Sensitivity of Ocean Color Atmospheric Correction to Uncertainties in Ancillary Data: A Global Analysis With SeaWiFS Data

Atmospheric correction (AC) algorithms for ocean color (OC) data processing usually rely on ancillary data documenting the atmosphere and the sea state to help the calculation of the remote sensing reflectance
$R_{\text {RS}}$
from the radiance measured by a space sensor. This study aims at assessing the impact that the uncertainties associated with these ancillary data have on the AC outputs. For this objective, a full year of global Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery is processed with the standard AC algorithm
l2gen
of the National Aeronautics and Space Administration with different sets of ancillary data, the reference case with National Centers for Environmental Prediction (NCEP) Reanalysis-2 meteorological data and satellite ozone products, as well as with ten ensemble members from the European Centre for Medium-Range Weather Forecast (ECMWF) CERA-20C data. The spread within the ensemble data and the differences with respect to the reference case are taken as a measure of the uncertainties associated with ancillary data. The impact on
$R_{\text {RS}}$
of perturbations in ancillary variables vary in space, the variables having the largest effects being wind speed and relative humidity, and ozone at bands where ozone absorption is largest, while sea-level pressure and precipitable water have the smallest effect. Sensitivity coefficients quantifying the relationship between perturbations in ancillary variables and effects on
$R_{\text {RS}}$
change with variable and wavelength. At the global scale, the variations found on
$R_{\text {RS}}$
when ancillary data are perturbed are usually small but not negligible and should be considered in the ocean color (OC) data uncertainty budget.