• Microhydrodynamics

• Cloud microphysics

• Stochastic modeling 

• Turbulence

Droplet collision dynamics plays a central role in warm rain formation, where precipitation initiates through the growth of cloud droplets via collision and coalescence. Since condensational growth alone is insufficient to explain the rapid onset of rainfall, accurate modeling of collision dynamics and droplet interactions is essential. In most atmospheric models, these processes are represented through parameterizations, which often rely on simplified assumptions and empirical formulations. However, collision rates are strongly influenced by hydrodynamic interactions, turbulence, interparticle non-hydrodynamic forces, and environmental conditions, leading to significant uncertainties in predicting the evolution of the droplet size distribution (DSD).

Cloud–climate interactions inherently involve multiscale, multiphysics, and multiphase processes. This complexity necessitates a detailed understanding of cloud microphysics, where fundamental droplet-level processes are studied independently to develop physically consistent parameterizations for larger-scale models. Improving the collision parameterizations directly impacts the accuracy of cloud microphysics schemes used in numerical weather prediction and climate models, particularly in predicting precipitation rates, timing of rainfall onset, and aerosol–cloud–precipitation interactions. A deeper understanding of droplet collision dynamics, therefore, helps reduce uncertainties in weather forecasts and enhance the reliability of climate projections, especially in the context of cloud feedbacks and hydrological cycle representation.

Motivated by these challenges, I have systematically investigated the role of key near-field physical mechanisms, including non-continuum lubrication interactions and electrostatic interactions, on the collision dynamics of droplets subjected to background laminar flows and gravitational settling. These studies aim to provide improved physical insights into droplet interactions that can inform the development of more accurate microphysical parameterizations.

At small separation distances, the hydrodynamic interaction between approaching droplets is governed by lubrication forces, which significantly slow down their relative motion. For droplets interacting in a gaseous medium such as air, the continuum lubrication approximation would no longer be valid when the gap thickness between two surfaces is less than the mean free path of the medium. In such cases, non-continuum lubrication interactions would become the dominant mechanism for collisions. Consequently, non-continuum lubrication interactions can alter the continuum relative trajectories of droplets and lead to collisions even in the absence of any non-hydrodynamic attractive force. Movies below show typical relative trajectories of two spheres subjected to a simple shear flow and interacting via continuum and non-continuum hydrodynamics. (Link for the paper: J. Fluid Mech., 950, A18)

In addition to surface electric charges, cloud droplets are also affected by electric fields within clouds. I studied how strong electric fields that develop during cloud electrification can enhance the likelihood of droplet collisions. My findings suggest that electric-field-induced forces can significantly increase the collision efficiency of droplets in a laminar background flow or under gravity (see Figures 2 and 3). (Link for the papers: Phys. Rev. Fluids, 10, 103601 , J. Atmos. Sci., 82, 2357–2373)

Figure 2: (a) Two uncharged conducting droplets subjected to a uniaxial compressional flow and an external electric field. (b) Collision efficiency as a function of strength of the external electric field.

Figure 3: (a) Two uncharged conducting droplets subjected to gravity and an external electric field. (b) Collision efficiency as a function of strength of the external electric field.

Understanding the interactions between ice sheets and global climate forcings over geological timescales is essential for projecting their future. Previous studies have highlighted the role of ice dynamics and climate interactions in establishing the 100,000-year glacial cycles, particularly regarding the growth of the North American ice sheet. In recent years, researchers have reconstructed consistent time series of ice volume, temperature, and carbon dioxide by applying inverse forward modeling to benthic oxygen isotope records. Here, we model the stochastic behavior of paleoclimate time series to evaluate the coupling among climate variables during Late Pleistocene glacial cycles. We quantify the behavior of these time series using multifractal time-weighted detrended fluctuation analysis, which differentiates between near-red-noise and white-noise behavior below and above the 100,000-year glacial cycle, respectively, in all records. Further, we develop a stochastic model to capture coupling dynamics among carbon dioxide, methane, nitrous oxide, temperature, and global ice volume. To test our model, we compute response functions for each pair of variables and compare them with empirical data, confirming our predictions regarding inter-variable causal relations. (Link for the paper under review: https://arxiv.org/abs/2603.26937)