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GOES-16 satellite image

Lilli Freischem (she/her)

Graduate Student - NERC DTP

Sub department

  • Atmospheric, Oceanic and Planetary Physics

Research groups

  • Atmospheric processes
  • Climate processes
lilli.freischem@physics.ox.ac.uk
  • About
  • Teaching
  • Publications

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 fractal analysis of high clouds in the tropics, 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 NextGEMS 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 global 3D cloud retrievals as part of an interdisciplinary team of researchers. This project was part of the Frontier Development Lab's (FDL) summer research sprint.
  • I joined the FDL's Instrument-to-Instrument Translation (ITI) team to extend the ITI tool to enable intercalibrating and harmonising satellite observations from Earth observing satellites via unsupervised machine learning. As part of this work, we developed rs_tools, 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

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