Cloud information content in EPIC/DSCOVR's oxygen A- and B-band channels: An optimal estimation approach. (September 2018) - Davis, Anthony B.; Merlin, Guillaume; Cornet, Céline; Labonnote, Laurent C.; Riédi, Jérôme; Ferlay, Nicolas; Dubuisson, Philippe; Min, Qilong; Yang, Yuekui; Marshak, Alexander
Highlights: Cloud information content in EPIC's differential optical absorption spectroscopy measurements for the O2 A- and B-bands is quantified using optimal estimation theory, as are the predicted uncertainties on retrieved cloud properties. We revisit an early theoretical assessment of EPIC cloud profiling capability that ignored instrumental error, and conclude that only cloud top height can be retrieved with acceptable uncertainty under most circumstances. Consequences of our findings for ongoing EPIC cloud top height retrieval algorithm development are discussed, including the impact of forward model bias and uncertainty in other cloud properties required by the retrieval. Abstract: We use computational 1D radiative transfer modeling and the formalism of optimal estimation to quantify the cloud information content in the oxygen A- and B-band channels of the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate ObserVatoRy (DSCOVR) platform. EPIC/DSCOVR images the sunlit hemisphere of our planet from  ≈ 1, 500, 000 km away with  ≈ 8 km pixels at the center of the disc. EPIC pixel-scale spectral data is used to estimate in-band/continuum radiance ratios for O2 's A- and B-bands, from which one can derive, in principle, both cloud top height and cloud geometrical thickness. We use the general framework of optimal estimation theory to show that in practice, once measurement error is factored in, only cloud top height can be reliably inferred. With that limitation in mind, we discuss the ramifications for retrieval algorithm development.