The U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has developed a daily global 5-km product suite based on satellite observations to monitor thermal stress on coral reefs. These products fulfill requests from coral reef managers and researchers for higher resolution products by taking advantage of new satellites, sensors and algorithms. Improvements of the 5-km products over CRW's heritage global 50-km products are derived from: (1) the higher resolution and greater data density of NOAA's next-generation operational daily global 5-km geo-polar blended sea surface temperature (SST) analysis; and (2) implementation of a new SST climatology derived from the Pathfinder SST climate data record. The new products increase near-shore coverage and now allow direct monitoring of 95% of coral reefs and significantly reduce data gaps caused by cloud cover. The 5-km product suite includes SST Anomaly, Coral Bleaching HotSpots, Degree Heating Weeks and Bleaching Alert Area, matching existing CRW products. When compared with the 50-km products and in situ bleaching observations for 2013-2014, the 5-km products identified known thermal stress events and matched bleaching observations. These near reef-scale products significantly advance the ability of coral reef researchers and managers to monitor coral thermal stress in near-real-time.

Abstract. Since 1990, the NOAA National Environmental Satellite Data and Information Service (NESDIS) has provided satellite-derived sea surface temperature (SST) measurements based on nonlinear SST algorithms, using advanced very high resolution radiometer (AVHRR) multiple-infrared window channel data. This paper develops linear and nonlinear SST algorithms from the radiative transfer equation. It is shown that the nonlinear algorithms are more accurate than linear algorithms but that the functional dependence of the nonlinearity is data dependent. This theoretical discussion (sections 2-4) is followed with a discussion in section 5 of the accuracy over a 9-year period of the satellite-derived SST measurements provided by NOAA NESDIS when compared with coincident drifting buoys. Between 1989 and 1998 the global scatter of the daytime satellite SST against drifting buoy measurements has decreased from -0.8 ø to 0.5øC, while the nighttime scatter has remained fairly constant at 0.5øC. An exception to these accuracy measurements occurred after the eruption of Mount Pinatubo in June 1991.

In this paper, a global validation package for satellite aerosol optical thickness retrieval using the Aerosol Robotic Network (AERONET) observations as ground truth is described. To standardize the validation procedure, the optimum time/space match-up window, the ensemble statistical analysis method, the best selection of AERONET channels and the numerical scheme used to interpolate/extrapolate these observations to satellite channels have been identified through sensitivity studies. The package is shown to be a unique tool for more objective validation and inter-comparison of satellite aerosol retrievals, helping to satisfy an increasingly important requirement of the satellite aerosol remote sensing community. Results of applying the package to the 2 nd generation operational aerosol observational data (AEROBS) from NOAA-14/AVHRR in 1998 and to the same year aerosol observation data (CERES-SSF4) from TRMM/VIRS are presented as examples of global validation. The usefulness of the package for identifying improvements to the aerosol optical thickness (τ) retrieval algorithm is also demonstrated. The principal causes of systematic errors in the current NOAA/NESDIS operational aerosol optical thickness retrieval algorithm have been identified and can be reduced significantly, if the correction and adjustment suggested from the global validation are transfer model parameters that reduce systematic errors in τ retrievals are suggested for consideration in our next generation algorithm. Basic features that should be included in the next generation algorithm to reduce random error in τ retrievals and the resulting error in the effective Angstrom wavelength exponent have also been discussed. Compared to the AERONET observation, the NOAA-14/AVHRR (AEROBS) τ values for mean conditions are biased high by 0.05 and 0.08, with random errors of 0.08 and 0.05, at 0.63µm and 0.83µm, respectively. Correspondingly, the TRMM/VIRS (CERES-SSF4) values for mean conditions are biased high by 0.06 and 0.02, with random errors of 0.06 and 0.04, at 0.63µm and 1.61µm, respectively. After corrections and adjustments to the retrieval algorithm, the biases in both channels of AVHRR and VIRS are reduced significantly to values close to zero, although random error is almost unchanged. The effective Angstrom wavelength exponent (α) derived directly from the aerosol optical thicknesses (τs) has been shown to be poorly correlated both before and after adjustments, indicating that random error in the τ measurement (possibly related to aerosol model parameter variations or cloud/surface reflectance contamination) needs to be reduced.

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