Commonly-occurring stratification and synoptic tendencies lead to liquid clouds and warm precipitation processes in the PBL over large portions of the globe. The climate is so sensitive to these low-level clouds that they are identified in IPCC reports as major uncertainty sources for climate prediction; their representation in GCMs thus needs improvement. PBL clouds have therefore been scrutinized in numerous field campaigns over both ocean and land. The main method for measuring clouds in field campaigns is in-situ airborne probing and, though these data are invaluable, it is widely recognized that spatial and temporal sampling is innately poor. We then turn to remote sensing as a way of drastically improving spatial sampling since it delivers cloud properties over more than a line-of-flight through 3+1D space. The obvious tradeoff is, however, generally complicated connections between remotely-measured radiances and inherent cloud properties of real interest to cloud process modelers. Active remote sensing from below or above the clouds improves vastly over in-situ sampling, but its outcome remains confined to a “ribbon” of vertical profiles ordered in time (from below) or space (from above). Passive imaging has the complimentary problem of delivering a potentially wide horizontal swath of cloud properties, but integrated along the vertical. At least that is the conventional wisdom when it comes to the solar spectrum, where observed radiances from clouds are dominated by multiple scattering. Based on recent results from AirMSPI imaging at 20 km altitude, we challenge the perceived limitation of passive shortwave radiometry to deliver only column-integrated properties. We demonstrate that multi-pixel exploitation of multi-angle spectro-polarimteric imaging at solar wavelengths can be used to extract not only maps of microphysical properties but also 3D cloud structure for both PBL-topped stratiform layers and vertically-developed 3D clouds in convective regimes. A key realization is: airborne and space-based sensors offer radically different spatial and angular sampling opportunities with unique advantages in both cases. We look forward to future PBL-specific missions in space for their global reach. At the same time, there is a clear case for deploying high-altitude imagers in all future campaigns.

Cloud tomography (CT) is a promising approach in passive remote sensing using space-based imaging sensors like MISR and MODIS. In contrast with current cloud property retrievals in the VNIR-SWIR, which are grounded in 1D radiative transfer (RT), CT embraces the 3D nature of convective clouds. Forster et al. (2020) defined the “veiled core” (VC) of such clouds as the optically deep region where detailed 3D structure of the cloud has little impact on the multi-angle/multi-spectral images as long as average VC extinction and any significant cloud-scale gradient are preserved. Quantitatively, the difference between radiance fields escaping clouds remains commensurate with sensor noise when said clouds differ only in the small-scale distribution of extinction inside their VC. An important corollary for the large and ill-posed CT inverse problem is that the only unknowns of interest for the whole VC are its mean and any cloud-scale vertical trend in the extinction coefficient. Another ramification for CT algorithms under development is that the forward 3D RT model driving the inversion may be vastly simplified in the VC to gain efficiency. We explore that possibility here, assuming radiative diffusion as the simplified RT for the VC. We also describe the relevant RT physics that unfold in the VC and in the outer shell (OS) where detailed spatial structure does matter for image formation. This includes control by the VC of the cloud-scale contrast between brightnesses of illuminated and shaded boundaries, as well as the gradual blurring of spatial structure via directional diffusion with increasing optical distance into the OS. “Transport” space is the merger of 3D (or 1D) physical space and 2D direction space. Cloud image formation involves radiative diffusion processes (i.e., random walks) in both of these spaces, depending on what transport regime prevails. Fortunately for the future of computed CT and of passive cloud remote sensing in general, there is a clear spatial separation: asymptotic limit of radiative diffusion in the VC, standard RT in the OS. A hybrid forward model for CT will make use of this fact. Reference: Forster, L., Davis, A. B., Diner, D. J., & Mayer, B. (2021). Toward Cloud Tomography from Space Using MISR and MODIS: Locating the “Veiled Core” in Opaque Convective Clouds, Journal of the Atmospheric Sciences, 78(1), 155-166.