VIRTUAL MEETING | Machine Learning Applications in the Atmospheric Sciences

SPEAKER | Dr Samantha Adams, Data Science Research Manager, Met Office Informatics Lab

ABSTRACT | In recent years the exploitation of Machine Learning in many different domains has expanded considerably due to the increasing availability of large datasets and compute power. Machine Learning is not a new concept to the atmospheric sciences and techniques such as Generalised Linear Modelling, clustering, dimension reduction and even Neural Networks have been in use for many years. However, in recent years new techniques within the Deep Learning field have made impressive progress in solving hard problems in challenging domains (for example, image classification, object recognition and natural language processing). These methods open new opportunities for the atmospheric sciences that may revolutionize some areas of model development, data assimilation, post-processing and data analysis.
This talk will give a broad overview of some of the current application areas in the atmospheric sciences. Potential challenges with the adoption of Machine Learning into this domain are also discussed.

DISCLAIMER | Although this meeting was hosted by The Royal Meteorological Society, the work shared is that of the speakers and the views and comments of this meeting do not reflect the views and opinions of the Society.