Infrared satellite observations of sea surface temperature (SST) have become essential for many applications in meteorology, climatology, and oceanography. Infrared satellite instruments passively measure the infrared energy reflected and emitted by the Earth\u27s surface, which is then modified by the intervening atmosphere. Tropospheric aerosol concentrations increase infrared signal attenuation and affect the accuracy of infrared satellite SST retrievals. Satellite-derived skin SST with measurements from the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) deployed on ships during the Aerosols and Ocean Science Expeditions (AEROSE) and quality-controlled, collocated subsurface drifter temperatures. Satellite skin SSTs with any possible cloud contamination were removed from the dataset then the remaining measurements were temporally and spatially collocated with the in-situ SST (skin and bulk) measurements. With in-situ SSTskin and filtering of cloud contaminated data, results indicate that, in this region, SSTskin retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the Aqua satellites have cool biases compared to shipboard radiometric measurements. There is also a pronounced negative bias in the Saharan outflow area that can introduce SSTskin errors \u3e1 K at aerosol optical depths \u3e 0.5. In this study, a new method to derive night-time Dust-introduced SST Difference Index (DSDI) algorithms based on simulated brightness temperatures at infrared wavelengths of 3.9, 8.7, 10.8 and 12.0 μm, is derived using Radiative Transfer for TOVS (RTTOV). Algorithm for MODIS measurements were derived by regression of the difference between the MODIS SSTskin and the in-situ measurements against the DSDI. The biases and STD are reduced by 0.25K and 0.19K after the DSDI correction. The goal of this study is to understand better the characteristics of aerosol effects on satellite retrieved infrared SST, and to derive empirical formulae for improved accuracies in aerosol-contaminated regions
