Abstract
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 SCIAMACHY spectra are often contaminated by clouds. SCIAMACHY 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 SCIAMACHY, no problems in spatial and temporal collocation of measurements occur. This enables the derivation of a SCIAMACHY 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 on different approaches demonstrating a considerable improvement regarding geometric cloud fraction determination using the MICROS algorithm.
Highlights
Clouds are the subject of interest for the numerical weather prediction, global circulation models, and climate studies
Since accurate cloud information is needed for reliable aerosol, trace gas and cloud optical property retrievals, we are aimed at improving the current MERIS cloud screening algorithm (Kokhanovsky et al, 2009) in order to improve the accuracy of the SCIAMACHY cloud fraction for the ground pixels at nadir observation
A dust index (DI) based on SCIAMACHY near infrared (NIR) measurements is introduced for the SCIAMACHY cloud fraction calculation, which is defined as DI = < RTOA,scia(1560 nm) >
Summary
Clouds are the subject of interest for the numerical weather prediction, global circulation models, and climate studies They are masking/modifying the signal of interest, for instance, in satellite retrievals of snow, land surface or aerosol properties and trace gas concentrations. Since accurate cloud information is needed for reliable aerosol, trace gas and cloud optical property retrievals, we are aimed at improving the current MERIS cloud screening algorithm (Kokhanovsky et al, 2009) in order to improve the accuracy of the SCIAMACHY cloud fraction for the ground pixels at nadir observation. In this paper we present a newly developed algorithm for determining a more accurate geometric cloud fraction for SCIAMACHY ground scenes at nadir using MERIS spectral measurements, which we have called MICROS, i.e. MERIS cloud fraction for SCIAMACHY.
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