Publications from NERC Open Research Archive

2025

Moreno-Rodenas, Antonio. Verbist, Koen. Mertens, Annelies. Gerritsma, Isabel. Deng, Jing. Haag, Arjen. Taner, Ümit. Nuttall, Jonathan David. Dahm, Ruben. Meshgi, Ali. Korving, Hans. Bianchini, Silvia. Tofani, Veronica. Casagli, Nicola. Ray, Patrick. Rahat, Saiful Haque. Guan, Jianzhao. Chen, Yin. Zhang, Lei. Shi, Hongling. Kaltenborn, Julia. Bente, Kim. McDonald, Andrew ORCID record for McDonald, Andrew. Derksen, Chris. Amarnath, Giriraj. (2025) Applications of AI for water management.

Uebbing, Lars. Joakimsen, Harald L. Luppino, Luigi T. Martinsen, Iver. McDonald, Andrew ORCID record for McDonald, Andrew. Wickstrøm, Kristoffer K. Lefèvre, Sébastien. Arnt-Børre, Salberg,. Hosking, Scott J. ORCID record for Hosking, Scott J.. Jenssen, Robert. (2025) Investigating the Impact of Feature Reduction for Deep Learning-based Seasonal Sea Ice Forecasting.

Coca-Castro, Alejandro ORCID record for Coca-Castro, Alejandro. Fouilloux, Anne ORCID record for Fouilloux, Anne. Barros Lourenço, Ricardo ORCID record for Barros Lourenço, Ricardo. McDonald, Andrew ORCID record for McDonald, Andrew. Rao, Yuhan ORCID record for Rao, Yuhan. Hosking, Scott ORCID record for Hosking, Scott. (2025) Improving the reproducibility in geoscientific papers: lessons learned from a Hackathon in climate science.

2023

Galib, A. H. McDonald, A. ORCID record for McDonald, A.. Tan, P-N. Luo, L. (2023) Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning.

1. Nilas – a new approach to sea ice forecasting

   Nilas produces high resolution, physically realistic sea ice forecasts which outperform physical models for the 1 to 15 day range.

   Read more of: Nilas – a new approach to sea ice forecasting

   

7. IceNet – Machine Learning for Seasonal Sea Ice Forecasting

   IceNet is a deep learning system that forecasts Arctic sea ice. The system is trained on climate simulations and observational data. It outperforms state-of-the-art dynamical models, especially for extreme sea ice events.

   Read more of: IceNet – Machine Learning for Seasonal Sea Ice Forecasting

   

1. PolarSim – AI forecasting and modelling

   PolarSim delivers AI forecasting, modelling, downscaling and emulation tools for the polar regions

   Read more of: PolarSim – AI forecasting and modelling