Algorithms for multimodal data-based inference and prediction for climate science applications


This project will develop new multimodal deep learning algorithms that are able to capture the relationship between diverse data and image modalities – such as infrared imager information, hyperspectral infrared sounding information or active remote sensing – in order to deliver relevant insights in outstanding climate science challenges such as aerosol-cloud-precipitation-climate interactions.

The project will also develop robust deep learning algorithms that are able to deal with a number of issues arising in the climate science domain such as: i) uncertainty in the testing/training data, ii) different degrees of uncertainty in different data modalities, and iii) small data.

The algorithms will also be applied to real data to determine historical auto-conversation rates from actual satellite observations. These novel observations are critical to evaluate the representation of precipitation.


Miguel Rodrigues (supervisor), Johannes Quaas (co-supervisor), Alberto Arribas (MetOffice, non-academic advisor)


3 months at University of Leipzig, 3 months at the MetOffice InformaticsLab

Enrolment in Doctoral degree

PhD in Electronic & Electrical Engineering at University College London

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