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

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.

Ice microphysical processes in the dendritic growth layer: a statistical analysis combining multi-frequency and polarimetric Doppler cloud radar observations

“…Statistically-based observational process signatures are very useful for evaluating and improving microphysical schemes in weather prediction models (e.g. Karrer et al, 2021;Ori et al, 2020). They are also urgently needed as constraint for recent model developments such as habit-dependent growth (Jensen et al, 2017;Sulia and Kumjian, 2017;Harrington et al, 2013;Hashino and Tripoli, 2007) and Lagrangian Monte Carlo models where the particle history can be traced L. von Terzi et al: Ice microphysical processes in the dendritic growth layer (Grabowski et al, 2019;Brdar and Seifert, 2018).…”

Ice Aggregation in Low‐Level Mixed‐Phase Clouds at a High Arctic Site: Enhanced by Dendritic Growth and Absent Close to the Melting Level

“…It will further enable us to obtain DWR profiles reaching cloud top, as well as overcome the sensitivity limitations of the MRR. In this regard, dual-frequency cloud radar observations provide the unique opportunity to test and improve the representation of ice-growth processes in numerical models (Karrer et al, 2021;Ori et al, 2020), and this possibility will be in the future explored with the ICOsahedral Non-hydrostatic (ICON) modeling framework (Zängl et al, 2015), in its Large Eddy Model (LEM) version. Only data observed during calibration events (see Section 3.2.1) are included.…”

A Performance Baseline for the Representation of Clouds and Humidity in Cloud‐Resolving ICON‐LEM Simulations in the Arctic

“…This could also be seen in the highly resolved ICON-LEM simulations performed over Germany by and Heinze et al (2017). Deficiencies in the micro-physics schemes implemented in ICON have been pinpointed in other studies as well (Karrer et al, 2021;Kretzschmar et al, 2020;Ori et al, 2020). As interest in the Arctic environment has been growing, ICON has been used for the higher latitudes in different setups for case studies and campaigns (Bresson et al, 2022;Gruber et al, 2019;Wendisch et al, 2019).…”