Toward a Virtual Cloud Chamber

Microphysics, via convective and radiative processes, contributes to one of the most significant uncertainties in predicting the weather or future climate. However, It is impossible to obtain comprehensive, direct observations at the minute scales of the microphysical processes. Indeed, direct observations of hydrometeors are mostly one-dimensional (1D) in the four-dimensional (4D) space-time (i.e., with measure approaching 0), restricted to (near) the surface or a few transects/spirals from airborne campaigns. These are invaluable, but still pitifully inadequate, for improving parameterizations in dynamic models. Indirect, remote sensing techniques provide the only feasible alternative to the comprehensive observation of microphysics. On the other hand, the inverse problem associated with remote sensing is generally underconstrained, i.e., different conditions may produce the same observations. Due to these observational limitations, our understanding of the vital microphysics for hydrometeor profile evolution remains blurry. We are thus constructing a virtual cloud chamber (VCC) based on the recently published SnowMeLT model (Pelissier et al 2023, https://doi.org/10.1175/JAS-D-22-0150.1), which simulates the realistic melting of solid hydrometeors using the technique of smooth particle hydrodynamics. As much as possible, SnowMeLT implements relevant physics of hydrometeor melting from the first principles, e.g., thermodynamics and water-ice interfacial dynamics. It serves as a great starting point for the VCC. We are extending it to include additional necessary physics to realize the VCC, such as aerodynamics. We will further expand it to simulate multiple hydrometeors of multiple species and the interactions among them, such as collision and collection, eventually constituting a realistic dynamic ensemble. We plan to verify the implemented physics and validate VCC simulation results with physical cloud chamber observations, in-situ microphysical observations, and coincident remote sensing observations, thereby sharpening our understanding and improving the simulation fidelity of the VCC. We believe the VCC can lead to better microphysics parameterization and reduced uncertainties in dynamic model predictions. We will also discuss the resource implications of constructing such a VCC.