Aerosol Retrievals from the Multiyear Multisatellite AVHRR Pathfinder Atmosphere (PATMOS) Dataset for Correcting Remotely Sensed Sea Surface Temperatures

Abstract

Eight-year (1990?98), two-satellite (NOAA-11 and -14), global daily ?(110 km)2 gridded observations from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmosphere (PATMOS) dataset have been previously merged with the Pathfinder Matchup Database (PFMDB) and used to develop the Phase I aerosol correction for sea surface temperatures (SSTs) from AVHRR. In this study, this unique PATMOS?BUOY matchup dataset (N = 105 831) is used to derive and quality control an advanced set of aerosol parameters to be used in the Phase II algorithm: aerosol optical depths in channels 1 (?1 = 0.63 ?m) and 2 (?2 = 0.83 ?m), τ1 and τ2, and Ångström exponent α = ?ln(τ1/τ2)/ln(?1/?2). Inaccurate retrievals at low sun and outliers are removed from the data. PATMOS global, multiyear, multisatellite aerosol properties, derived from cloud-free portions of the (110 km)2 grid, resemble many features previously observed in the space?time-restricted, (8 km)2 resolution Aerosol Observation (AEROBS) operational retrievals, in spite of a different spatial resolution, cloud screening, and sampling. Histograms of τ and α are accurately fit by lognormal and normal probability density functions, respectively. Retrievals of τ2 are consistent with τ1 at low τ, but reveal high multiplicative bias, resulting in a low additive bias in α. Random errors in α are inversely proportional to τ, with signal-to-noise ratio well approximated as ? = τ1/τ1o. Parameter τ1o (τ threshold at which signal in α compares to its noise, i.e., ? = 1) in PATMOS data (τ1o ? 0.11 ± 0.01) is less than in AEROBS (τ1o ? 0.18 ± 0.02), since noise is suppressed by the additional spatial averaging in PATMOS. The effect of cloud screening and sampling is also quantified. PATMOS τ1, τ2, and α reveal a strong trend against cloud amount, which is not fully understood, and some residual artificial time/angle trends, due to undercorrected calibration errors and remaining algorithm problems. But overall, they show a high degree of self- and interconsistency, thus providing a superior set of aerosol predictors to be used in the Phase II SST aerosol correction algorithm.