Improving the representation of aggregation in a two-moment microphysical scheme with statistics of multi-frequency Doppler radar observations

Markus Karrer,Stefan Kneifel,Davide Ori,Axel Seifert

doi:10.5194/acp-21-17133-2021

Abstract

Abstract. Aggregation is a key microphysical process for the formation of precipitable ice particles. Its theoretical description involves many parameters and dependencies among different variables that are either insufficiently understood or difficult to accurately represent in bulk microphysics schemes. Previous studies have demonstrated the valuable information content of multi-frequency Doppler radar observations to characterize aggregation with respect to environmental parameters such as temperature. Comparisons with model simulations can reveal discrepancies, but the main challenge is to identify the most critical parameters in the aggregation parameterization, which can then be improved by using the observations as constraints. In this study, we systematically investigate the sensitivity of physical variables, such as number and mass density, as well as the forward-simulated multi-frequency and Doppler radar observables, to different parameters in a two-moment microphysics scheme. Our approach includes modifying key aggregation parameters such as the sticking efficiency or the shape of the size distribution. We also revise and test the impact of changing functional relationships (e.g., the terminal velocity–size relation) and underlying assumptions (e.g., the definition of the aggregation kernel). We test the sensitivity of the various components first in a single-column “snowshaft” model, which allows fast and efficient identification of the parameter combination optimally matching the observations. We find that particle properties, definition of the aggregation kernel, and size distribution width prove to be most important, while the sticking efficiency and the cloud ice habit have less influence. The setting which optimally matches the observations is then implemented in a 3D model using the identical scheme setup. Rerunning the 3D model with the new scheme setup for a multi-week period revealed that the large overestimation of aggregate size and terminal velocity in the model could be substantially reduced. The method presented is expected to be applicable to constrain other ice microphysical processes or to evaluate and improve other schemes.

Highlights

Ice growth processes which lead to precipitable particles are essential to understand because more than 60 % of the global precipitation reaching the surface is formed in the ice phase (Heymsfield et al, 2020)
Since we find little influence of self-similar Rayleigh–Gans approximation (SSRGA) parameters in Sect. 3.1.5, we use the adjusted SSRGA properties of the aggregates of needles from O20 for the Mix2 aggregates throughout the study to be consistent with 020, using the SSRGA parameters derived from the same 3D aggregate models would be most physically consistent
To interpret the following sensitivity experiments, we describe which parameters need to be considered in the simulation of aggregation in a bulk scheme, which parameters and process formulations are currently used in the SB06 scheme, and how the assumptions could be updated with recently published parameterizations

Summary

Introduction

Ice growth processes which lead to precipitable particles are essential to understand because more than 60 % of the global precipitation reaching the surface is formed in the ice phase (Heymsfield et al, 2020). Besides depositional growth and riming, aggregation is one of the key growth mechanisms in ice clouds. For example, by radar observations (e.g., Barrett et al, 2019), aggregation can cause a rapid increase in the particle size in favorable conditions, such as the dendritic growth zone close to −15 ◦C or close to the melting layer (Lamb and Verlinde, 2011). Sublimation, or riming, aggregation does not directly modify the ice and snow water content. Its strong influence on particle shape, particle size distribution, and terminal velocity vt links aggregation to other processes, such as depositional growth, sublimation, and riming, that alter the mass flux considerably.

Methods

Results

Conclusion