Deep Learning Atmospheric Features

Lead Research Organisation: University of Reading
Department Name: Computer Science

Abstract

The detection of interesting features (cyclones, atmospheric rivers, fronts) in meteorological data has a long history of ever-increasing fidelity as newer techniques have been developed exploiting increasing computer power and more mathematical sophistication. Recently, the armoury of tools has been increased by the addition of newer techniques borrowed from Computer Vision. Early work suggests they may have higher levels of accuracy in feature detection than existing techniques, but in the case of Deep Learning this comes with enormous requirements on computing - at least when applied to the raw data - and with that enormous requirements on data storage.

In the future we expect to run large ensembles of climate simulations (that is, many realisations of what might occur under a specific set of scenarios). Today we would write out all the data, and then investigate the output data for interesting features. As resolution increases, just storing all that data will be difficult, let alone doing the feature detection on the stored data. A possible way to handle this in the future would be to look for the features in each one of the ensemble members as the model runs - and only write out the simulations which we already know have interesting features.

This project is about testing and comparing the fidelity of a range of detection techniques to both raw high- resolution climate model data and both reduced precision and reduced resolution variants of the same data. The goal will be to develop a learning technique that can be used during the model simulation and identify such features without itself being enormously computationally expensive. This will most likely be done by using reduced versions of the data, rather than changing the techniques, but different techniques may work better at different levels of reduced data. However, if we do need to change and/or modify techniques, we will be asking ourselves "Which are the most important building blocks for a robust and versatile learning? Which are the crucial inner layers and neurons for effective training?"

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
NE/R008868/1 01/10/2017 30/09/2022
2137770 Studentship NE/R008868/1 01/11/2018 31/10/2022 Daniel Galea