- 1University of Leeds, Institute for Climate and Atmospheric Science, School of Earth and Environment, Leeds, UK
- 2National Centre for Atmospheric Science, Leeds, UK
- 3Met Office, Exeter, UK
- 4University of Manchester, Department of Earth and Environmental Sciences, Manchester, UK
Graupel continues to be the least well constrained and understood hydrometeor in numerical models. Playing an important role in cloud electrification and precipitation in cumulonimbus, graupel is critical to model correctly. New observations from the Deep Convective Microphysics Experiment1 (DCMEX) have been used to evaluate a recently developed machine-learning algorithm, which categorises hydrometeor images. An overview of the overarching ice habit distribution from DCMEX cumulonimbus will be presented, alongside preliminary analysis of the observed graupel formation and its corresponding environmental conditions.
DCMEX presents a unique opportunity of complementary in-situ and radar observations. The project was conducted in the Magdalena Mountains of New Mexico during the summer of 2022. The airborne sampling strategy involved repeated sampling of cloud turrets as convection strengthened through the day, allowing for evolving in-situ observations as the cloud deepened. The campaign was able to successfully sample convective cloud on 17 out of 19 flight days.
To understand the microphysical processes within a developing cloud, ice images from the 2D Stereo Probe and High Volume Precipitation Spectrometer have been analysed. These images have been categorised into habit, using the supervised machine learning algorithms from Jaffeux et al.2,3. Independent evaluation of the algorithms has been conducted, to test the generalisation capabilities under different cloud conditions.
This work aims to strengthen our understanding of graupel in deep convective cloud, whilst evaluating a novel machine learning approach to process data. Ultimately, this will contribute to the assessment of ice microphysics in regional forecasts.
References:
(1) Finney, D.L. et al. (2024). Earth Syst. Sci. Data, 16(5), 2141-2163.
(2) Jaffeux, L. et al. (2022). Atmos. Meas. Tech., 15(17), 5141-5157.
(3) Jaffeux, L. et al. (2024). EGUsphere (Preprint).
How to cite: Alkilani-Brown, E., Blyth, A., Finney, D., Deva, C., Field, P., and Crosier, J.: In-situ characterisation of graupel in deep convective cloud, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7218, https://doi.org/10.5194/egusphere-egu25-7218, 2025.
