Climate CT- Cloud Tomography by Satellites for Better Climate Prediction

ongoing

Clouds play a lead climatic role, controlling energy fluxes and regulating fresh water distribution. There is an acute need for cloud-resolving and global-climate models that accurately describe and parametrize the physics of warm convective and stratiform clouds, and the clouds’ sensitivity to environmental changes. Currently this requirement is not being met due to a gap in observational capabilities. Namely, there is a lack of sufficient sensing tailored to capture the 3D macro and microphysical properties of warm clouds, which are often spatially unresolved. Moreover, current retrievals use a plane-parallel radiative model, which is incompatible with the 3D heterogeneous nature of clouds. These gaps lead to uncertainties in climate models and prediction. We propose an innovative sensing approach: cloud scattering-tomography, relying on an unprecedented large formation of ten cooperating, high performance pico-satellites. They will simultaneously image cloud fields from multiple directions, at 50m resolution. Based on this data, the novel tomography approach will seek the 3D volumetric structure of cloud fields, base-to-top profiles of droplets' size and their variance, volumetric distribution of optical extinction and rain indicators. The required pointing accuracy, data size and coordinated control of a complex 10 pico-satellite formation demands advanced space engineering, beyond existing technologies of traditional single satellites and constellations of satellites. Realizing a large formation requires innovative, distributed, networked, cooperative control, including advanced sensors and actuators for pico-satellites, as well as in-orbit autonomy. On-board hardware and flexible software will be adapted to meet computational needs within the physical limitations of pico-satellites (energy, mass, volume). Using the acquired spaceborne images for tomography-based 3D atmospheric retrievals requires advancements in computer vision and efficient analysis based on three-dimensional radiative transfer. New information gained will improve and validate our cloud resolving models, leading to more realistic simulations of cloud fields. This will enable better understanding of how environmental changes affect warm clouds and help improve their representation in climate models. This multidisciplinary, synergic approach will establish and test critical and currently unconventional aspects of remote sensing and mathematical retrieval based on a pico-satellite formation. It will yield a database of 3D macro and micro structure of warm cloud fields, while setting the stage for next-generation distributed spaceborne global observations.