University of Cambridge > Talks.cam > CEDSG-AI4ER > Stochastic Parameterizations: Better Modelling of Temporal Correlations using ML

Stochastic Parameterizations: Better Modelling of Temporal Correlations using ML

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Herbie Bradley.

The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Using stochasticity and machine learning have led to better models but there is a lack of work on combining the benefits from both. We show that by using a physically-informed recurrent neural network within a probabilistic framework, our resulting model for the Lorenz 96 atmospheric simulation is competitive and often superior to both a bespoke baseline and an existing probabilistic machine-learning (GAN) one. This is due to a superior ability to model temporal correlations compared to standard first-order autoregressive schemes. The model also generalises to unseen regimes. We evaluate across a number of metrics from the literature, but also discuss how the probabilistic metric of likelihood may be a unifying choice for future probabilistic climate models.

This talk is part of the CEDSG-AI4ER series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2022 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity