I am a DPhil student on the NERC UKRI Doctoral Training Programme in Environmental Research, supervised by Philip Stier and Hannah Christensen. My DPhil research focuses on evaluating and constraining global km-scale climate models. My first project focused on the , what it can tell us about convective organisation, and how we can use it to evaluate km-scale models. I am currently exploring how we can use machine learning models to better understand cloud and convection biases in the models.
Before starting my doctoral research, I obtained an MInf Informatics from the University of Edinburgh which focused on machine learning and data analysis.
Previous projects
- I developed deep learning models to merge active and passive satellite sensors and thereby create as part of an interdisciplinary team of researchers. This project was part of the (FDL) summer research sprint.
- I joined the FDL's Instrument-to-Instrument Translation (ITI) team to extend the to enable intercalibrating and harmonising satellite observations from Earth observing satellites via unsupervised machine learning. As part of this work, we developed , a software package automating the download, geoprocessing, and preprocessing of remote sensing data for machine learning projects.
Ask me about
Machine learning, Python, regridding issues and rowing.
Research interests
atmospheric physics
clouds
storm-resolving models
machine learning