Periodic Reporting for period 1 - CLOUDLAB (Using clouds as a natural laboratory to improve precipitation forecast skills)
Reporting period: 2021-09-01 to 2023-02-28
The CLOUDLAB project addresses the limited understanding of cloud microphysical processes, particularly the formation and growth of ice crystals within supercooled clouds. These processes are challenging to study in a systematic way in a natural environment due to rapid changes which makes it difficult to disentangle different processes.
CLOUDLAB holds significant societal relevance for four key reasons:
1. Improved weather forecasts and climate projections: The knowledge gained from the project, particularly regarding the formation and growth of ice crystals within supercooled clouds, aims to improve the ice-phase parameterizations in weather forecasts and climate models. The prediction of the life cycle (formation, development, and dissipation) and geographic distribution of low clouds pose a major challenge for numerical weather prediction models due to spatial heterogeneity and an insufficient understanding and representation of the underlying processes.
2. Improve the efficiency of cloud seeding for precipitation enhancement and fog dispersal: Cloud seeding is gaining interest on increase presentation in regions facing water scarcity and to disperse fog around airports to improve visibility and safety. The efficiency of cloud seeding is still unclear because a control situation is often missing (i.e. how the cloud would have evolved without seeding).
3. Climate intervention (geoengineering): The project's insights into seeding-induced cloud microstructure modifications are also relevant for climate intervention strategies. These strategies aim to reduce longwave heating of clouds through thinning of wintertime cirrus or mixed-phase clouds in polar regions using suitable INPs or to increase cloud reflectivity of stratus and stratocumulus clouds by seeding with appropriate aerosol particles.
CLOUDLAB aims to enhance the understanding of ice processes in supercooled clouds by combining targeted cloud seeding with laboratory, field and modelling approaches, with the ultimate goal to improve precipitation forecast and climate projections. To achieve this overarching goal, the project is divided into different objectives:
• Successfully injecting Ice-Nucleating Particles (INPs) into supercooled stratus clouds and detecting the seeded patch using in-situ and ground-based remote sensing instrumentation
• Inducing partial glaciation of supercooled stratus clouds by injecting sufficiently high concentrations of INPs.
• Utilizing the spatial inhomogeneities of ice crystals and cloud droplets in the evolving plume after targeted cloud seeding to infer the speed of glaciation.
• Using measurements of ice crystal concentrations, sizes and habits to constrain and improve ice nucleation and growth parameterizations in the corresponding ICON large eddy simulations.
• Increasing precipitation forecast skills through the improved cloud microphysics parameterization scheme.
CLOUDLAB holds significant societal relevance for four key reasons:
1. Improved weather forecasts and climate projections: The knowledge gained from the project, particularly regarding the formation and growth of ice crystals within supercooled clouds, aims to improve the ice-phase parameterizations in weather forecasts and climate models. The prediction of the life cycle (formation, development, and dissipation) and geographic distribution of low clouds pose a major challenge for numerical weather prediction models due to spatial heterogeneity and an insufficient understanding and representation of the underlying processes.
2. Improve the efficiency of cloud seeding for precipitation enhancement and fog dispersal: Cloud seeding is gaining interest on increase presentation in regions facing water scarcity and to disperse fog around airports to improve visibility and safety. The efficiency of cloud seeding is still unclear because a control situation is often missing (i.e. how the cloud would have evolved without seeding).
3. Climate intervention (geoengineering): The project's insights into seeding-induced cloud microstructure modifications are also relevant for climate intervention strategies. These strategies aim to reduce longwave heating of clouds through thinning of wintertime cirrus or mixed-phase clouds in polar regions using suitable INPs or to increase cloud reflectivity of stratus and stratocumulus clouds by seeding with appropriate aerosol particles.
CLOUDLAB aims to enhance the understanding of ice processes in supercooled clouds by combining targeted cloud seeding with laboratory, field and modelling approaches, with the ultimate goal to improve precipitation forecast and climate projections. To achieve this overarching goal, the project is divided into different objectives:
• Successfully injecting Ice-Nucleating Particles (INPs) into supercooled stratus clouds and detecting the seeded patch using in-situ and ground-based remote sensing instrumentation
• Inducing partial glaciation of supercooled stratus clouds by injecting sufficiently high concentrations of INPs.
• Utilizing the spatial inhomogeneities of ice crystals and cloud droplets in the evolving plume after targeted cloud seeding to infer the speed of glaciation.
• Using measurements of ice crystal concentrations, sizes and habits to constrain and improve ice nucleation and growth parameterizations in the corresponding ICON large eddy simulations.
• Increasing precipitation forecast skills through the improved cloud microphysics parameterization scheme.
Within this timeframe, the CLOUDLAB project has achieved several key milestones in its efforts to advance the understanding of cloud physics. The main work performed and results achieved so far are as follows:
1. Demonstrated that a multi-rotor Uncrewed Aerial Vehicle (UAV) can be used for targeted glaciogenic cloud seeding, which opens up new possibilities for cloud seeding research and applications in weather modification and climate intervention.
2. Integration of optical particle counters (OPCs) on the measurement UAV and HoloBalloon Tethered Balloon System (TBS) to provide accurate aerosol measurements to detect the seeding plume.
3. Set-up of the CLOUDLAB main site including a large set of ground-based remote sensing instrumentation (e.g. cloud radars, microwave radiometers), aerosol instrumentation and the TBS HoloBalloon. Established different UAV launching sites in the surrounding of the main site and obtained flight permissions for the UAVs, including the horizontal seeding pattern, which has ensured smooth operations and efficient execution of the experiments.
4. Developed a quick-look feature for data review after each scan and developed a webpage to display data from all remote sensing sources. This information is essential for adapting experimental parameters to environmental conditions, particularly the wind direction.
5. Conducted 38 out-of-cloud seeding experiments to characterize the dispersion and particle size distribution of the seeding plume, utilizing the measurement UAV equipped with a OPC. This has provided valuable data on the dispersion of seeding plumes and their interactions with the environment.
6. Conducted 52 successful in-cloud seeding experiments at varying temperatures, growth times, and INP concentrations, providing valuable insights into cloud microphysics (e.g. ice crystal growth rates, Wegener-Bergeron-Findeisen process) and the conditions necessary for effective cloud seeding.
7. Analysis of remote sensing and in-situ measurements to quantify the number and size of cloud droplets and ice crystals formed during the seeding experiments.
8. Implemented seeding particle tracers in the ICON-LES model, which enables the simulation of the conducted seeding experiments. The large-eddy simulations of seeding experiments were able to reproduce the cloud conditions and to capture microphysical changes induced by seeding.
9. An overview of CLOUDLAB project with some first results was summarized in a manuscript that was submitted to BAMS.
1. Demonstrated that a multi-rotor Uncrewed Aerial Vehicle (UAV) can be used for targeted glaciogenic cloud seeding, which opens up new possibilities for cloud seeding research and applications in weather modification and climate intervention.
2. Integration of optical particle counters (OPCs) on the measurement UAV and HoloBalloon Tethered Balloon System (TBS) to provide accurate aerosol measurements to detect the seeding plume.
3. Set-up of the CLOUDLAB main site including a large set of ground-based remote sensing instrumentation (e.g. cloud radars, microwave radiometers), aerosol instrumentation and the TBS HoloBalloon. Established different UAV launching sites in the surrounding of the main site and obtained flight permissions for the UAVs, including the horizontal seeding pattern, which has ensured smooth operations and efficient execution of the experiments.
4. Developed a quick-look feature for data review after each scan and developed a webpage to display data from all remote sensing sources. This information is essential for adapting experimental parameters to environmental conditions, particularly the wind direction.
5. Conducted 38 out-of-cloud seeding experiments to characterize the dispersion and particle size distribution of the seeding plume, utilizing the measurement UAV equipped with a OPC. This has provided valuable data on the dispersion of seeding plumes and their interactions with the environment.
6. Conducted 52 successful in-cloud seeding experiments at varying temperatures, growth times, and INP concentrations, providing valuable insights into cloud microphysics (e.g. ice crystal growth rates, Wegener-Bergeron-Findeisen process) and the conditions necessary for effective cloud seeding.
7. Analysis of remote sensing and in-situ measurements to quantify the number and size of cloud droplets and ice crystals formed during the seeding experiments.
8. Implemented seeding particle tracers in the ICON-LES model, which enables the simulation of the conducted seeding experiments. The large-eddy simulations of seeding experiments were able to reproduce the cloud conditions and to capture microphysical changes induced by seeding.
9. An overview of CLOUDLAB project with some first results was summarized in a manuscript that was submitted to BAMS.
CLOUDLAB has made notable advancements in cloud physics research, particularly in targeted cloud seeding using UAVs, co-located state-of-the-art in-situ and remote sensing measurements, and the replication of seeding experiments with the ICON model. The innovative research approach of CLOUDLAB allows to directly infer the growth rates of ice crystals in natural clouds – a task that was considered too difficult up to now.
We demonstrated that a multi-rotor UAV can be used for targeted glaciogenic cloud seeding. The project's innovative approach of using UAVs instead of aircrafts for cloud seeding enables flexible and precise control of the seeding location and repeating seeding experiments at high frequency. By altering experimental parameters (such as seeding distance) in a controlled manner, we can conduct laboratory-like experiments in a natural cloud environment (i.e. supercooled stratus clouds). The combination of detailed in-situ observations and continuous remote sensing measurements will enhance our understanding of ice formation and growth in mixed-phase clouds.
As the project proceeds, this unique data set will be used to further refine the cloud microphysics parameterizations, leading to improved weather forecasting and climate projections. Additionally, the project's findings will help address knowledge gaps in the fields of weather modification and climate intervention.
By the end of the project, the CLOUDLAB team aims to consolidate their findings into high-quality publications, thereby contributing to the scientific community's understanding of cloud physics. The project's novel methodologies and valuable insights are expected to have a lasting impact in the fields of atmospheric science and climate research.
We demonstrated that a multi-rotor UAV can be used for targeted glaciogenic cloud seeding. The project's innovative approach of using UAVs instead of aircrafts for cloud seeding enables flexible and precise control of the seeding location and repeating seeding experiments at high frequency. By altering experimental parameters (such as seeding distance) in a controlled manner, we can conduct laboratory-like experiments in a natural cloud environment (i.e. supercooled stratus clouds). The combination of detailed in-situ observations and continuous remote sensing measurements will enhance our understanding of ice formation and growth in mixed-phase clouds.
As the project proceeds, this unique data set will be used to further refine the cloud microphysics parameterizations, leading to improved weather forecasting and climate projections. Additionally, the project's findings will help address knowledge gaps in the fields of weather modification and climate intervention.
By the end of the project, the CLOUDLAB team aims to consolidate their findings into high-quality publications, thereby contributing to the scientific community's understanding of cloud physics. The project's novel methodologies and valuable insights are expected to have a lasting impact in the fields of atmospheric science and climate research.