Probabilistic observational constraint of a microphysics scheme with flexible structural complexity
Authors
Marcus van Lier-Walqui — Columbia University
Matthew Kumjian — Pennsylvania State University
Hugh Clifton Morrison — University Corporation for Atmospheric Research
Olivier P. Prat — North Carolina Institute for Climate Studies
Category
Microphysics (cloud, aerosol and/or precipitation)
Description
We present work investigating the observational constraint of a novel probabilistic bulk microphysics scheme, the Bayesian Observationally-constrained Statistical-physical Scheme (BOSS). Current bulk microphysics schemes employ numerous assumptions and ad hoc parameter and structural choices with uncertainties that are difficult or impossible to directly estimate owing to their being "hard-coded" into into their respective schemes. BOSS treats free parameters and scheme structure alike as flexible, and subject to constraint by observations or prior knowledge. No assumptions are made regarding the structural form of the drop size distribution, or the mathematical form of process rates, which are instead generalized as a sum of power laws. This flexibility allows for the complexity of BOSS to be increased or decreased to match the complexity of processes observed in nature. For example, BOSS can smoothly vary the number and choice of prognostic DSD moments (single-, double-, triple-moment, etc), and can also vary the number of power law terms used to model microphysical process rates. Here we investigate constraint of BOSS via synthetic observations in an idealized framework. Special attention is given to comparing versions of BOSS with varying levels of complexity, and to what extent estimates of uncertainty in BOSS capture errors associated with inadequate microphysical models.
