Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
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…
Tropical Cirrus Are Highly Sensitive to Ice Microphysics Within a Nudged Global Storm‐Resolving Model
Cirrus dominate the longwave radiative budget of the tropics. For the first time, the variability in cirrus properties and longwave cloud radiative effects (CREs) that arises from using different…
Kilometer-scale global warming simulations and active sensors reveal changes in tropical deep convection
Changes in tropical deep convection with global warming are a leading source of uncertainty for future climate projections. A comparison of the responses of active sensor measurements of cloud ice…
ACE: A fast, skillful learned global atmospheric model for climate prediction
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter,…
Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion
In climate science and meteorology, local precipitation predictions are limited by the immense computational costs induced by the high spatial resolution that simulation methods require. A common…
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…
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…
Improving the reliability of ML-corrected climate models with novelty detection
The use of machine learning (ML) for the online correction of coarse-resolution atmospheric models has proven effective in reducing biases in near-surface temperature and precipitation rate.…
Pace v0.2: a Python-based performance-portable atmospheric model
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…
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 (ML) state-dependent corrections to the prognosed model tendencies, such that the climate model…