Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders Abstract. Large-scale atmospheric dynamics modulate the occurrence of extreme precipitation events and provide sources of predictability of these events on timescales ranging from days to decades. In ...

Excited to share that our new paper is out in Weather and Climate Dynamics and was selected as a highlight publication🎉

We introduce a machine-learning approach to identify physically interpretable drivers of regional precipitation extremes. Have a look here: wcd.copernicus.org/articles/6/9...

Identifying probabilistic weather regimes targeted to a local-scale impact variable | Environmental Data Science | Cambridge Core Identifying probabilistic weather regimes targeted to a local-scale impact variable - Volume 3

Excited for my first post here to be my first PhD paper recently published in Environmental Data Science!

We introduce a probabilistic machine learning method to identify the dynamical atmospheric drivers of a local impact of interest. Have a look here bit.ly/3CFtZta.