In the initial two periods of this project, we improved our Large Eddy Simulation (LES) models to better simulate warm clouds with increased accuracy. A significant aspect of this involved implementing a new microphysical scheme that accounts for the regeneration of aerosols after the evaporation of drops, detailing the aerosols' return to the atmosphere post-cloud interaction. This addition significantly enhances the accuracy of aerosol budget treatment, both in the environment and within clouds, leading to a more realistic description of cloud microphysics. In parallel, we conducted super high-resolution simulations of individual clouds to examine the mixing processes between warm clouds and their surroundings. We simulated various warm cloud fields as well. In parallel, we continued exploring continental convective clouds (defined as green Cumulus), a primary cloud type within this project. We constructed a new global long-term database for these clouds, utilizing it to define the meteorological conditions that support the formation of such cloud fields.
In the field of theoretical radiation transfer, our work revealed that true color glories, an optical phenomenon resulting from sunlight interacting with water droplets in clouds, are observable in raw, unpolarized satellite images on a daily basis. This observation provides a substantial and untapped cloud dataset, establishing a straightforward link between cloud droplet size and the structure of the glory through a diffraction-like approximation.
On a global scale, we investigated climate trends in temperatures and the corresponding response of clouds. Using an innovative method, we demonstrated the warming of sea surface temperatures in most oceans, with notable cooling in the North Atlantic and Southern Ocean. The response of clouds to global warming was evident in decreased cloud coverage over most continents and an increasing trend over tropical and subtropical oceans.
Our efforts also extended to the interface of cloud physics, nonlinear dynamics, and data science. In a paper introducing a new route to complexity via phase-dependent stochastic parameterizations, we presented a general framework to enhance the realism of solutions for nonharmonic oscillations by breaking down their nonlinearity. By superimposing stochastic parameterizations on these structures, we achieved stochastic chaotic solutions, uncovering a new pathway to complexity with realistic cloud oscillations that exhibit enhanced time-variability. These stochastic parameterizations aim to replace missing physics in simpler models, particularly those related to rain effects.
We greatly advanced methods for computed tomography (CT) in non-trivial setings, and what we learned in clouds is helping us affect other domains. For clouds, we derived efficient scattering-based CT. This has been demonstrated by several studies corresponding to different principles: machine learning, stoachstic differential rendering, recycling monte-carlo paths for efficient use of graphical processing units, multi-scale (coarse to fine) recovery, and use of monotnonicity priors. Moreover, we generalized scattering-based CT to polarimetric imaging, thereby retrieving microphysics of cloud droplets.
The algorithmic methods helped us affect additional domains of application. One of then includes X-ray CT exploiting scattering: our methods enable this to be achieved. Moreover, we showed that this approach can speed up X-ray CT acquisition, by source multiplexing. Another domain is of atmospheric turbulence fields: we showed, for the first time, that scintillation of light sources in the open air can be a data source for CT of of the turbulence strength, when using an array of cameras spread outdoors in a county-scale. Moreover, we devised an efficient self-calibration method using CT projections, and showed that it can significantly improve in-situ CT of populations of plankton.
The issues of calibration and self-calibration became increasingly significant as our project advanced towards a real-world system. Therefore, we drived a method for calibrating atmospheric aerosol lidars, and invented methods for polarimetric calibration of spaceborne cameras - in orbit. One approach uses solar-farms as a calibration target. Another method uses zodiacal light (scattered by inter-planetary dust) as the target. In addition, we created a calibration setup – including polarization – for the spaceborne payloads.
Work on the payloads went from scratch to working systems through the project. Initially, we were unsure what is required of the payload. We worked intensively to define this. As a result, we defined a detailed tender. A company (Dragonfly) won the tender and is building for CloudCT the payloads. We already received two of the payloads and they undergo testing. This is part of our empirical lab-based research in the project. Additional lab work involved tomography in controlled settings. One branch of this work involved building a special chamber that created (for a few seconds) indoor clouds not bounded by walls. Another branch looks into 3D recovery of a scattering medium in a microscopic setting.
