2 citations · 5 across the 6 of their papers we have counts for
6 papers · 1 filter
Zero-Shot Time Series Forecasting with Covariates via In-Context Learning
Andreas Auer, Raghul Parthipan, Pedro Mercado +5
Pretrained time series models, capable of zero-shot forecasting, have demonstrated significant potential in enhancing both the performance and accessibility of time series forecast…
Defining error accumulation in ML atmospheric simulators
Raghul Parthipan, Mohit Anand, Hannah M. Christensen +2
Machine learning (ML) has recently shown significant promise in modelling atmospheric systems, such as the weather. Many of these ML models are autoregressive, and error accumulati…
Machine Learning for Stochastic Parametrisation
Hannah M. Christensen, Salah Kouhen, Greta Miller +1
Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale va…
Taylorformer: Probabilistic Modelling for Random Processes including Time Series
Omer Nivron, Raghul Parthipan, Damon J. Wischik
We propose the Taylorformer for random processes such as time series. Its two key components are: 1) the LocalTaylor wrapper which adapts Taylor approximations (used in dynamical s…
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation
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? Typically, the training data comprises co…
Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking +1
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 paramet…