| Summary: | As hyperspectral instruments can provide the detailed spectral information, a new spectral similarity method for detecting and differentiating dust from non-dust scenes using the Atmospheric Infrared Sounder (AIRS) observations has been developed. The detection is based on a pre-defined Dust Spectral Similarity Index (DSSI), which was calculated from the accumulated brightness temperature differences between selected 16 AIRS observation channels, in the thermal infrared region of 800–1250 cm−1. It has been demonstrated that DSSI can effectively separate the dust from non-dust by elevating dust signals. For underlying surface covered with dust, the DSSI tends to show values close to 1.0. However, the values of DSSI for clear sky surfaces or clouds (ice and water) are basically lower than those of dust, as their spectrums have significant differences with dust. To evaluate this new simple DSSI dust detection algorithm, several Asia dust events observed in northern China were analyzed, and the results agree favorably with those from the Moderate resolution Imaging Spectro radiometer (MODIS) and Cloud Aerosol LiDAR with Orthogonal Polarization (CALIOP) observations.
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