Initiative

Drizzle Representation via Enclosed Atmospheric Measurements and Modeling

Aerosol-cloud interactions remain one of the largest uncertainties in climate predictions. DREAM2 aims to unravel these complexities to enhance climate and weather models.

DREAM 2 Logo

The DREAM2 Chamber is an innovative research initiative designed to advance our understanding of clouds, aerosols, and precipitation processes, with the goal of improving global climate predictions. 

Cloud chambers provide a controlled environment for studying cloud processes and are crucial for addressing fundamental questions in atmospheric research. The development of a large-scale cloud chamber in North America is a high priority for the atmospheric research community, as it would advance our understanding of climate-critical processes, such as rain formation, cloud processing of aerosol, and the impact of ice-nucleating particles on cloud physics.

Scientific Research Focus Areas

  • Research Objective 1: Direct measurement of collision-coalescence.

    Demonstrating precise measurement of collision-coalescence using innovative instruments like miniSPLAT and PCVI to improve accuracy in cloud droplet analysis.

  • Research Objective 2: Investigate SOA cloud processing

    Examining secondary organic aerosol (SOA) processes in cloud environments to better understand their chemical impacts and effects on cloud condensation nuclei.

  • Research Objective 3: Characterize cloud droplet size distributions

    Developing and testing the HOLODROPS instrument to measure cloud droplet size changes with high precision using advanced holographic techniques.

  • Research Objective 4: Develop a digital twin of the DOE chamber

    Creating a digital twin using the PINACLES model to simulate cloud chamber conditions, enhance controls, and integrate machine-learning-ready microphysics packages.

  • Research Objective 5: Connecting cloud microphysics across scales

    Mapping insights from cloud chamber experiments to larger-scale atmospheric models, focusing on processes like collision-coalescence using machine learning and LES simulations.

  • Research Objective 6: Quantum computing strategies for direct numerical simulations

    Exploring quantum computing for direct numerical simulations of cloud droplets, enabling revolutionary breakthroughs in modeling droplet dynamics and cloud systems.