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Imaging Snowflakes in Freefall using a Novel Hovering Microscopy System
Understanding the relation between the complex morphology of snowflakes and their fall behavior is crucial in understanding the dynamics of natural snowfalls; with numerous applications in atmospheric and climate sciences, weather forecasting, sports, recreation, building construction, etc. To elucidate the fall behavior of snowflakes this project aims to perform imaging of snowflakes using a novel drone-based microscopy platform. This newly developed platform is capable of capturing high-resolution imagery of snowflakes in freefall, meanwhile monitoring the ambient flow conditions. The objective is to perform multiple data campaigns for different atmospheric conditions and bring new understanding to the snowflakes' most turbulent end-of-life time at descent through the atmospheric surface layer.
Keywords: UAVs, Snowfall, Microscopy, Imaging, Field Experiments
The student work will pick up from a previous project at the Institute of Fluid Dynamics that developed a novel drone-based microscopy platform. The platform includes a commercial-type DJI Matrice drone mounted with a long-range microscope and pulsed illumination system to capture high-resolution snowflake images in freefall at a distance. The current work will focus on applying the system in the field. The goal is to harvest outdoor imaging data to perform snowflake characterization during the 2025 winter months.
At the start of the research, the student will familiarize with the onboard flight and imaging systems. The student will implement automated snowflake acceptance and rejection routines from a previous project to improve online data streaming and collection. The student will also perform a hardware integration of onboard temperature, humidity, and flow sensors, allowing to append metadata of the ambient flow conditions. There will also be room for flow characterization of the imaging volume, as far as time permits.
In the second stage of the research, the student will deploy the imaging system in flight. Flight testing and practice sessions will be conducted in Zurich, while the data collection will be performed at a professional measurement site in Davos. The measurement campaigns will build up a large snowflake data set that can be cross-validated with on-site meteorological towers and compared to previous drone measurements using a commercial solution.
In the final stage, the acquired data will be analyzed. The student will need to extract and interpret statistical distributions of aspect ratio, orientation angle, and snowflake complexity. Furthermore, the student will need to compare these analyses against the temperature, humidity, and turbulence kinetic energy. This will allow a full statistical characterization of the snowflake morphological variety and freefall behavior. The student will need to map these data against the height of the air column and compare them against the existing literature.
The student work will pick up from a previous project at the Institute of Fluid Dynamics that developed a novel drone-based microscopy platform. The platform includes a commercial-type DJI Matrice drone mounted with a long-range microscope and pulsed illumination system to capture high-resolution snowflake images in freefall at a distance. The current work will focus on applying the system in the field. The goal is to harvest outdoor imaging data to perform snowflake characterization during the 2025 winter months.
At the start of the research, the student will familiarize with the onboard flight and imaging systems. The student will implement automated snowflake acceptance and rejection routines from a previous project to improve online data streaming and collection. The student will also perform a hardware integration of onboard temperature, humidity, and flow sensors, allowing to append metadata of the ambient flow conditions. There will also be room for flow characterization of the imaging volume, as far as time permits.
In the second stage of the research, the student will deploy the imaging system in flight. Flight testing and practice sessions will be conducted in Zurich, while the data collection will be performed at a professional measurement site in Davos. The measurement campaigns will build up a large snowflake data set that can be cross-validated with on-site meteorological towers and compared to previous drone measurements using a commercial solution.
In the final stage, the acquired data will be analyzed. The student will need to extract and interpret statistical distributions of aspect ratio, orientation angle, and snowflake complexity. Furthermore, the student will need to compare these analyses against the temperature, humidity, and turbulence kinetic energy. This will allow a full statistical characterization of the snowflake morphological variety and freefall behavior. The student will need to map these data against the height of the air column and compare them against the existing literature.
The goal of this project is to gather microscopic image data of snowflakes in freefall. These image data will help us gain new insight into the settling dynamics of natural snowfall in the atmosphere as is not accessible in the lab. This project contributes to a broader commitment to studying snowfalls in collaboration between the ETH Zürich Institute of Fluid Dynamics (IFD, ETH) and the Snow and Avalanche Institute (WSL-SLF) in Davos.
The goal of this project is to gather microscopic image data of snowflakes in freefall. These image data will help us gain new insight into the settling dynamics of natural snowfall in the atmosphere as is not accessible in the lab. This project contributes to a broader commitment to studying snowfalls in collaboration between the ETH Zürich Institute of Fluid Dynamics (IFD, ETH) and the Snow and Avalanche Institute (WSL-SLF) in Davos.
You will work with members from the Institute of Fluid Dynamics in the Department of Mechanical and Process Engineering (D-MAVT) and have access to facilities at the Autonomous Systems Lab (ASL, ETH) at ETH Zurich. To apply, please send your full transcript (up to the current semester) and a short motivation letter to kmuller@ethz.ch and fcoletti@ethz.ch. A strong interest and relevant classes in fluid dynamics, signal processing, and robotics are preferred. The project is planned to start in Spring 2025 and is open to bachelor’s and master’s students. See exp.ethz.ch/research/field-measurements and youtube.com/watch?v=-erfmxWTzPs&t=1s&ab_channel=UZHRoboticsandPerceptionGroup for more information.
You will work with members from the Institute of Fluid Dynamics in the Department of Mechanical and Process Engineering (D-MAVT) and have access to facilities at the Autonomous Systems Lab (ASL, ETH) at ETH Zurich. To apply, please send your full transcript (up to the current semester) and a short motivation letter to kmuller@ethz.ch and fcoletti@ethz.ch. A strong interest and relevant classes in fluid dynamics, signal processing, and robotics are preferred. The project is planned to start in Spring 2025 and is open to bachelor’s and master’s students. See exp.ethz.ch/research/field-measurements and youtube.com/watch?v=-erfmxWTzPs&t=1s&ab_channel=UZHRoboticsandPerceptionGroup for more information.
Airborne_Microscope_Snowflakes_in_Freefall.pdf
