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| In the framework of the collaboration between PolarCAP and CLOUDLAB, a unique data set will be produced and analyzed that includes polarimetric radar and lidar observations from the Leipzig Aerosol and Cloud Remote Observing System (LACROS) as well as data from the cloud-resolving spectral bin model COSMO-SPECS. PolarCAP will benefit strongly from the available cloud in-situ measurements. Progress will be achieved in the ability to constrain the efficiency of different ice nucleating substances, to link the time scales of microphysical processes and stratus dissipation, and to evaluate and develop remote-sensing-based retrievals for cloud properties. | In the framework of the collaboration between PolarCAP and CLOUDLAB, a unique data set will be produced and analyzed that includes polarimetric radar and lidar observations from the Leipzig Aerosol and Cloud Remote Observing System (LACROS) as well as data from the cloud-resolving spectral bin model COSMO-SPECS. PolarCAP will benefit strongly from the available cloud in-situ measurements. Progress will be achieved in the ability to constrain the efficiency of different ice nucleating substances, to link the time scales of microphysical processes and stratus dissipation, and to evaluate and develop remote-sensing-based retrievals for cloud properties. |
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| <WRAP tablewidth 60% center>**Figure 1:** Layout of the PolarCAP project, with the main instrumentation of TROPOS (LACROS, COSMO-SPECS) and ETH Zurich indicated.</WRAP>\\ | <met figure |
| | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:polarcap_fig1_2022.png" |
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| | title="Figure 1" |
| | caption="Layout of the PolarCAP project, with the main instrumentation of TROPOS (LACROS, COSMO-SPECS) and ETH Zurich indicated." |
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| One of the highlights of the campaign was the measurement of a seeder-feeder cloud system. The seeder-feeder case study is utilized to study how natural seeding (with ice crystals) into a lower supercooled liquid cloud affects precipitation formation and cloud properties. The knowledge gained in this study is not only useful for the region of Eriswil. As seeder-feeder interactions are frequent phenomena worldwide, it can help to improve weather models and weather forecasts worldwide. It turned out that the seeder-feeder process is misrepresented in weather models. The conditions were ideal for applying state-of-the-art remote-sensing and in-situ retrieval techniques and evaluating their consistency. | One of the highlights of the campaign was the measurement of a seeder-feeder cloud system. The seeder-feeder case study is utilized to study how natural seeding (with ice crystals) into a lower supercooled liquid cloud affects precipitation formation and cloud properties. The knowledge gained in this study is not only useful for the region of Eriswil. As seeder-feeder interactions are frequent phenomena worldwide, it can help to improve weather models and weather forecasts worldwide. It turned out that the seeder-feeder process is misrepresented in weather models. The conditions were ideal for applying state-of-the-art remote-sensing and in-situ retrieval techniques and evaluating their consistency. |
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| <WRAP centeralign> | <met figure |
| {{ projects:polarcap_2025_1.png?direct&800&nolink }} | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:polarcap_2025_1.png" |
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| <WRAP tablewidth 60% center>**Figure 1:** Schematic overview of the instruments and methods and the physical process derived from these methods. In brackets the required measurement device is highlighted.</WRAP> | title="Figure 1" |
| \\ \\ | caption="Schematic overview of the instruments and methods and the physical process derived from these methods. In brackets the required measurement device is highlighted." |
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| Figure 1 gives an overview of the applied retrievals. The applied approaches of the fall streak tracking algorithm, VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn), dual-wavelength ratio (DWR), Eddy dissipation rate (EDR), peakTree (Doppler-peak-separation algorithm), ice crystal shape retrieval (Vertical Distribution of Particle Shape, VDPS), riming retrievals, and ice crystal number concentration (ICNC) retrievals are shown. In addition, model results of HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) and ICON-D2 are used. Each of the mentioned retrievals contributes to a better understanding of the microphysical processes within the cloud. All retrievals together give a clear picture on the ice crystal habits and the changes in ice crystal properties along their way through the cloud. The results of this study are shown in Ohneiser et al., 2025a<html><sup><a href="#ref-2025a" class="citation-link">[1]</a></sup></html>. | Figure 1 gives an overview of the applied retrievals. The applied approaches of the fall streak tracking algorithm, VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn), dual-wavelength ratio (DWR), Eddy dissipation rate (EDR), peakTree (Doppler-peak-separation algorithm), ice crystal shape retrieval (Vertical Distribution of Particle Shape, VDPS), riming retrievals, and ice crystal number concentration (ICNC) retrievals are shown. In addition, model results of HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) and ICON-D2 are used. Each of the mentioned retrievals contributes to a better understanding of the microphysical processes within the cloud. All retrievals together give a clear picture on the ice crystal habits and the changes in ice crystal properties along their way through the cloud. The results of this study are shown in Ohneiser et al., 2025a<html><sup><a href="#ref-2025a" class="citation-link">[1]</a></sup></html>. |
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| Another highlight of the campaign was the measurement of ice-nucleating particles in Eriswil (Eri, coordinates: $47.07°\text{N}, 7.87°\text{E}$, $921\text{m}$ a.s.l.) and Hohenpeißenberg (HPB, coordinates: $47.80°\text{N}, 11.01°\text{E}$, $945\text{m}$ a.s.l.). The results are shown in Fig. 2. First, during the warm-Bise period, the INP population was found to be similar at Eri and HPB, no matter if a Bise cloud was present or not. Second, during cold-Bise, no INP contrast was found when both HPB and Eri were within or below the cold-Bise cloud and thus within the planetary boundary layer (PBL). Nevertheless, the INP concentration was overall found to be much lower than during the warm-Bise situations. Third, when the HPB site was located in the free troposphere during a cold-Bise situation, INP concentrations were also much higher compared to Eri that was still within the PBL. These observations led to the conclusion that during cold-Bise situations the INP reservoir is depleted. The inversion-capped winterly PBL is apparently not capable to replenish the INP reservoir. As remote-sensing and in-situ measurements at Eri revealed, the concentration of pristine ice crystals was higher than the available INP concentration. It is thus likely that a fraction of the ice crystals is formed by INP that were entrained from the free-troposphere into the Bise cloud, or alternatively that secondary ice formation mechanisms were active. The results of this study are shown in Ohneiser et al., 2025b<html><sup><a href="#ref-2025b" class="citation-link">[2]</a></sup></html>. | Another highlight of the campaign was the measurement of ice-nucleating particles in Eriswil (Eri, coordinates: $47.07°\text{N}, 7.87°\text{E}$, $921\text{m}$ a.s.l.) and Hohenpeißenberg (HPB, coordinates: $47.80°\text{N}, 11.01°\text{E}$, $945\text{m}$ a.s.l.). The results are shown in Fig. 2. First, during the warm-Bise period, the INP population was found to be similar at Eri and HPB, no matter if a Bise cloud was present or not. Second, during cold-Bise, no INP contrast was found when both HPB and Eri were within or below the cold-Bise cloud and thus within the planetary boundary layer (PBL). Nevertheless, the INP concentration was overall found to be much lower than during the warm-Bise situations. Third, when the HPB site was located in the free troposphere during a cold-Bise situation, INP concentrations were also much higher compared to Eri that was still within the PBL. These observations led to the conclusion that during cold-Bise situations the INP reservoir is depleted. The inversion-capped winterly PBL is apparently not capable to replenish the INP reservoir. As remote-sensing and in-situ measurements at Eri revealed, the concentration of pristine ice crystals was higher than the available INP concentration. It is thus likely that a fraction of the ice crystals is formed by INP that were entrained from the free-troposphere into the Bise cloud, or alternatively that secondary ice formation mechanisms were active. The results of this study are shown in Ohneiser et al., 2025b<html><sup><a href="#ref-2025b" class="citation-link">[2]</a></sup></html>. |
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| {{ projects:polarcap_2025_2.png?direct&1000&nolink }} | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:polarcap_2025_2.png" |
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| <WRAP tablewidth 60% center>**Figure 2:** INP conc. activated in air for the temperature range $0$ to $-30°\text{C}$, $a+b+c$) both in cold Bise (cloud top temperatures below $0°\text{C}$), $d+e+f$) Eri in cold Bise, HPB directly above Bise cloud in the free troposphere, $g+h+i$) Both in warm Bise situation.</WRAP> | title="Figure 2" |
| \\ \\ | caption="INP conc. activated in air for the temperature range $0$ to $-30°\text{C}$, $a+b+c$) both in cold Bise (cloud top temperatures below $0°\text{C}$), $d+e+f$) Eri in cold Bise, HPB directly above Bise cloud in the free troposphere, $g+h+i$) Both in warm Bise situation." |
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| We continue with an update on the modelling aspect of the cloud seeding missions, conducted by CLOUDLAB over the last three winter seasons. A significantly more efficient immersion freezing parameterization<html><sup><a href="#ref3" class="citation-link">[3]</a></sup></html> specifically designed for silver iodide (AgI) particles was implemented into the COSMO-SPECS model, which is based on an exponential fit to laboratory measurements<html><sup><a href="#ref4" class="citation-link">[4]</a></sup></html>, of the temperature dependent //freezing fraction//. | We continue with an update on the modelling aspect of the cloud seeding missions, conducted by CLOUDLAB over the last three winter seasons. A significantly more efficient immersion freezing parameterization<html><sup><a href="#ref3" class="citation-link">[3]</a></sup></html> specifically designed for silver iodide (AgI) particles was implemented into the COSMO-SPECS model, which is based on an exponential fit to laboratory measurements<html><sup><a href="#ref4" class="citation-link">[4]</a></sup></html>, of the temperature dependent //freezing fraction//. |
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| A large variety of ensemble simulations were conducted to optimize the set of model parameters to match the observations from the holographic imager (HOLIMO) best. We can confirm, that the DeMott parameterization<html><sup><a href="#ref5" class="citation-link">[5]</a></sup></html> (which is the default) leads to a need to release an exaggerated amount of flare particles to produce comparable INP values to the observations. However, the Omanovic freezing, requires much more realistic values, reducing the flare particle rate by $10^5$. For visual comparison of cloud radar and the model, the liquid and ice water contents (panel b), are plotted below, which shows good agreement in macrophysical parameters (cloud base/top height). Depending on the choice of model parameters, COSMO-SPECS is able to replicate the seeding events in terms of (liquid and ice particle) number concentration and contents well. For CDNC $(c)$, the deviation ranges from factor $3$ $(=300%)$, down to $10%$ error, same holds for ICNC $(d)$. In panels $(e)$ and $(f)$ we compare the model ensembles PSD of liquid and ice particles to the in-situ observations. The liquid particle spectra $(e)$ show deviations up to a factor of $12$, where the error in mean droplet diameters ranges from $5%$ to $20%$. COSMO-SPECS closely matches the observations of the frozen particle spectra, with a deviation of $<10%$ and number concentrations by $<30%$. Still, the narrow ice peak in the model spectra leaves more room for investigations of COSMO-SPECS. In a next step, the model PSDs are forward simulated using the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA), to compute the corresponding "virtual" radar reflectivity factor $\tilde{Z}_e$. Preliminary results with deviations ($|Z_e-\tilde{Z}_e|$) up to $10\,\text{dBZ}$ indicate that there is still potential to improve the PAMTRA configuration to better align the forward model setup with actual observed hydrometeor type descriptions. | A large variety of ensemble simulations were conducted to optimize the set of model parameters to match the observations from the holographic imager (HOLIMO) best. We can confirm, that the DeMott parameterization<html><sup><a href="#ref5" class="citation-link">[5]</a></sup></html> (which is the default) leads to a need to release an exaggerated amount of flare particles to produce comparable INP values to the observations. However, the Omanovic freezing, requires much more realistic values, reducing the flare particle rate by $10^5$. For visual comparison of cloud radar and the model, the liquid and ice water contents (panel b), are plotted below, which shows good agreement in macrophysical parameters (cloud base/top height). Depending on the choice of model parameters, COSMO-SPECS is able to replicate the seeding events in terms of (liquid and ice particle) number concentration and contents well. For CDNC $(c)$, the deviation ranges from factor $3$ $(=300%)$, down to $10%$ error, same holds for ICNC $(d)$. In panels $(e)$ and $(f)$ we compare the model ensembles PSD of liquid and ice particles to the in-situ observations. The liquid particle spectra $(e)$ show deviations up to a factor of $12$, where the error in mean droplet diameters ranges from $5%$ to $20%$. COSMO-SPECS closely matches the observations of the frozen particle spectra, with a deviation of $<10%$ and number concentrations by $<30%$. Still, the narrow ice peak in the model spectra leaves more room for investigations of COSMO-SPECS. In a next step, the model PSDs are forward simulated using the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA), to compute the corresponding "virtual" radar reflectivity factor $\tilde{Z}_e$. Preliminary results with deviations ($|Z_e-\tilde{Z}_e|$) up to $10\,\text{dBZ}$ indicate that there is still potential to improve the PAMTRA configuration to better align the forward model setup with actual observed hydrometeor type descriptions. |
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| <WRAP tablewidth 60% center>**Figure 3:** Composite of $(a)$ cloud radar reflectivity factor and $(b)$ liquid water content overlayed with ice water content of a COSMO-SPECS run on $400\,\text{m}$ horizontal resolution. Panels $(c)$ and $(d)$ show the cloud droplet and ice crystal number concentration of ensemble simulations, varying in flare particle emission and initial CCN and INP values. Panels $(e)$ and $(f)$ provide the mean particle size distribution of liquid and frozen hydrometeors for the time frame marked by the red dashed lines. Black lines in panels $(a)$ and $(b)$ indicate the height of the holographic imager over time. | |
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| | caption="Composite of $(a)$ cloud radar reflectivity factor and $(b)$ liquid water content overlayed with ice water content of a COSMO-SPECS run on $400\,\text{m}$ horizontal resolution. Panels $(c)$ and $(d)$ show the cloud droplet and ice crystal number concentration of ensemble simulations, varying in flare particle emission and initial CCN and INP values. Panels $(e)$ and $(f)$ provide the mean particle size distribution of liquid and frozen hydrometeors for the time frame marked by the red dashed lines. Black lines in panels $(a)$ and $(b)$ indicate the height of the holographic imager over time." |
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| **References** \\ | **References** \\ |
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| During the campaign 2023/24 two more cooperations took place. The PROM project CORSIPP of LIM (Leipzig Institute for Meteorology) joined the campaign in Eriswil with their scanning 94GHz polarimetric cloud radar. In addition, EPFL (École Polytechnique Fédérale de Lausanne) joined the campaign with a scanning polarimetric X-band radar. In the end, the campaign was one of the largest joint deployments of multi-wavelength radar and lidar systems. An overview of the campaign can be seen in Figure 1. An additional side project of TROPOS and the Hohenpeißenberg Meteorological Observatory of the German Weather Service (DWD) dealt with the characterization of the aerosol conditions during the supercooled stratus cloud events. Aerosol in-situ samplers were installed at Hohenpeißenberg observatory and Eirswil to characterize potential contrasts in the concentration of ice nucleating particles (INP) between the two sites. The analysis of these datasets (2 weeks of samples were taken) is ongoing in 2024. | During the campaign 2023/24 two more cooperations took place. The PROM project CORSIPP of LIM (Leipzig Institute for Meteorology) joined the campaign in Eriswil with their scanning 94GHz polarimetric cloud radar. In addition, EPFL (École Polytechnique Fédérale de Lausanne) joined the campaign with a scanning polarimetric X-band radar. In the end, the campaign was one of the largest joint deployments of multi-wavelength radar and lidar systems. An overview of the campaign can be seen in Figure 1. An additional side project of TROPOS and the Hohenpeißenberg Meteorological Observatory of the German Weather Service (DWD) dealt with the characterization of the aerosol conditions during the supercooled stratus cloud events. Aerosol in-situ samplers were installed at Hohenpeißenberg observatory and Eirswil to characterize potential contrasts in the concentration of ice nucleating particles (INP) between the two sites. The analysis of these datasets (2 weeks of samples were taken) is ongoing in 2024. |
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| {{ projects:polarcap_first_year_2024_1.png?direct&800&nolink }} | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:polarcap_first_year_2024_1.png" |
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| <WRAP tablewidth 60% center>**Figure 1:** Cloudlab field site in Eriswil as set up during the campaign in winter 2023/2024. Photo by Jan Henneberger.</WRAP> | title="Figure 1" |
| \\ \\ | caption="Cloudlab field site in Eriswil as set up during the campaign in winter 2023/2024. Photo by Jan Henneberger." |
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| With our radar measurements during the cloud seeding experiments conducted by the ETH, we were able to support the ETH colleagues with their studies on ice crystal growth mechanisms. In addition, we identified case studies of natural cloud seeding events. By exploring these case studies in a great detail, we hope to learn more about the involved processes that lead to enhanced precipitation during natural seeding events. Recently, we developed a fall streak tracking algorithm that helps to identify the evolution of the microphysical properties of the ice crystals on their pathway through the cloud system. The 35-GHz and 94-GHz cloud radar measurements gave us the chance to calculate a dual wavelength ratio which gives us more insight into the cloud microphysical processes. | With our radar measurements during the cloud seeding experiments conducted by the ETH, we were able to support the ETH colleagues with their studies on ice crystal growth mechanisms. In addition, we identified case studies of natural cloud seeding events. By exploring these case studies in a great detail, we hope to learn more about the involved processes that lead to enhanced precipitation during natural seeding events. Recently, we developed a fall streak tracking algorithm that helps to identify the evolution of the microphysical properties of the ice crystals on their pathway through the cloud system. The 35-GHz and 94-GHz cloud radar measurements gave us the chance to calculate a dual wavelength ratio which gives us more insight into the cloud microphysical processes. |
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| <p class="polarcap-caption"><strong>Figure 2:</strong> Temporal evolution of the ice crystal number concentration of COSMO-SPECS Run2 with artificial seeding at 10:52:00 UTC. The ruler shows the distance of the plume over time, with marks units of km. The x shows the location of the observational site.</p> | <p class="polarcap-caption"><strong>Figure 2:</strong> Temporal evolution of the ice crystal number concentration of COSMO-SPECS Run2 with artificial seeding at 10:52:00 UTC. The ruler shows the distance of the plume over time, with marks units of km. The x shows the location of the observational site.</p> |
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| {{ projects:polarcap_first_year_2024_8.png?direct&400&nolink }} | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:polarcap_first_year_2024_8.png" |
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| <WRAP tablewidth 60% center>**Figure 3:** 35-GHz cloud radar observations from 25 January 2023. Upper panel shows the radar reflectivity factor, the lower panel shows the linear depolarization ratio. Red dashed line corresponds to the seeding height within the COMSO-SPECS simulations.</WRAP> | title="Figure 3" |
| \\ \\ | caption="35-GHz cloud radar observations from 25 January 2023. Upper panel shows the radar reflectivity factor, the lower panel shows the linear depolarization ratio. Red dashed line corresponds to the seeding height within the COMSO-SPECS simulations." |
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| Figure 3 illustrates a time-height cross-section, of the 35-GHz radar reflectivity factor and linear depolarization ratio from 10:30 to 11:40 UTC on the corresponding day as the simulations. The occurrence of the plume event above the radar at 11:02 UTC closely aligns with the simulations. Next steps involve the evaluation of this event through a comprehensive model-observation comparison. This step aims to investigate deeper into the characteristics and dynamics of the observed phenomenon, enhancing our understanding through analysis and validation against simulated data. | Figure 3 illustrates a time-height cross-section, of the 35-GHz radar reflectivity factor and linear depolarization ratio from 10:30 to 11:40 UTC on the corresponding day as the simulations. The occurrence of the plume event above the radar at 11:02 UTC closely aligns with the simulations. Next steps involve the evaluation of this event through a comprehensive model-observation comparison. This step aims to investigate deeper into the characteristics and dynamics of the observed phenomenon, enhancing our understanding through analysis and validation against simulated data. |