3 citations · 4 across the 8 of their papers we have counts for
8 papers
Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling
Pritthijit Nath, Sebastian Schemm, Peter Haynes +2
Machine-learnt corrections can complement numerical weather prediction provided that they operate stably within an evolving numerical model. In this study, we couple the Met Office…
Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
Pritthijit Nath, Sebastian Schemm, Henry Moss +3
Weather and climate models rely on parametrisations to represent unresolved sub-grid processes. Traditional schemes rely on fixed coefficients that are weakly constrained and tuned…
FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models
Pritthijit Nath, Sebastian Schemm, Henry Moss +3
Sub-grid parameterisations in climate models are traditionally static and tuned offline, limiting adaptability to evolving states. This work introduces FedRAIN-Lite, a federated re…
Towards a Foundation Model for Communication Systems
Davide Buffelli, Sowmen Das, Yu-Wei Lin +5
Artificial Intelligence (AI) has demonstrated unprecedented performance across various domains, and its application to communication systems is an active area of research. While cu…
Improving Tropical Cyclone Forecasting With Video Diffusion Models
Zhibo Ren, Pritthijit Nath, Pancham Shukla
Tropical cyclone (TC) forecasting is crucial for disaster preparedness and mitigation. While recent deep learning approaches have shown promise, existing methods often treat TC evo…
Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models
Zhangyue Ling, Pritthijit Nath, César Quilodrán-Casas
This study explores the application of diffusion models in the field of typhoons, predicting multiple ERA5 meteorological variables simultaneously from Digital Typhoon satellite im…
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models
Pritthijit Nath, Henry Moss, Emily Shuckburgh +1
This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely…
Forecasting Tropical Cyclones with Cascaded Diffusion Models
Pritthijit Nath, Pancham Shukla, Shuai Wang +1
As tropical cyclones become more intense due to climate change, the rise of Al-based modelling provides a more affordable and accessible approach compared to traditional methods ba…