along with a round robin exercise of seven different retrieval algorithms [72]. It produced: (1) AOT and Ångström exponent (AE); (2) Stratospheric extinction profiles and aerosol optical thickness; and (3) Absorbing aerosol index (AAI). ...
This review paper discusses how to develop, produce, sustain, and serve satellite climate data records (CDRs) in the context of transitioning research to operation (R2O). Requirements and critical procedures of producing various CDRs, including Fundamental CDRs (FCDRs), Thematic CDRs (TCDRs), Interim CDRs (ICDRs), and climate information records (CIRs) are discussed in detail, including radiance/reflectance and the essential climate variables (ECVs) of land, ocean, and atmosphere. Major international CDR initiatives, programs, and projects are summarized. Societal benefits of CDRs in various user sectors, including Agriculture, Forestry, Fisheries, Energy, Heath, Water, Transportation, and Tourism are also briefly discussed. The challenges and opportunities for CDR development, production and service are also addressed. It is essential to maintain credible CDR products by allowing free access to products and keeping the production process transparent by making source code and documentation available with the dataset.
... The aerosol model used in both ADV and ASV is a mixture of four aerosol components. The components are adopted from the ESA Aerosol_cci (climate change initiative; Hollmann et al. 2013) project 1 (Holzer-Popp et al. 2013;de Leeuw et al. 2015). The properties of the components are described in Table 1. ...
An advanced along-track scanning radiometer (AATSR) global multi-year aerosol retrieval algorithm is described. Over land, the AATSR dual-view (ADV) algorithm utilizes the measured top of the atmosphere (TOA) reflectance in both the nadir and forward views to decouple the contributions of the atmosphere and the surface to retrieve aerosol properties. Over ocean, the AATSR single-view (ASV) algorithm minimizes the discrepancy between the measured and modelled TOA reflectances in one of the views to retrieve the aerosol information using an ocean reflectance model. Necessary steps to process global, multi-year aerosol information are presented. These include cloud screening, the averaging of measured reflectance, and post-processing. Limitations of the algorithms are also discussed. The main product of the aerosol retrieval is the aerosol optical depth (AOD) at visible/near-infrared wavelengths. The retrieved AOD is validated using data from the surface-based AERONET and maritime aerosol network (MAN) sun photometer networks as references. The validation shows good agreement with the reference (r = 0.85, RMSE = 0.09 over land; r = 0.83, RMSE = 0.09 at coasts and r = 0.96, RMSE = 0.06 over open ocean). The results of the aerosol retrievals are presented for the full AATSR mission years 2002–2012 with seasonally averaged time series for selected regions.
... Differences between retrievals, in the absence of external validation data or a programming error, indicate variations in the state within the unconstrained state space. They form an ensemble that illuminates where the formulation of the problem is most relevant, highlighting where future research could be concentrated to represent the observations more carefully (Holzer-Popp et al., 2013). Belief that one representation is " better " than others independent of external validation is an expression of a priori knowledge. ...
This paper discusses a best-practice representation of uncertainty in satellite remote sensing data. An estimate of uncertainty is necessary to make appropriate use of the information conveyed by a measurement. Traditional error propagation quantifies the uncertainty in a measurement due to well-understood perturbations in a measurement and in auxiliary data – known, quantified "unknowns". The under-constrained nature of most satellite remote sensing observations requires the use of various approximations and assumptions that produce non-linear systematic errors that are not readily assessed – known, unquantifiable "unknowns". Additional errors result from the inability to resolve all scales of variation in the measured quantity – unknown "unknowns". The latter two categories of error are dominant in under-constrained remote sensing retrievals, and the difficulty of their quantification limits the utility of existing uncertainty estimates, degrading confidence in such data. This paper proposes the use of ensemble techniques to present multiple self-consistent realisations of a data set as a means of depicting unquantified uncertainties. These are generated using various systems (different algorithms or forward models) believed to be appropriate to the conditions observed. Benefiting from the experience of the climate modelling community, an ensemble provides a user with a more complete representation of the uncertainty as understood by the data producer and greater freedom to consider different realisations of the data.
... This algorithm was applied to data over southern Africa; however, they concluded that aerosol properties were not accurately retrieved for strongly absorbing aerosols resulting from biomass burning. More recently, Holzer-Popp et al. [2013] discuss several satellite retrieval algorithms developed for application to European Earth observation satellite sensor data, some of which account for variation in aerosol absorption properties. ...
[1] As a representative site of the southern African biomass-burning region, sun-sky data from the 15 year Aerosol Robotic Network (AERONET) deployment at Mongu, Zambia, was analyzed. For the biomass-burning season months (July–November), we investigate seasonal trends in aerosol single scattering albedo (SSA), aerosol size distributions, and refractive indices from almucantar sky scan retrievals. The monthly mean single scattering albedo at 440 nm in Mongu was found to increase significantly from ~0.84 in July to ~0.93 in November (from 0.78 to 0.90 at 675 nm in these same months). There was no significant change in particle size, in either the dominant accumulation or secondary coarse modes during these months, nor any significant trend in the Ångström exponent (440–870 nm; r2 = 0.02). A significant downward seasonal trend in imaginary refractive index (r2 = 0.43) suggests a trend of decreasing black carbon content in the aerosol composition as the burning season progresses. Similarly, burning season SSA retrievals for the Etosha Pan, Namibia AERONET site also show very similar increasing single scattering albedo values and decreasing imaginary refractive index as the season progresses. Furthermore, retrievals of SSA at 388 nm from the Ozone Monitoring Instrument satellite sensor show similar seasonal trends as observed by AERONET and suggest that this seasonal shift is widespread throughout much of southern Africa. A seasonal shift in the satellite retrieval bias of aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer collection 5 dark target algorithm is consistent with this seasonal SSA trend since the algorithm assumes a constant value of SSA. Multi-angle Imaging Spectroradiometer, however, appears less sensitive to the absorption-induced bias.
