1 citations · 2 across the 4 of their papers we have counts for
4 papers
How different are deterministic physics suites when coupled to fixed model dynamics and why?
Edward Groot, Hannah Christensen, Xia Sun +5
It is often difficult to attribute uncertainty and errors in atmospheric models to designated model components. This is because sub-grid parameterised processes interact strongly w…
Regularization of ML models for Earth systems by using longer model timesteps
Raghul Parthipan, Mohit Anand, Hannah M Christensen +3
Regularization is a technique to improve generalization of machine learning (ML) models. A common form of regularization in the ML literature is to train on data where similar inpu…
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…