Data Reduction

Volumes of data being generated by simulations and next generation observation platforms are huge, but not all the data being produced needs to be stored as is. We are investigating techniques to identify data that does not need to be stored (and conversely identify data that is important), and to compress data when it does need to be stored.

We ourselves are directly investigating:


Bryan Lawrence
Professor of Weather and Climate Computing
Daniel Galea
Former Ph.D Student
Computational Modelling Services (NCAS)
CMS colleagues work on our projects as necessary.