Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

• Published in: Journal of advances in modeling earth systems 2022-07, Vol.14 (7), p.n/a
• Main Authors: Vukicevic, T., Posselt, D. J., Stankovic, A.
• Format: Article
• Language: English
• Subjects: Cloud microphysics; Convection; data assimilation; ensemble prediction; Microphysics; Parameters; Precipitation; stochastic parameterizations
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1029/2021MS002933

This study examines the characteristics of several model parameter perturbation methodologies for ensemble simulations of cloud microphysical processes in convection. A simplified 1D model is used to focus the results on cloud microphysics without the complication of feedbacks to the dynamics and environment. Several parameter perturbation methods are tested, including non‐stochastic and stochastic with various distributions and parameter covariance. We find that an ensemble comprised of different time‐invariant parameters (non‐stochastic) exhibits little bias, but small spread. In addition, its behavior does not respect the time evolution of convection through its various phases. Stochastic parameter (SP) methods in which no inter‐parameter covariance is applied produce greater spread, but significant bias. The bias is particularly large for lognormal parameter perturbation distributions. The ensemble spread is retained and the bias reduced when time‐varying parameter covariance is applied. In this case, the SP scheme is able to adapt to the time and state‐dependent covariance structures and produce ensemble characteristics that are consistent with the specific microphysical processes operating at any given time. The results suggest that SP schemes would benefit from inclusion of parameter covariances, and specifically those that vary with the state of the system. It also suggests that a Normal or LogNormal SP scheme with no covariance may significantly impact the ensemble bias. Finally, the results indicate that high temporal and spatial resolution observations may be needed to characterize the variability in parameter values and covariance. Plain Language Summary All numerical weather and climate prediction models contain approximate representations (parameterizations) of high resolution cloud processes. Model error results from either inaccurate parameter settings, or from the fact that parameters are static while they should vary in time and space. Previous studies have shown that random variation of model parameters can help to improve predictability of weather and climate. This study examines several methods by which model parameters can be varied in time, and finds that the best results are obtained when parameter variations are consistent with the time evolution of the weather system that is being simulated. Key Points Sensitivity of microphysics model state to stochastic parameter perturbations is highly time dependent Parameter perturbations inf