The doctoral dissertation on the subject of cloud turbulence microphysics at interfaces: a DNS model with phase change and droplet interaction was finalized by Mina Golshan on 22/02/2023 in Politecnico di Milano (Dipartiment di Science e Technologie Aerospaziali (DAER)).
In the present work, a few problems in the area of small-scale cloud dynamics, using direct numerical simulations to study the temporal evolution of a perturbation located in the turbulent layer that generally separates a cloud from the surrounding clear air, were addressed by details. The second part of the research is related to in-field experimentation using the mini green radiosondes developed in H2020 COMPLETE Marie Curie Network (http:// www.complete-h2020network.eu). In the first part, initial value problems were considered, where the temporal evolution of an initial distribution of turbulent kinetic energy, temperature, humidity, and droplet distributions was observed. A sufficiently intense stratification was observed to change the mixing dynamics. The formation of a sub-layer inside the shear-less layer was observed. The sub-layer, under a stable thermal stratification condition, behaved like a pit of kinetic energy. However, it was observed that turbulent kinetic energy transient growth took place under unstable conditions, which led to the formation of an energy peak just below the center of the shearless layer. A monodisperse droplet population with a radius of 15 µm and a polydisperse distribution with radii within the 0.6 – 40 µm range. Polydispersity has shown a different behavior in droplet evaporation and condensation both within the homogeneous cloudy region and in the anisotropic interface mixing zone for both distributions. However, the two populations show a common aspect in the course of the transient, that is, an increased probability of collisions within the interface layer, which exhibits a marked anisotropic velocity fluctuation. These DNSs show that supersaturation fluctuations broaden the droplet size distribution and induce an increase in the collision rate. This result is in contrast to the classical growth of (non-turbulent) condensation, which leads to an increase in the average droplet size but also to a narrower droplet distribution. It was also found that although the turbulent kinetic energy of the airflow hosting the cloud decreased by 90 % over the course of the simulation, the collision activity decreased by 40% inside the cloud but increased by 25% in the mixing area of the interaction. The size distribution of the number density of the droplets, for the initially monodisperse population in the mixing layer, showed a standard deviation growth 15 times faster than that in the cloud region. In the polydisperse case, the concentration distributions were oppositely skewed, and the width of the distributions decreased more rapidly (about four times) over time within the interface region than in the cloud. Moreover, for the monodisperse population, a clustering of the values of the reaction, phase, and evaporation times, that is around 20-30 seconds, is observed in the central area of the mixing layer, just before the location where the maximum value of the turbulent supersaturation flux occurs. This clustering of values is similar for the polydisperse population but also includes the condensation time. The mismatch between the time derivative of the supersaturation and the condensation term in the interfacial mixing layer is correlated with the planar covariance of the horizontal longitudinal velocity derivatives of the carrier airflow and the supersaturation field, thus suggesting that a quasi-linear relationship may exist between these quantities. In the second part, which contains in-field experimentation with radioprobes, is a preliminary result of ongoing work. It is mainly a proof-of-concept, and in the future, more variables could be measured by equipping the probes with suitable sensors. This part of the research is aimed at studying the water droplet dispersion due to turbulence in warm clouds. The analysis is afforded by means of both experimental and numerical approaches (numerical approaches based on the Horizon 2020 Marie Sklodowska Curie project). The radiosonde data post-processing is based on distance-neighbor graphs, which was the original statistical analysis proposed by L.F. Richardson in 1926. While the first part of the work deals with DNSs and is limited to small-scale dynamics, the second part, which is experiments with radio probes, was aimed to analyse a few aspects of large-scale dispersion, which cannot be dealt with DNSs, and to measure in a Lagrangian way the state changes that a parcel of moist air undergoes in the atmosphere, thus providing a reference for numerical simulations. Therefore, the two parts reflect the same point of view on the same research work, i.e., the interaction between droplets and turbulence is seen from a different iv timescale.