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  • Author or Editor: Mark A. Saunders x
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Christopher J. Merchant
and
Mark A. Saunders

Abstract

The presence of stratospheric aerosol can bias the results of infrared satellite retrievals of sea surface temperature (SST) and total precipitable water (TPW). In the case of linear SST retrieval using the Along Track Scanning Radiometer (ATSR), on the ESA European remote-sensing satellites, constant coefficients can be found that give negligible bias (less than 0.1 K) over a wide range of aerosol amount (11-μm optical thickness from 0.0 to 0.022). For TPW retrieval, in contrast, the biases associated with stratospheric aerosol are less satisfactory (2 kg m−2 or greater across a range of 11-μm optical thickness of 0.0–0.01). However, the authors show how to find optimal aerosol-dependent retrieval coefficients for any stratospheric aerosol distribution from knowledge of the mean and variance of that aerosol distribution. Examples of SST and TPW retrieval using simulated ATSR brightness temperature data are given.

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Matthew S. Jones
,
Mark A. Saunders
, and
Trevor H. Guymer

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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