no code implementations • 23 May 2024 • Raghul Parthipan, Mohit Anand, Hannah M. Christensen, J. Scott Hosking, Damon J. Wischik
Machine learning (ML) has recently shown significant promise in modelling atmospheric systems, such as the weather.
no code implementations • 12 Feb 2024 • Hannah M. Christensen, Salah Kouhen, Greta Miller, Raghul Parthipan
Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner.
no code implementations • 30 May 2023 • Omer Nivron, Raghul Parthipan, Damon J. Wischik
We propose the Taylorformer for time series and other random processes.
1 code implementation • 8 Oct 2022 • Raghul Parthipan, Damon J. Wischik
How can we learn from all available data when training machine-learnt climate models, without incurring any extra cost at simulation time?
1 code implementation • 28 Mar 2022 • Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, Damon J. Wischik
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.