Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Pushing the frontiers in climate modelling and analysis with machine learning
Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond…
Weather and climate predicted accurately — without using a supercomputer
A cutting-edge global model of the atmosphere combines machine learning with a numerical model based on the laws of physics. This ‘hybrid’ system accurately predicts the weather — and even shows…
Climate sensitivity and relative humidity changes in global storm-resolving model simulations of climate change
The climate simulation frontier of a global storm-resolving model (GSRM; or k-scale model because of its kilometer-scale horizontal resolution) is deployed for climate change simulations. The…
Probabilistic Emulation of a Global Climate Model with Spherical DYffusion
Data-driven deep learning models are on the verge of transforming global weather forecasting. It is an open question if this success can extend to climate modeling, where long inference rollouts and…
The precipitation response to warming and CO2 increase: A comparison of a global storm resolving model and CMIP6 models.
Global storm-resolving models (GSRMs) can explicitly resolve some of deep convection are now being integrated for climate timescales. GSRMs are able to simulate more realistic precipitation…
Emulation of cloud microphysics in a climate model
We present a machine learning based emulator of a microphysics scheme for condensation and precipitation processes (Zhao-Carr) used operationally in a global atmospheric forecast model (FV3GFS). Our…
A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation
Coarse-grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse-grained cloud fields from a fine-grid reference model are a natural target for a…
Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics
High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting…
Global Precipitation Correction Across a Range of Climates Using CycleGAN
Accurate precipitation simulations for various climate scenarios are critical for understanding and predicting the impacts of climate change. This study employs a Cycle‐generative adversarial…
Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation
Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce…