Stochastic Parameterizations: Better Modelling of Temporal Correlations using Probabilistic Machine Learning. - Raghul Parthipan; Hannah M. Christensen; J. Scott Hosking; Damon J. Wischik (CoRR, 2022)

Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation. - Raghul Parthipan; Damon J. Wischik (CoRR, 2022)

Taylorformer: Probabilistic Predictions for Time Series and other Processes. - Omer Nivron; Raghul Parthipan; Damon J. Wischik (CoRR, 2023)

A Temporal Bias Correction using a Machine Learning Attention model. - Omer Nivron; Damon J. Wischik; Mathieu Vrac; Emily Shuckburgh (CoRR, 2024)

Defining error accumulation in ML atmospheric simulators. - Raghul Parthipan; Mohit Anand; Hannah M. Christensen; J. Scott Hosking; Damon J. Wischik (CoRR, 2024)

Regularization of ML models for Earth systems by using longer model timesteps. - Raghul Parthipan; Mohit Anand; Hannah M. Christensen; Frederic Vitart; Damon J. Wischik; Jakob Zscheischler (CoRR, 2025)