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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2018-231
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2018-231
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Jul 2018

Research article | 26 Jul 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements

Soheila Jafariserajehlou1, Linlu Mei1, Marco Vountas1, Vladimir Rozanov1, John Philip Burrows1, and Rainer Hollmann2 Soheila Jafariserajehlou et al.
  • 1Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, Bremen, 28359, Germany
  • 2DWD – Deutscher Wetterdienst, Frankfurter Straße 135, 63067 Offenbach, Germany

Abstract. The accurate identification of the presence of cloud in the ground scenes observed by remote sensing satellites is an end in itself. Our lack of knowledge of cloud at high latitudes increases the error and uncertainty in the evaluation and assessment of the changing impact of aerosol and cloud in a warming climate. A prerequisite for the accurate retrieval of Aerosol Optical Thickness, AOT, is the knowledge of the presence of cloud in a ground scene. In this study observations of the up welling radiance in the visible (VIS), near infrared (NIR), shortwave infrared (SWIR), and the thermal infrared (TIR) are used to determine the reflectance. We have developed a new cloud identification algorithm for application to the observations of Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency (ESA)-Envisat and Sea and Land Surface Temperature Radiometer (SLSTR) on-board the ESA Copernicus Sentinel-3A and -3B. The AATSR/SLSTR Cloud Identification Algorithm (ASCIA) developed addresses the requirements for the study AOT at high latitudes and utilizes time-series measurements. It is assumed that cloud free surfaces have unchanged or little changed patterns for a given sampling period, whereas cloudy or partly cloudy scenes show much higher variability in space and time. In this method, the Pearson Correlation Coefficient (PCC) parameter is used to measure the stability of the atmosphere-surface system observed by satellites. The cloud free surface is classified by analyzing the PCC values at the block scale 25×25km2. Subsequently, the reflection of 3.7μm is used for accurate cloud identification at the scene level either 1×1km2 or 0.5×0.5km2. The ASCIA data product has been validated by comparison with independent observations e,g. Surface synoptic observations (SYNOP), AErosol RObotic NETwork (AERONET) and the following satellite-products from i) ESA standard cloud product from AATSR L2 nadir cloud flag, ii) one method based on clear-snow spectral shape developed at IUP Bremen (Istomina et al., 2010), which we call, ISTO, iii) Moderate Resolution Imaging Spectroradiometer (MODIS). In comparison to ground based SYNOP measurements, we achieved a promising agreement better than 95% and 83% within ±2 and ±1 okta respectively. In general, ASCIA shows an improved performance in comparison to other algorithms applied to AATSR measurements for cloud identification at high latitudes.

Soheila Jafariserajehlou et al.
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Soheila Jafariserajehlou et al.
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We developed a new algorithm for cloud identification over the Arctic. This algorithm called ASCIA, utilizes time-series measurements of Advanced Along-Track Scanning Radiometer (AATSR) on Envisat and Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel-3A and -3B. The data product of ASCIA is compared with three satellite products; ASCIA shows an improved performance compared to them. We validated ASCIA by ground-based measurements and a promising agreement is achieved.
We developed a new algorithm for cloud identification over the Arctic. This algorithm called...
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