University of Cambridge > Talks.cam > CEDSG-AI4ER > Using deep learning for precipitation nowcasting applications

Using deep learning for precipitation nowcasting applications

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The UK has a very good radar network coverage, with a continually growing amount of available precipitation observation data. Machine learning offers an opportunity to harness more of the potential of this valuable data, especially for nowcasting applications (0-2 hour forecasts). The London Terminal Manoeuvring Area is (normally!) one of the busiest sectors of airspace in the world, where improved short-term forecasts would be particularly beneficial for air traffic management. The aim of this work is to understand if and how deep learning methods could add value over more traditional precipitation nowcasting methods. Results from a neural network that makes use of latent space to represent the uncertainty in the system will be presented. Objective verification results and example case studies will be shown, comparing the machine learning model predictions to the Met Office’s current operational model. An ensemble approach using this model will be presented as well as consideration of challenges in these methods.

At the beginning of the talk I will also give a brief overview of other Data Science activities going on at the Met Office.

This talk is part of the CEDSG-AI4ER series.

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