VIRTUAL MEETING | Machine Learning Applications in the Atmospheric Sciences
LOCATION
UPDATE: Registration has now closed.
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 gave 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.
This meeting was being held during a lunch time hour 12:00-13:00. The event process remains the same, whereby you will receive the joining link for the meeting 24 hours in advance, if registering before the 4th March, or on the day of the event if registering between the evening of the 4th and the morning of the 5th.
The presentation lasted for approximately 45 minutes, with a 15 minute Q&A session at the end.
UPDATE: Registration has now closed.
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 gave 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.
This meeting was being held during a lunch time hour 12:00-13:00. The event process remains the same, whereby you will receive the joining link for the meeting 24 hours in advance, if registering before the 4th March, or on the day of the event if registering between the evening of the 4th and the morning of the 5th.
The presentation lasted for approximately 45 minutes, with a 15 minute Q&A session at the end.