The composition of the atmosphere is significant to our ecosystem. Accordingly, there is a need to sense distributions of atmospheric scatterers such as aerosols and cloud droplets. There is growing interest in recovering these scattering fields in three-dimensions (3D). Even so, current atmospheric observations usually use expensive and unscalable equipment. Moreover, current analysis retrieves partial information (e.g., cloud-base altitudes, water droplet size at cloud tops) based on simplified 1D models. To advance observations and retrievals, we develop a new computational imaging approach for sensing and analyzing the atmosphere, volumetrically. Our approach comprises a ground-based network of cameras. We deployed it in conjunction with additional remote sensing equipment, including a Raman lidar and a sunphotometer, which provide initialization for algorithms and ground truth. The camera network is scalable, low cost, and enables 3D observations in high spatial and temporal resolution. We describe how the system is calibrated to provide absolute radiometric readouts of the light field. Consequently, we describe how to recover the volumetric field of scatterers, using tomography. The tomography process is adapted relative to prior art, to run on large-scale domains and being in-situ within scatterer fields. We empirically demonstrate the feasibility of tomography of clouds, using ground-based data.