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The last IPCC assessment states that clouds and aerosols remain a challengein climate prediction with Global Climate Models. Therefore, NASA's 2017Decadal Survey has made them, along with convection and precipitation, apriority target for future missions under the ACCP banner, now AtmosphericObserving System. Atmospheric science is now more than ever driving renewal inremote sensing to probe clouds and aerosols more accurately and with improvedsampling. Here, we bring new results that support Differential OpticalAbsorption Spectroscopy (DOAS) using oxygen absorption features in thevisible/near-IR spectrum. With known concentration and cross-section, theremaining unknown in O$_2$ DOAS is the path the light followed through theabsorbing gas. In presence of scattering by cloud or aerosol particles, thatpath is broken at each interaction. Cumulative pathlength through the gas is arandom variable, and its probability distribution function (PDF) will containdesirable information about the clouds or aerosols. Here, we computestatistical moments of the that PDF, showing that mean and variance conveydifferent pieces of information, namely, size and opacity of the medium. Wealso extend the connection between geometric thickness and mean pathlength inpresence of absorption by the scattering particles. We view pathlength momentsas intermediate products that can be obtained from O$_2$ spectroscopy, anddescribe an algorithm to do that. In turn, these moments yield cloud or aerosolprofile parameters of interest: layer geometric and optical thicknesses. Onenormally thinks about active sensors, radars and lidars, for such atmosphericprofiling. Here, the profile is parameterized and representative of ahorizontal average determined by multiple scattering. However, there areadvantages in passive vs active instrumentation, starting with the possibilityof imaging over a large swath.

Moments of Sunlight Pathlength in Water and Aerosol Clouds from O$_2$ Spectroscopy: Exploitable Parameter Sensitivities