Cloud and surface classification using SCIAMACHY polarization measurement devices

Lotz, W.; Vountas, Marco; Dinter, Tilman; Burrows, J. P.

doi:10.5194/acp-9-1279-2009

Cited by 15 publications

(11 citation statements)

References 15 publications

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“…The PMDs cover the spectral range of the main channels but provide a better spatial resolution of approximately 30 km along track and 7 km across track, leading to about 8 PMD measurements in one 30×60 km 2 ground scene. Due to this higher spatial resolution, PMD measurements are used in most of the current SCIAMACHY cloud fraction algorithms (Krijger et al, 2005;Lotz et al, 2009) as sub-pixel information for the much larger covered areas based on the SCIAMACHY science spectra. In order to further enhance the accuracy of the cloud fraction determined for SCIAMACHY, we use a different approach that is based on the analysis of spectral measurements performed by the MERIS sensor, which is explained in the next subsection.…”

Section: Sciamachymentioning

confidence: 99%

“…This is due to the higher spatial resolution of PMDs compared to the science channels of SCIAMACHY. Then a set of thresholds and constraints is used in order to determine the cloud fraction (Tuinder et al, 2004;Loyola, 2004;Krijger et al, 2005;Grzegorski et al, 2006;Rozanov et al, 2006;Lotz et al, 2009). Moreover, some of the existing trace gas retrievals are making use of the SCIAMACHY cloud fraction derived from the FRESCO algorithm, which determines an "effective" cloud fraction using the oxygen A band under the assumption of an a priori chosen cloud albedo of 0.8 (Koelemeijer et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Synergetic cloud fraction determination for SCIAMACHY using MERIS

et al. 2011

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Abstract.Since clouds play an essential role in the Earth's climate system, it is important to understand the cloud characteristics as well as their distribution on a global scale using satellite observations. The main scientific objective of SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) onboard the ENVISAT satellite is the retrieval of vertical columns of trace gases.On the one hand, SCIAMACHY has to be sensitive to low variations in trace gas concentrations which means the ground pixel size has to be large enough. On the other hand, such a large pixel size leads to the problem that SCIA-MACHY spectra are often contaminated by clouds. SCIA-MACHY spectral measurements are not well suitable to derive a reliable sub-pixel cloud fraction that can be used as input parameter for subsequent retrievals of cloud properties or vertical trace gas columns. Therefore, we use MERIS/ENVISAT spectral measurements with its high spatial resolution as sub-pixel information for the determination of MerIs Cloud fRation fOr Sciamachy (MICROS). Since MERIS covers an even broader swath width than SCIA-MACHY, no problems in spatial and temporal collocation of measurements occur. This enables the derivation of a SCIA-MACHY cloud fraction with an accuracy much higher as compared with other current cloud fractions that are based on SCIAMACHY's PMD (Polarization Measurement Device) data.We present our new developed MICROS algorithm, based on the threshold approach, as well as a qualitative validation of our results with MERIS satellite images for different locations, especially with respect to bright surfaces such as snow/ice and sands. In addition, the SCIAMACHY cloud fractions derived from MICROS are intercompared with other current SCIAMACHY cloud fractions based onCorrespondence to: C. Schlundt (cornelia@iup.physik.uni-bremen.de) different approaches demonstrating a considerable improvement regarding geometric cloud fraction determination using the MICROS algorithm.

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Section: Sciamachymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synergetic cloud fraction determination for SCIAMACHY using MERIS

et al. 2011

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“…The algorithm is based on spectral threshold tests and mostly utilizes the difference in reflectance between clouds and the snow covered surface around 1.6 µm. A similar algorithm was used by Lotz et al (2009).…”

Section: Introductionmentioning

confidence: 99%

What do satellite backscatter ultraviolet and visible spectrometers see over snow and ice? A study of clouds and ozone using the A-train

et al. 2010

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Abstract. In this paper, we examine how clouds over snow and ice affect ozone absorption and how these effects may be accounted for in satellite retrieval algorithms. Over snow and ice, the Aura Ozone Monitoring Instrument (OMI) Raman cloud pressure algorithm derives an effective scene pressure. When this scene pressure differs appreciably from the surface pressure, the difference is assumed to be caused by a cloud that is shielding atmospheric absorption and scattering below cloud-top from satellite view. A pressure difference of 100 hPa is used as a crude threshold for the detection of clouds that significantly shield tropospheric ozone absorption. Combining the OMI effective scene pressure and the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) cloud top pressure, we can distinguish between shielding and non-shielding clouds.To evaluate this approach, we performed radiative transfer simulations under various observing conditions. Using cloud vertical extinction profiles from the CloudSat Cloud Profiling Radar (CPR), we find that clouds over a bright surface can produce significant shielding (i.e., a reduction in the sensitivity of the top-of-the-atmosphere radiance to ozone absorption below the clouds). The amount of shielding provided by clouds depends upon the geometry (solar and satellite zenith angles) and the surface albedo as well as cloud optical thickness. We also use CloudSat observations to qualitatively evaluate our approach. The CloudSat, Aqua, and Aura satellites fly in an afternoon polar orbit constellation with ground overpass times within 15 min of each other.Correspondence to: A. P. Vasilkov (alexander vassilkov@ssaihq.com) The current Total Ozone Mapping Spectrometer (TOMS) total column ozone algorithm (that has also been applied to the OMI) assumes no clouds over snow and ice. This assumption leads to errors in the retrieved ozone column. We show that the use of OMI effective scene pressures over snow and ice reduces these errors and leads to a more homogeneous spatial distribution of the retrieved total ozone.

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“…Several cloud-detection algorithms were developed for use in SCIAMACHY or GOME, the predecessor of SCIAMACHY, like ICFA (Kuze and Chance, 1994), OCRA (Loyola, 1998), CRAG (von Bargen et al, 2000), CRUSA (Wenig, 2001), FRESCO (Koelemeijer et al, 2001), SACURA (Kokhanovsky et al, 2003;Rozanov and Kokhanovsky, 2004), ROCINN (Loyola et al, 2007, FRESCO+ (Wang et al, 2008), GOMECAT (Kurosu et al, 1998), HICRU (Grzegorski et al, 2004), SPICS (Lotz et al, 2009) and MICROS (Schlundt et al, 2011). Other algorithms are developed for the more recently launched OMI as the instrument does not measure the O 2 A band nor broadband PMD measurements as in SCIAMACHY, such as Inverse Cloud Model (Accarreta et al, 2004), Rotational Raman scattering cloud pressure (Joiner and Vasilkov, 2006).…”

Section: Introductionmentioning

confidence: 99%

Improved identification of clouds and ice/snow covered surfaces in SCIAMACHY observations

et al. 2011

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Abstract. In the ultra-violet, visible and near infra-red wavelength range the presence of clouds can strongly affect the satellite-based passive remote sensing observation of constituents in the troposphere, because clouds effectively shield the lower part of the atmosphere. Therefore, cloud detection algorithms are of crucial importance in satellite remote sensing. However, the detection of clouds over snow/ice surfaces is particularly difficult in the visible wavelengths as both clouds an snow/ice are both white and highly reflective. The SCIAMACHY Polarisation Measurement Devices (PMD) Identification of Clouds and Ice/snow method (SPICI) uses the SCIAMACHY measurements in the wavelength range between 450 nm and 1.6 µm to make a distinction between clouds and ice/snow covered surfaces, specifically developed to identify cloud-free SCIAMACHY observations. For this purpose the on-board SCIAMACHY PMDs are used because they provide higher spatial resolution compared to the main spectrometer measurements. In this paper we expand on the original SPICI algorithm (Krijger et al., 2005a) to also adequately detect clouds over snow-covered forests which is inherently difficult because of the similar spectral characteristics. Furthermore the SCIAMACHY measurements suffer from degradation with time. This must be corrected for adequate performance of SPICI over the full SCIAMACHY time range. Such a correction is described here. Finally the performance of the new SPICI algorithm is compared with various other datasets, such as from FRESCO, MICROS and AATSR, focusing on the algorithm improvements.

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Cloud and surface classification using SCIAMACHY polarization measurement devices

Cited by 15 publications

References 15 publications

Synergetic cloud fraction determination for SCIAMACHY using MERIS

Synergetic cloud fraction determination for SCIAMACHY using MERIS

What do satellite backscatter ultraviolet and visible spectrometers see over snow and ice? A study of clouds and ozone using the A-train

Improved identification of clouds and ice/snow covered surfaces in SCIAMACHY observations

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