408 citations · 1k across the 11 of their papers we have counts for
8 papers · 1 filter
AIMIP Phase 1: systematic evaluations of AI weather and climate models
Brian Henn, Christopher S. Bretherton, Nikolay Koldunov +18
We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a…
Advancing Seasonal Prediction of Tropical Cyclone Activity with a Hybrid AI-Physics Climate Model
Gan Zhang, Megha Rao, Janni Yuval +1
Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs explor…
Neural general circulation models optimized to predict satellite-based precipitation observations
Janni Yuval, Ian Langmore, Dmitrii Kochkov +1
Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. Here, we present a hybrid model that is trained directly on satellite-bas…
Neural General Circulation Models for Weather and Climate
Dmitrii Kochkov, Janni Yuval, Ian Langmore +13
General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics…
The intensification of winter mid-latitude storms in the Southern Hemisphere
Rei Chemke, Yi Ming, Janni Yuval
The strength of mid-latitude storm tracks shapes weather and climate phenomena in the extra-tropics, as these storm tracks control the daily to multi-decadal variability of precipi…
Non-local parameterization of atmospheric subgrid processes with neural networks
Peidong Wang, Janni Yuval, Paul A. O'Gorman
Subgrid processes in global climate models are represented by parameterizations which are a major source of uncertainties in simulations of climate. In recent years, it has been su…
Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision
Janni Yuval, Paul A. O'Gorman, Chris N. Hill
A promising approach to improve climate-model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms tha…
Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
Janni Yuval, Paul A. O'Gorman
Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leadi…