Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Old dog, new trick: Reservoir computing advances machine learning for climate modeling
Physics-informed machine learning (ML) applied to geophysical simulation is developing explosively. Recently, graph neural net and vision transformer architectures have shown 1-7 day global weather…
A Global Survey of Rotating Convective Updrafts in the GFDL X‐SHiELD 2021 Global Storm Resolving Model
We present the global characteristics of rotating convective updrafts in the 2021 version of GFDL's eXperimental System for High‐resolution prediction on Earth‐to‐Local Domains (X‐SHiELD), a…
Emulating Fast Processes in Climate Models
Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating…
Improving the predictions of ML-corrected climate models with novelty detection
While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irregular inputs than…
Machine-learned climate model corrections from a global storm-resolving model
Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution ( (cid:38) 50 km) than is optimal for accurately resolving important…
Machine-learned climate model corrections from a global storm-resolving model: Performance across the annual cycle
One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned state-dependent corrections to the prognosed model tendencies, such that the climate model…
Pace v0.1: A python-based performance-portable implementation of the FV3 dynamical core
Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software…
Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations
Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse‐resolution global atmosphere model with real…
Impact of Warmer Sea Surface Temperature on the Global Pattern of Intense Convection: Insights From a Global Storm Resolving Model
Intense convection (updrafts exceeding 10 m s−1) plays an essential role in severe weather and Earth's energy balance. Despite its importance, how the global pattern of intense convection changes in…
Correcting a coarse-grid climate model in multiple climates by machine learning from global 25-km resolution simulations
Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse-resolution global atmosphere model with real…