Seven years of global retrieval of cloud properties using space-borne data of GOME

Lelli, Luca; Kokhanovsky, Alexander A.; Розанов, В. В.; Vountas, Marco; Sayer, A. M.; Burrows, J. P.

doi:10.5194/amt-5-1551-2012

Cited by 33 publications

(46 citation statements)

References 78 publications

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“…To similar findings came , while extending the total amount of analyzed GOME ground scenes (931 in total). More recently, Lelli et al (2012) analyzed GOME-derived cloud top heights and co-located them with ground-based measurements, confirming previous results. As for SCIAMACHY, earliest validation was carried out with a ground-based cloud profiling radar located at the site of Cabauw (Netherlands) showing a qualitative agreement between the two products .…”

Section: Introductionsupporting

confidence: 81%

“…In the presence of a scattering layer of optical depth large enough (as is the case of terrestrial water clouds), the contribution of multiple scattering to the transfer of radiation below a cloud outnumbers the contribution of single scattering of the direct beam. The attenuation of the monotonically decreasing diffusive component of the light field between the surface and the cloud bottom is described by virtue of the cloud transmittance introduced in Lelli et al (2012, Equations 3-6, p. 1554.…”

Section: The Atmospheric Forward Modelmentioning

confidence: 99%

“…This leads to the practical motivation that a-priori are needed, especially for SACURA, that was implemented as operational algorithm aiming at the generation of global long-term cloud record from measurements in nadir geometry only. As such, the major factors affecting the accuracy of the retrieved cloud optical parameters are: (1) surface albedo, as function of which COT errors can total up to ±20% for various viewing geometries (Lelli et al, 2012, Figure 2); (2) radiometric calibration and stability, since an error of 2% in TOA radiance at non-absorbing wavelengths causes a COT uncertainty of 12% (Lelli et al, 2014, Figure 15); (3) the cloud fields' bumpiness, that induces a systematic COT underestimation by an average factor of 20%, if clouds are assumed as plane-parallel scattering layers (Varnai and Marshak, 2007).…”

Section: The Atmospheric Forward Modelmentioning

confidence: 99%

“…Accounting for the quality flag scheme allows the extraction from the record of realistic cloud scenarios. To this end, it has been advised (Lelli et al, 2012, Section 4.1, p. 1561) the aggregation of fully converged retrievals (flag 5) together with retrievals converged for CTH only (flag 2) and with those flagged 3, given a CTH < 5 km, if multi-layer clouds are to be discarded. Pixels labeled with flag 2 are to be considered only when investigating the bottom altitude of clouds, while the remaining retrievals, labeled 0 (no retrieval) and 4 (no convergence), must be dropped.…”

Section: Inversion Of the Measurementsmentioning

confidence: 99%

“…Therefore, it is crucial to assess the accuracy of SCIAMACHY cloud products by intercomparison with cloud properties derived from independent measurements. In particular, cloud altitude and optical thickness for SCIAMACHY and GOME are derived with the Semi-Analytical CloUd Retrieval Algorithm (SACURA) Lelli et al, 2012), while the portion of a satellite ground pixel occupied by a cloud (termed cloud fraction or cloud cover) is calculated with the Optical Cloud Recognition Algorithm OCRA (Loyola and Ruppert, 1998;Loyola, 2004;Lutz et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of SCIAMACHY ESA/DLR Cloud Parameters Version 5.02 by Comparisons to Ground-Based and Other Satellite Data

Lelli

Weber

Burrows

2016

Front. Environ. Sci.

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This paper reports on the evaluation of long-term cloud products as retrieved from measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument with the DLR/ESA processor in its version 5.02 and the operational implementation of the Semi-Analytical CloUd Retrieval Algorithm SACURA. The comparison is performed against spaceborne and ground-based cloud data. The satellite records are the SCIAMACHY/SACURA in its scientific implementation and the Global retrieval of ATSR cloud parameters and evaluation (GRAPE) data set, in its version 3.2, generated for the nadir view of the Advanced Along-Track Scanning Radiometer (AATSR) instrument onboard ENVISAT. Ground-based data are derived from profiles of micro-pulse lidars, continuously operated at three Atmospheric Radiation Measurement (ARM) research facilities. They are, namely, North Slope Alaska, Southern Great Plains and Tropical Western Pacific-Nauru, located in three different latitude belts. It has been found that SCIAMACHY cloud top heights, inferred in the visible-near infrared, have a seasonal dependent overestimation in range 0.6-1.0 km when compared to the thermal infrared-derived AATSR cloud top heights. The comparison with the in-situ cloud retrievals reveals that SCIAMACHY cloud altitudes are more accurate for local cloud cover values > 0.6.

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Section: Introductionsupporting

confidence: 81%

Section: The Atmospheric Forward Modelmentioning

confidence: 99%

Section: The Atmospheric Forward Modelmentioning

confidence: 99%

Section: Inversion Of the Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of SCIAMACHY ESA/DLR Cloud Parameters Version 5.02 by Comparisons to Ground-Based and Other Satellite Data

Lelli

Weber

Burrows

2016

Front. Environ. Sci.

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Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME‐2 and SCIAMACHY

et al. 2017

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The primary goal of this paper is to introduce two new surface reflectivity climatologies. The two databases contain the Lambertian‐equivalent reflectivity (LER) of the Earth's surface, and they are meant to support satellite retrieval of trace gases and of cloud and aerosol information. The surface LER databases are derived from the Global Ozone Monitoring Experiment (GOME)‐2 and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instruments and can be considered as improved and extended descendants of earlier surface LER climatologies based on the Total Ozone Mapping Spectrometer (TOMS), GOME‐1, and Ozone Monitoring Instrument (OMI) instruments. The GOME‐2 surface LER database consists of 21 wavelength bands that span the wavelength range from 335 to 772 nm. The SCIAMACHY surface LER database covers the wavelength range between 335 and 1670 nm in 29 wavelength bands. The two databases are made for each month of the year, and their spatial resolution is 1° × 1°. In this paper we present the methods that are used to derive the surface LER; we analyze the spatial and temporal behavior of the surface LER fields and study the amount of residual cloud contamination in the databases. For several surface types we analyze the spectral surface albedo and the seasonal variation. When compared to the existing surface LER databases, both databases are found to perform well. As an example of possible application of the databases we study the performance of the Fast Retrieval Scheme for Clouds from the Oxygen A‐band (FRESCO) cloud information retrieval when it is equipped with the new surface albedo databases. We find considerable improvements. The databases introduced here can not only improve retrievals from GOME‐2 and SCIAMACHY but also support those from other instruments, such as TROPOspheric Monitoring Instrument (TROPOMI), to be launched in 2017.

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Wave Interaction with Land, Water and Air

Solimini

2016

Remote Sensing and Digital Image Processing

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Seven years of global retrieval of cloud properties using space-borne data of GOME

Abstract: Abstract. We present a global and regional multi-annual

Cited by 33 publications

References 78 publications

Evaluation of SCIAMACHY ESA/DLR Cloud Parameters Version 5.02 by Comparisons to Ground-Based and Other Satellite Data

Evaluation of SCIAMACHY ESA/DLR Cloud Parameters Version 5.02 by Comparisons to Ground-Based and Other Satellite Data

Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME‐2 and SCIAMACHY

Wave Interaction with Land, Water and Air

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