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Improving our understanding of the origin of ice crystals in clouds using Deep Learning and Nano-scale 3D printing

   Department of Earth and Environmental Sciences

  , , Prof M Gallagher  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Clouds remain the largest contribution to overall uncertainty in climate feedbacks (IPCC, 2021). The ice-phase is a particular source of significant uncertainty (e.g. Field et al. 2017). Whereas clouds that span temperatures warmer than 0ºC are relatively well-understood, at temperatures lower than 0ºC the possibility of the ice phase opens up a large number of additional processes that are poorly understood (e.g. Lohmann and Feicher, 2005). In order to understand the importance of the ice phase the UoM operates a three-view cloud particle imager (3V-CPI, https://airbornescience.nasa.gov/instrument/3V-CPI) on research aircraft and at ground-based measurement sites.

The 3V-CPI is able to capture images of ice crystals from three different viewing directions. In one of the directions the images have 2.3 µm pixel resolution, 1280 × 1024 and up to 400 images can be taken per second. This generates a large amount of data, with a large amount of detail present within the images. Ice crystals grow into different shapes, known as habits, which depend on ambient conditions within the clouds. They may be columnar, plate-like, polycrystalline, and present evidence of growth instabilities such as branching and secondary branching. They may also be aggregated together or display evidence of accreted liquid water, and may even be fragmented. These ice particle properties tell us important information on the myriad of processes that can occur within mixed-phase clouds.

During the PhD you will collect and use datasets from the 3V-CPI and apply current Deep Learning techniques to learn about ice crystal properties in a range of mixed-phase and ice-only clouds. The Deep Learning techniques will also be used to perform image / habit recognition. Deep Embedded Clustering (DEC, Xie et al. 2016), using Convolutional Layers, is a promising technique for this problem. It has the advantage of being an unsupervised clustering method, which means that the labour-intensive process of labelling images is not required. You will learn techniques that will enable significant progress to be made in the field of atmospheric science, but also be readily transferable to other areas of employment.

Depending on your interests you may also wish to take part in calibration activities using “engineered particles”. To aid with this the Faculty of Science and Engineering have acquired a new state-of-the-art NanoScribe 3D printer, which uses femto-second UV laser pulses to cure UV sensitive resins. This activity would involve the manufacture of model / analogue ‘ice crystals” stable at room temperature and capable of being used as calibration standards. They may also be used to investigate whether 3D reconstruction is possible from 2D images, using Deep Learning techniques. The University of Manchester has recently invested in a major initiative in AI and has plans to be an AI Centre of Excellence in the near future. 

Datasets to be used include flight data from cumulus congestus clouds in New Mexico (Summer 2022) and over the UK in the summer of 2023/2024. There will be the opportunity to visit or participate in these latter campaigns depending on your interests

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder). 

All appointments are made on merit.


Field, P.R., Lawson, R.P., Brown, P.R.A., Lloyd, G., Westbrook, C., Moisseev, D., Miltenberger, A., Nenes, A., Blyth, A., Choularton, T., Connolly, P., Buehl, J., Crosier, J., Cui, Z., Dearden, C., DeMott, P., Flossmann, A., Heymsfield, A., Huang, Y., Kalesse, H., Kanji, Z.A., Korolev, A., Kirchgaessner, A., Lasher-Trapp, S., Leisner, T., McFarquhar, G., Phillips, V., Stith, J., Sullivan, S., 2016. Chapter 7. Secondary Ice Production - current state of the science and recommendations for the future. Meteorol. Monogr. AMSMONOGRAPHS-D-16-0014.1. https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0014.1
IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.
Lohmann, U., Feichter, J., 2005. Global indirect aerosol effects: A review. Atmos. Chem. Phys. 5, 715–737.
Xie, J., Girshick, R., Farhadi, A., 2016. Unsupervised Deep Embedding for Clustering Analysis.

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