Research Projects

Cloud-aerosol interactions remain one of the largest uncertainties in climate predictions due to the complex role of aerosols as cloud condensation nuclei (CCN), influencing warm rain formation and the Earth's radiative balance. To understand their effects on cloud initiation, it is essential to characterize aerosol properties, namely chemical (e.g., nitrate, minerals), physical (size, shape, number density), and optical properties (transparency, absorption). Given the vast diversity of aerosol types, a more practical and meaningful approach is to classify them broadly as biogenic or anthropogenic based on origin. Biogenic aerosols, from natural sources like forests, tend to be larger and more hygroscopic, affecting drizzle formation depending on their state. Anthropogenic aerosols produced from combustion and industrial activity are typically smaller, more numerous, and often suppress coalescence due to high CCN concentrations. Aerosols are highly dynamic entities, undergoing continuous changes due to atmospheric parameters, such as temperature, humidity, and wind. These transformations via heterogeneous reactions, molecular exchange, and particle collisions alter their morphology and hygroscopicity, affecting their cloud-forming potential. However, most current measurement systems cannot capture these dynamics in real-time. Offline techniques (e.g., cascade impactors) are bulky and slow. Although advanced real-time techniques exist (e.g., optical particle counters, single particle mass spectrometers) no integrated system currently exists to capture size, shape, optical properties, and concentration simultaneously. Satellite missions like CALIPSO offer vertical profiles but lack resolution and sensitivity for near-surface and fast-evolving aerosol events. To address this gap, we propose CloudHoloNet, an in-situ, real-time aerosol sensing network based on Digital Inline Holography (DIH), a compact, low-cost optical technique capable of capturing 3D particle size, shape, and optical properties. A validated DIH sensor will be upgraded to detect submicron aerosols and deployed on tall buildings in strategically chosen locations: biogenic-dominated (e.g., forest edges), anthropogenic-dominated (e.g., urban zones), and regions with strong updrafts conducive to cloud formation. The DIH units will be integrated with meteorological sensors and real-time machine-learning pipelines. Aerosol data will be time-synchronized using HYSPLIT-based air parcel trajectory modelling, tracing movement from ground to cloud base. Concurrent satellite data (INSAT-3D/3DR, MODIS) will provide cloud properties, specifically droplet size distribution, reflectivity, and precipitation onset. Select events will be validated against CALIPSO profiles during extreme weather or pollution episodes. By correlating real-time DIH aerosol measurements with satellite-derived cloud characteristics, we aim to quantify the influence of aerosol type on cloud initiation and evolution.