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| projects:life_cycle_of_convective_storms [2025/09/03 11:57] – kathrin | projects:life_cycle_of_convective_storms [2026/09/13 12:46] (current) – ayush |
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| KIT: [[https://www2.meteo.uni-bonn.de/spp2115/doku.php?id=researchers#andrewbarrett|Andrew Barrett (independent researcher)]]\\ | KIT: [[https://www2.meteo.uni-bonn.de/spp2115/doku.php?id=researchers#andrewbarrett|Andrew Barrett (independent researcher)]]\\ |
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| ===Abstract=== | ===Abstract=== |
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| 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. | 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. |
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| {{ life_cycle_piggybacking.png?direct&300 }} | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:life_cycle_piggybacking.png" |
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| 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. | 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. |
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| 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. | 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. |
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| Work in the first 2 years has focussed on understanding the sensitivity of convective storms to cloud microphysics; in the coming year we will evaluate the effects on the polarimetric radar signatures. We have used the microphysical piggybacking technique (Grabowski, 2014, 2015; Grabowski and Morrison, 2016) to separate direct effects of microphysics from indirect effects caused by feedbacks from latent heating on to cloud-scale circulations. On the time scale of convective storms, we find that most – but not all – variability is caused by microphysical processes, with only a small change to cloud-scale circulations, with the relative impact of feedbacks to the cloud-scale circulations increasing with time. | Work in the first 2 years has focussed on understanding the sensitivity of convective storms to cloud microphysics; in the coming year we will evaluate the effects on the polarimetric radar signatures. We have used the microphysical piggybacking technique (Grabowski, 2014, 2015; Grabowski and Morrison, 2016) to separate direct effects of microphysics from indirect effects caused by feedbacks from latent heating on to cloud-scale circulations. On the time scale of convective storms, we find that most – but not all – variability is caused by microphysical processes, with only a small change to cloud-scale circulations, with the relative impact of feedbacks to the cloud-scale circulations increasing with time. |
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| {{ figure_01_life_cycle_2021.png?direct&950 }} | |
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| <WRAP tablewidth 60% center>**Figure 1:** West-East cross-sections through a composite of simulated convective cells showing the relative locations of hydrometeor mass (grey shading), sources of mass (blue contours) and sinks of mass (red contours) for four different hydrometeor categories a) rain, b) graupel, c) hail and d) ice and snow combined. Storms are composited on their updraft location at 5 km, the updraft core is marked by grey line contours.</WRAP> | |
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| We first asked what the 3D structure of simulated convective cells looks like, and where in those cells do the relevant microphysical processes occur. We were particularly interested in which microphysical processes were responsible for producing extreme rainfall and hail in thunderstorms. Using a composite of simulated storms from the ICON model using the 2-moment microphysics scheme (Seifert and Beheng, 2006) we were able to answer these questions. In Figure 1, hydrometeor mass contents (grey shading), sources (blue) and sinks (red) are marked for three “microphysical pathways” (rain, mixed-phase and ice-phase pathways). Using this analysis, we determined that 80-96% of surface precipitation occurs via the mixed-phase pathway, where either graupel (see Fig. 1b) or hail (see Fig. 1c) stones form and grow by collecting supercooled liquid hydrometeors with which they collide (known as the riming process). These hydrometeors mainly melt before reaching the surface and are the origin of the rain mass far from the updraft (see Fig. 1a). Almost no surface rain was formed purely via collision and coalescence of liquid drops (the “warm rain” pathway) because the raindrops either freeze or are collected by frozen hydrometeors (riming) before falling from the updraft (see Fig. 1a). Ice and snow remain suspended in the upper atmosphere (see Fig. 1d) and do not contribute to surface precipitation at all. Performing similar analysis under different environmental conditions (increased wind shear, increased aerosol concentration) confirms the importance of the mixed-phase pathway and riming in various conditions.\\ | <met figure |
| | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:figure_01_life_cycle_2021.png" |
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| | title="Figure 1" |
| | caption="West-East cross-sections through a composite of simulated convective cells showing the relative locations of hydrometeor mass (grey shading), sources of mass (blue contours) and sinks of mass (red contours) for four different hydrometeor categories a) rain, b) graupel, c) hail and d) ice and snow combined. Storms are composited on their updraft location at $5\,\mathrm{km}$, the updraft core is marked by grey line contours." |
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| The importance of the mixed-phase pathway, and in particular the riming process warrants further investigation. Using the piggybacking framework that we recently implemented in the ICON model, we perform experiments to further understand the increased importance of the riming process in comparison to other microphysical processes. We run realistic simulations where each of five microphysical processes is systematically varied. The riming process again shows the largest sensitivity out of all processes. Figure 2 shows the sensitivity of radar reflectivity histograms to parameterized riming rate (modified from a factor 4 decrease [left column] to a factor 4 increase [right column]). As the riming rate increases, there is a systematic reduction of high reflectivity points at all heights and an increase in the number of low reflectivity values especially above 7 km altitude. In the same simulations, the increased riming rate caused a significant increase in surface precipitation (due to the importance of the mixed-phase pathway, mentioned above). This highlights an interesting paradox: that modifying the riming rate can increase the surface precipitation while reducing the number of large hydrometeors in the atmosphere (and therefore reducing radar reflectivity). | We first asked what the 3D structure of simulated convective cells looks like, and where in those cells do the relevant microphysical processes occur. We were particularly interested in which microphysical processes were responsible for producing extreme rainfall and hail in thunderstorms. Using a composite of simulated storms from the ICON model using the 2-moment microphysics scheme (Seifert and Beheng, 2006) we were able to answer these questions. In Figure 1, hydrometeor mass contents (grey shading), sources (blue) and sinks (red) are marked for three “microphysical pathways” (rain, mixed-phase and ice-phase pathways). Using this analysis, we determined that 80–96% of surface precipitation occurs via the mixed-phase pathway, where either graupel (see Fig. 1b) or hail (see Fig. 1c) stones form and grow by collecting supercooled liquid hydrometeors with which they collide (known as the riming process). These hydrometeors mainly melt before reaching the surface and are the origin of the rain mass far from the updraft (see Fig. 1a). Almost no surface rain was formed purely via collision and coalescence of liquid drops (the “warm rain” pathway) because the raindrops either freeze or are collected by frozen hydrometeors (riming) before falling from the updraft (see Fig. 1a). Ice and snow remain suspended in the upper atmosphere (see Fig. 1d) and do not contribute to surface precipitation at all. Performing similar analysis under different environmental conditions (increased wind shear, increased aerosol concentration) confirms the importance of the mixed-phase pathway and riming in various conditions.\\ |
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| \\ | The importance of the mixed-phase pathway, and in particular the riming process warrants further investigation. Using the piggybacking framework that we recently implemented in the ICON model, we perform experiments to further understand the increased importance of the riming process in comparison to other microphysical processes. We run realistic simulations where each of five microphysical processes is systematically varied. The riming process again shows the largest sensitivity out of all processes. Figure 2 shows the sensitivity of radar reflectivity histograms to parameterized riming rate (modified from a factor 4 decrease [left column] to a factor 4 increase [right column]). As the riming rate increases, there is a systematic reduction of high reflectivity points at all heights and an increase in the number of low reflectivity values especially above $7\,\mathrm{km}$ altitude. In the same simulations, the increased riming rate caused a significant increase in surface precipitation (due to the importance of the mixed-phase pathway, mentioned above). This highlights an interesting paradox: that modifying the riming rate can increase the surface precipitation while reducing the number of large hydrometeors in the atmosphere (and therefore reducing radar reflectivity). |
| {{ figure_02_life_cycle_2021.png?direct&950 }}\\ | |
| \\ | <met figure |
| <WRAP tablewidth 60% center>**Figure 2:** Histograms of simulated radar reflectivity and height showing counts at each pixel in logarithmic units (3 = 10^3). The parameterized riming rate is modified in these simulations, with a scaling factor of 0.25 in the left column, increasing to 4.0 in the right column. The top row shows normal, fully coupled, ICON simulations whereas the bottom row shows piggybacked simulations, where the wind field is kept identical in each simulation. The similarity between the top and bottom row shows that microphysical processes are responsible for the differences seen, and that changes to the model wind field are relatively unimportant at these short time scales (differences between top and bottom rows are due to changes in the wind field) – a finding only possible using the microphysical piggybacking technique.</WRAP> | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:figure_02_life_cycle_2021.png" |
| | width="950" |
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| | title="Figure 2" |
| | caption="Histograms of simulated radar reflectivity and height showing counts at each pixel in logarithmic units ($3 = 10^3$). The parameterized riming rate is modified in these simulations, with a scaling factor of 0.25 in the left column, increasing to 4.0 in the right column. The top row shows normal, fully coupled, ICON simulations whereas the bottom row shows piggybacked simulations, where the wind field is kept identical in each simulation. The similarity between the top and bottom row shows that microphysical processes are responsible for the differences seen, and that changes to the model wind field are relatively unimportant at these short time scales (differences between top and bottom rows are due to changes in the wind field) – a finding only possible using the microphysical piggybacking technique." |
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| The insights gained from this analysis have allowed us to contribute to the development of the German Weather Service’s new ICON model configuration, developed as part of the SINFONY project. For the first time a 2-moment microphysics scheme will be used operationally; however, it was producing large biases in the vertical distribution of radar reflectivity and the frequency of high-intensity rainfall (>5 mm/hr). After reducing the parameterized riming rate within ICON, evaluation of simulations for 20 days in June 2020 showed a marked improvement. A substantial reduction of the bias (by more than half) of too frequent localised heavy precipitation was seen together with an improvement in the vertical distribution of radar reflectivity when compared to observations. We are continuing our work together to improve the ICON model’s representation of the 2D surface precipitation field and the 3D hydrometeor distribution. | The insights gained from this analysis have allowed us to contribute to the development of the German Weather Service’s new ICON model configuration, developed as part of the SINFONY project. For the first time a 2-moment microphysics scheme will be used operationally; however, it was producing large biases in the vertical distribution of radar reflectivity and the frequency of high-intensity rainfall ($> 5\,\mathrm{mm/hr}$). After reducing the parameterized riming rate within ICON, evaluation of simulations for 20 days in June 2020 showed a marked improvement. A substantial reduction of the bias (by more than half) of too frequent localised heavy precipitation was seen together with an improvement in the vertical distribution of radar reflectivity when compared to observations. We are continuing our work together to improve the ICON model’s representation of the 2D surface precipitation field and the 3D hydrometeor distribution. |
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| **References:** | **References:** |
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| 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. | 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. |
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| {{ life_cycle_figure2.png?direct&750 }} | <met figure |
| \\ | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:life_cycle_figure2.png" |
| <WRAP tablewidth 60% center>**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.</WRAP>\\ | width="750" |
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| | title="Figure 2" |
| | caption="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." |
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| **References** | **References** |
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