Ancillary Data Uncertainties within the SeaDAS Uncertainty Budget for Ocean Colour Retrievals

Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper, we analyse the uncertainty budget of SeaDAS atmospheric correction algorithm. A metrological approach is followed, where each error sources are identified an tree diagram and briefly discussed. algorithms depend on ancillary variables (such as meteorological properties column densities gases), yet these were not studied previously detail. To for first time, spread ERA5 ensemble used estimate data, which then propagated to remote sensing reflectances using a Monte Carlo example data set, wind speed relative humidity found be main contributors (among parameters) reflectance uncertainties.