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Reducing Cloud Contamination in ATSR Averaged Sea Surface Temperature Data

Matthew S. JonesSouthampton Oceanography Centre, Southampton, United Kingdom

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Mark A. SaundersMullard Space Science Laboratory, University College London, Surrey, United Kingdom

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Trevor H. GuymerSouthampton Oceanography Centre, Southampton, United Kingdom

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Abstract

The Along Track Scanning Radiometer (ATSR) was launched in July 1991 on the European Space Agency's first remote sensing satellite ERS-1. ATSR has the potential to measure sea surface temperature (SST) to a precision of 0.3 K, which is more than double the accuracy of any previously flown infrared radiometer. A key factor limiting ATSR's performance is remnant cloud contamination. Examination of the 0.5° spatially averaged ATSR SST data (version 500) from the South Atlantic for the whole of 1992 and 1993 shows the presence of regional cloud contamination in the night SST measurements. The authors establish a figure of 5.7% as a lower limit for this nighttime cloud contamination. The contamination leads to differences between day and night mean SSTs and to poor comparisons with in situ thermosalinograph SST data. A new cloud filtering process designed for postprocessing of the data is proposed to remove the contamination. The algorithm presented here relies on assumptions that the day data are less cloud contaminated than the night data and that a large proportion of the SST variability can he explained by an annual and semiannual model. Testing the filtering algorithm shows that differences between the day and night SST signals are substantially reduced and that comparisons with the thermosalinograph SST data improve by a factor of 3 in rms scatter and by 0.3 K in the mean difference.

Abstract

The Along Track Scanning Radiometer (ATSR) was launched in July 1991 on the European Space Agency's first remote sensing satellite ERS-1. ATSR has the potential to measure sea surface temperature (SST) to a precision of 0.3 K, which is more than double the accuracy of any previously flown infrared radiometer. A key factor limiting ATSR's performance is remnant cloud contamination. Examination of the 0.5° spatially averaged ATSR SST data (version 500) from the South Atlantic for the whole of 1992 and 1993 shows the presence of regional cloud contamination in the night SST measurements. The authors establish a figure of 5.7% as a lower limit for this nighttime cloud contamination. The contamination leads to differences between day and night mean SSTs and to poor comparisons with in situ thermosalinograph SST data. A new cloud filtering process designed for postprocessing of the data is proposed to remove the contamination. The algorithm presented here relies on assumptions that the day data are less cloud contaminated than the night data and that a large proportion of the SST variability can he explained by an annual and semiannual model. Testing the filtering algorithm shows that differences between the day and night SST signals are substantially reduced and that comparisons with the thermosalinograph SST data improve by a factor of 3 in rms scatter and by 0.3 K in the mean difference.

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