Development of a Cloud Mask from High-Resolution Polarized Aircraft Observations

Accurate cloud detection is fundamental for atmospheric remote sensing, particularly for airborne observations where high spatial resolution enables detailed characterization of cloud fields. We present a cloud mask algorithm for the spectrometer of the Munich Aerosol Cloud Scanner (specMACS), a hyperspectral polarimetric imaging system operated aboard the German research aircraft HALO.

 

specMACS features four special RGB cameras that simultaneously provide polarization measurements at high spatial resolution. This enables novel cloud detection approaches by exploiting the difference in polarization signatures between clouds and cloudless-sky ocean surfaces. Specular reflection from the ocean surface produces polarized signals, while cloud droplets depolarize incoming radiation through multiple scattering, creating a clear physical contrast for cloud identification.

 

Our algorithm uses radiative transfer simulations with the libRadtran package to generate reference radiances for cloudless sky atmospheric conditions. We systematically vary solar geometry, aerosol properties, and surface conditions in the simulations to establish classification criteria for cloud detection. Expected surface values from radiative transfer simulations are interpolated across the full high-resolution field-of-view and compared against specMACS observations to identify cloud presence.

 

The algorithm was developed and tested using data from the Persistent EarthCARE Underflight Studies of the ITCZ and Organized Convection (PERCUSION) campaign over the tropical Atlantic in 2024. Initial results demonstrate reliable cloud detection across a range of optical thicknesses, providing robust cloud masks for subsequent retrieval applications. This work establishes a foundation for improved quality in ocean and atmospheric retrievals from high-resolution polarimetric aircraft observations.