An evaluation of atmospheric correction for Geostationary Ocean Color Imager (GOCI) using new ancillary data

Abstract

We investigate improvements to the quality of Geostationary Ocean Color Imager (GOCI) operational ocean color data processing, which requires routine ocean color product production in near real-time (NRT) and scientific reprocessing. The product can be made with the appropriate choice of the ancillary data sources which are used in the processing of Level-2 (L2) ocean color products for deriving ocean color products, e.g., the normalized water-leaving radiances as well as the bio-optical products further derived from these radiances using empirical algorithms. The ancillary dataset includes meteorological data (e.g., wind speed, surface pressure, relative humidity) and concentrations of atmospheric gases (e.g., water vapor, ozone, nitrogen dioxide). In general, the suitable ancillary data is not available when the satellite data is first taken. Therefore, we use best available ancillary data sources for processing satellite data in NRT and then reprocesses the data about two months later to refine the products using the optimal ancillary data. The candidates of ancillary dataset include the National Centers for Environmental Prediction (NCEP) reanalysis-II, the Global Data Assimilation System (GDAS), Global Forecasting System (GFS), National Aeronautics and Space Administration (NASA) Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), the European Centre for Medium-Range Weather Forec