Evaluating and Improving Convection-Permitting Simulations of the Life Cycle of Convective Storms using Polarimetric Radar Data of convective storms (Life Cycle of Convective Storms)

Project based at the
Karlsruhe Institute of Technology

Karlsruhe Institute of Technology: Andrew Barrett (independent researcher)

What causes heavy precipitation or large hail to form inside some thunderstorms? That is the principle question leading this research. A secondary question, “Why do some models predict these hazards well and others rather poorly?” is also considered.

To better answer these questions, a combination of state-of-the-art numerical modelling and dual-polarimetric radar observations are used in combination. Additionally, a new technique called “piggybacking” is being added to the numerical model to provide additional insight into the differences between cloud physics parameterizations.

Model simulations of thunderstorms often show very large differences from reality (e.g. Varble et al., 2014) and also from other simulations performed with different models (e.g. White et al., 2017). The first part of this project will determine which parts of the model formulation contribute to the large differences between model simulations. To do this, the new “piggybacking” technique is introduced. Piggybacking allows multiple cloud microphysics parameterizations to be compared in a single model simulation and using the exact same air motions. Usually feedbacks via latent heating create very different air motions depending on the choice of cloud microphysics parameterization, which makes it almost impossible to meaningfully compare the parameterizations.
The new knowledge about which parts of the model formulation contribute to the between-model differences is then applied in the second part of the project. Here we will use this information to help make the model simulations more similar to reality. Radar observations - both traditional (e.g. reflectivity) and dual-polarimetric (e.g. differential reflectivity, differential phase shift) – will be compared to model output of the same quantities, which are derived from a forward model. The forward modelled outputs will be statistically compared to the radar observations for several days worth of simulations. With knowledge from the first part of the project, any biases detected in the simulations can be reduced through changes to the cloud microphysics.
Additionally, the most realistic simulations will be used to help explain the 3D distribution of the radar parameters – enabling us to determine the location of different hydrometeor types within the thunderstorm and the processes acting on the hydrometeors.
The end goal is to identify exactly which processes contribute to the formation of heavy precipitation and large hail. Additionally we will learn which parts of the model contribute most to the uncertainty when simulating thunderstorms. Therefore we can determine which processes need to be better described in the model and therefore which processes should be further studied so that the relevant parameterizations can be improved.

Current status
The start of the project contained significant technical work with the ICON model. The piggybacking technique and a second microphysics scheme were both added. Testing these new developments in various setups has been performed. In an idealised setup, where convection is initiated by a warm bubble – the sensitivities of total rain amounts and total hail amounts reaching the surface are shown to differ in origin (see below). Work is now progressing on simulating real cases with ICON; the piggybacking technique is being used to understand the how two different microphysical parameterizations simulate convection on these days and what the main reasons for their differences are.

Further Details
Microphysical Piggybacking
The piggybacking technique (Grabowski, 2014, 2015; Grabowski and Morrison, 2016) is built around the idea of having two microphysics schemes running simultaneously during one model simulation. Only one of these microphysics schemes interacts with the atmospheric circulation through latent heating feedbacks – the other one just reacts to the air movements (i.e. it piggybacks). Therefore the technical infrastructure for including a second microphysics scheme into ICON has been built. The new developments allow for either ICON’s standard 2-moment scheme (Seifert & Beheng, 2006) or the Predicting Particle Properties scheme (P3; Morrison and Milbrandt, 2015) to be the second microphysics scheme. In addition, all the microphysical process rates for each of the processes are calculated and output for each of the microphysics schemes.

Sensitivity of surface precipitation and hail to modified cloud physics
Using the newly introduced technique of microphysical piggybacking, we examined the sensitivity of the surface total precipitation and surface hail accumulation to the cloud microphysics. We focused on changing the rate of autoconversion in the model – this is the rate at which small cloud droplets grow to be large enough to fall from the cloud as rain drops. An increased autoconversion rate means that rain forms more quickly inside the cloud and falls towards the ground, similarly the amount of water reaching higher levels in the cloud (which would be important for hail formation) is reduced. The autoconversion rate is a significant uncertainty in model descriptions of cloud formation. It varies with the aerosol concentration of the atmosphere and is parameterized differently across numerous models; White et al. (2017) found that is was a significant cause of inter-model differences in simulated convective clouds.

Changing the autoconversion rate and therefore forming rain more quickly also changes the distribution of heating and cooling within and underneath the simulated cloud. For example, the rain falling out of the cloud starts to evaporate below cloud base before reaching the ground; this cools the surrounding air producing so called “cold pools”. When rain is formed more quickly, the cooling through evaporation starts earlier and cools the air faster changing the shape and size of the cold pool. The contrasting density of the air within the cold pool and outside changes the three-dimensional flow – resulting in changes to the updraft strength or size. These changes in updraft lead to cloud formation in different places or times – which also change the temperature distribution. There is a continuous, unbreakable circle of interactions between cloud formation and changes in atmospheric circulation through heating changes. With piggybacking we are able to break the circle and attribute changes in the simulated clouds to either a) effects coming directly from the cloud microphysics, or b) effects coming indirectly from changes in the cloud microphysics via changes heating and cooling and therefore air motion changes.

The piggybacking methodology allowed us to determine whether the changes to surface precipitation and surface hail fall are more sensitive to the direct impact of changing the microphysics parameterization, or indirect changes via changing to the latent heating/cooling which affects the air motions. For surface precipitation, the direct impact of changing the autoconversion rate is largest whereas the feedbacks via the dynamics are much less important. For surface hail, both the direct and indirect effects are important.
Figure 2 shows the total precipitation and total hail fall from 5 sets of simulations. All simulations within one set have identical air motion. Within each set 5 simulations are run with different autoconversion rates. However, only one of the simulations – that marked with the black outline – is coupled allowed to modify the air motions through heating and cooling, the other simulations are piggybacked and only react to the changing air motions.

For the total precipitation (Figure 2, upper panel) the 5 sets of simulations are very similar. Within each set there is a systematic decrease of total precipitation with increasing autoconversion rate. This is a sign that the total precipitation is sensitive to the direct change of autoconversion rate, but almost insensitive to the change of air motions associated with changed latent heating and cooling intensities or locations. In contrast, the total hail fall (figure 2, lower panel) is sensitive to both the direct change of autoconversion rate (as shown by a systematic increase of hail with increasing autoconversion rate within each set) and also to the changed air motions associated with latent heating and cooling changes (as shown by the large set-to-set variability). Four out of the five simulations in Set 2 produce more hail than in any other setup – this is a sign that the air motions in this set of simulations particularly favors hail formation.
These first results indicate the potential of the piggybacking technique to help separate the direct and indirect impacts of changing specific aspects of the cloud microphysics parameterizations. This technique will now be used to systematically test different aspects of the model configuration and determine the sensitivity of the precipitation produced to changes in the cloud microphysics parameterization.

Figure 2: Total precipitation (top) and total hail fall (bottom) from 2-hour idealised simulations where the autoconversion rate is varied. The simulations are grouped into five sets; all simulations within one set have identical air motions. Set 1 air motions are driven by coupling with the microphysics scheme with the slowest autoconversion (80% slower). Set 5 is couple to the fastest autoconversion (80% faster). Simulations where the microphysics are coupled to the air motions are marked with a black outline. Within each set, the autoconversion rate is changed between simulations, but these changes do not feed back to changing the air motions.

Grabowski, W. W., & Morrison, H. (2016). Untangling microphysical impacts on deep convection applying a novel modeling methodology. Part II: Double-moment microphysics. Journal of the Atmospheric Sciences, 73(9), 3749-3770.

Grabowski, W. W., 2014: Extracting microphysical impacts in large-eddy simulations of shallow convection. J. Atmos. Sci., 71, 4493–4499

Grabowski, W. W., 2015: Untangling microphysical impacts on deep convection applying a novel modeling methodology. J. Atmos. Sci., 72, 2446–2464

Morrison, H., & Milbrandt, J. A. (2015). Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. Journal of the Atmospheric Sciences, 72(1), 287-311.

Seifert, A., & Beheng, K. D. (2006). A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description. Meteorology and atmospheric physics, 92(1-2), 45-66.

Varble, A., Zipser, E. J., Fridlind, A. M., Zhu, P., Ackerman, A. S., Chaboureau, J. P., … & Shipway, B. (2014). Evaluation of cloud‐resolving and limited area model intercomparison simulations using TWP-ICE observations: 1. Deep convective updraft properties. Journal of Geophysical Research: Atmospheres, 119(24).

White, B., Gryspeerdt, E., Stier, P., Morrison, H., Thompson, G., & Kipling, Z. (2017). Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects. Atmospheric Chemistry and Physics, 17(19), 12145-12175.