Retrieving aerosol optical depth and type in the boundary layer over land and ocean from simultaneous GOME spectrometer and ATSR‐2 radiometer measurements, 1, Method description

Popp, Thomas; Schroedter, M.; Gesell, Gerhard

doi:10.1029/2001jd002013

Cited by 40 publications

(40 citation statements)

References 31 publications

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“…Table 1 in Holzer-Popp et al (2002) gives percentage contributions to the total optical depth of the respective mixtures by the basic components: water soluble, water insoluble, sea salt accumulation mode, coarse mode, soot, and mineral transported. It is used as a guide for converting MACC species into types.…”

Section: Aerosols Propertiesmentioning

confidence: 99%

McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions

et al. 2013

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A new fast clear-sky model called McClear was developed to estimate the downwelling shortwave direct and global irradiances received at ground level under clear skies. It is a fully physical model replacing empirical relations or simpler models used before. It exploits the recent results on aerosol properties, and total column content in water vapour and ozone produced by the MACC project (Monitoring Atmosphere Composition and Climate). It accurately reproduces the irradiance computed by the libRadtran reference radiative transfer model with a computational speed approximately 10⁵ times greater by adopting the abaci, or look-up table, approach combined with interpolation functions. It is therefore suited for geostationary satellite retrievals or numerical weather prediction schemes with many pixels or grid points, respectively. McClear irradiances were compared to 1 min measurements made in clear-sky conditions at several stations within the Baseline Surface Radiation Network in various climates. The bias for global irradiance comprises between −6 and 25 W m⁻². The RMSE ranges from 20 W m⁻² (3% of the mean observed irradiance) to 36 W m⁻² (5%) and the correlation coefficient ranges between 0.95 and 0.99. The bias for the direct irradiance comprises between −48 and +33 W m⁻². The root mean square error (RMSE) ranges from 33 W m⁻² (5%) to 64 W m⁻² (10%). The correlation coefficient ranges between 0.84 and 0.98. This work demonstrates the quality of the McClear model combined with MACC products, and indirectly the quality of the aerosol properties modelled by the MACC reanalysis

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Section: Aerosols Propertiesmentioning

confidence: 99%

McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions

et al. 2013

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“…Previous studies show that certain relationships between the surface reflectance at visible channels (0.67 Remote Sens. 2017, 9, 253 5 of 21 and 0.47 µm) and reflectance at near-infrared/ infrared channels (e.g., 0.86, 1.6, 2.1 and 3.75 µm) can be found for certain surface types depending on instruments [5,6,[38][39][40]. Figure 2 shows an example of the relationships between the spectral surface reflectance at visible and near-infrared channels for different surface types over China.…”

Section: Surface Parameterizationmentioning

confidence: 99%

“…One year (2008) cloud-free MODIS C6 TOA reflectance and corresponding MODIS surface reflectance product (MOD09A1) were collected over the study region as shown in Figure 1. Previous studies show that certain relationships between the surface reflectance at visible channels (0.67 and 0.47 µm) and reflectance at near-infrared/ infrared channels (e.g., 0.86, 1.6, 2.1 and 3.75 µm) can be found for certain surface types depending on instruments [5,6,[38][39][40]. Figure 2 shows an Previous attempts show the possibilities of using MODIS data product for surface parameterization and atmospheric parameters retrieval for instruments like Landsat (similar to GF4) [26,37].…”

Section: Surface Parameterizationmentioning

confidence: 99%

“…In order to avoid potential aerosol contamination in the analysis, match-ups with AODs at 0.55 µm were constrained to values smaller than 0.1 (obtained from MODIS C6 aerosol product) and the scattering angle between 110 • and 160 • were used in the statistical analysis [38]. The contribution of Rayleigh scattering for channel 0.86 µm was removed from the TOA reflectance following DB algorithm.…”

Section: Surface Parameterizationmentioning

confidence: 99%

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SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm

et al. 2017

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Abstract:The recently launched Chinese GaoFen-4 (GF4) satellite provides valuable information to obtain geophysical parameters describing conditions in the atmosphere and at the Earth's surface. The surface reflectance is an important parameter for the estimation of other remote sensing parameters linked to the eco-environment, atmosphere environment and energy balance. One of the key issues to achieve atmospheric corrected surface reflectance is to precisely retrieve the aerosol optical properties, especially Aerosol Optical Depth (AOD). The retrieval of AOD and corresponding atmospheric correction procedure normally use the full radiative transfer calculation or Look-Up- Table (LUT) methods, which is very time-consuming. In this paper, a Simplified AtmospHeric correction AlgoRithm for gAofen data (SAHARA) is presented for the retrieval of AOD and corresponding atmospheric correction procedure. This paper is the first part of the algorithm, which describes the aerosol retrieval algorithm. In order to achieve high-accuracy analytical form for both LUT and surface parameterization, the MODIS Dark-Target (DT) aerosol types and Deep Blue (DB) similar surface parameterization have been proposed for GF4 data. Limited Gaofen observations (i.e., all that were available) have been tested and validated. The retrieval results agree quite well with MODIS Collection 6.0 aerosol product, with a correlation coefficient of R 2 = 0.72. The comparison between GF4 derived AOD and Aerosol Robotic Network (AERONET) observations has a correlation coefficient of R 2 = 0.86. The algorithm, after comprehensive validation, can be used as an operational running algorithm for creating aerosol product from the Chinese GF4 satellite.

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“…Meanwhile the Synergetic Aerosol Retrieval (SYNAER, Holzer-Popp et al, 2002a, b, 2008 provides aerosol optical depth observations from ENVISAT (Environmental Satellite) Advanced Along Track Scanning Radiometer (AATSR) and Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) separately for the soot, mineral, water soluble, water insoluble and sea salt components. This opens the possibility to assimilate aerosols in a component-wise approach over both land and ocean.…”

Section: Introductionmentioning

confidence: 99%

Observation operator for the assimilation of aerosol type resolving satellite measurements into a chemical transport model

Schroedter-Homscheidt

Elbern

Popp

2010

Atmos. Chem. Phys.

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Abstract. Modelling of aerosol particles with chemical transport models is still based mainly on static emission databases while episodic emissions cannot be treated sufficiently. To overcome this situation, a coupling of chemical mass concentration modelling with satellite-based measurements relying on physical and optical principles has been developed. This study deals with the observation operator for a component-wise assimilation of satellite measurements. It treats aerosol particles classified into water soluble, water insoluble, soot, sea salt and mineral dust containing aerosol particles in the atmospheric boundary layer as separately assimilated aerosol components. It builds on a mapping of aerosol classes used both in observation and model space taking their optical and chemical properties into account. Refractive indices for primary organic carbon particles, anthropogenic particles, and secondary organic species have been defined based on a literature review. Together with a treatment of different size distributions in observations and model state, this allows transforming the background from mass concentrations into aerosol optical depths. A two-dimensional, variational assimilation is applied for component-wise aerosol optical depths. Error covariance matrices are defined based on a validation against AERONET sun photometer measurements. Analysis fields are assessed threefold: (1) through validation against AERONET especially in Saharan dust outbreak situations, (2) Sulphur Dioxide Network for soot-containing particles, and (3) through comparison with measurements of the water soluble components SO 4 , NH 4 , and NO 3 conducted by the EMEP (European Monitoring and Evaluation Programme) network. Separately, for the water soluble, the soot and the mineral dust aerosol components a bias reduction and subsequent a root mean square error reduction is observed in the analysis for a test period from July to November 2003. Additionally, examples of an improved analysis during wildfire and dust outbreak situations are shown.

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Retrieving aerosol optical depth and type in the boundary layer over land and ocean from simultaneous GOME spectrometer and ATSR‐2 radiometer measurements, 1, Method description

Cited by 40 publications

References 31 publications

McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions

McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions

SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm

Observation operator for the assimilation of aerosol type resolving satellite measurements into a chemical transport model

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