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Neural general circulation models for weather and climate
General circulation models (GCMs) are the foundation of weather and climate prediction,.
GCMs are physics-based simulators that combine a numerical solver for large-scale …
GCMs are physics-based simulators that combine a numerical solver for large-scale …
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 …
because of its kilometer-scale horizontal resolution) is deployed for climate change …
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
Modern climate projections lack adequate spatial and temporal resolution due to
computational constraints. A consequence is inaccurate and imprecise predictions of critical …
computational constraints. A consequence is inaccurate and imprecise predictions of critical …
The Precipitation Response to Warming and CO2 Increase: A Comparison of a Global Storm Resolving Model and CMIP6 Models
Global storm‐resolving models (GSRMs) that can explicitly resolve some of deep convection
are now being integrated for climate timescales. GSRMs are able to simulate more realistic …
are now being integrated for climate timescales. GSRMs are able to simulate more realistic …
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 …
atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less …
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 …
and predicting the impacts of climate change. This study employs a Cycle‐generative …
Interpretable multiscale machine learning‐based parameterizations of convection for ICON
Abstract Machine learning (ML)‐based parameterizations have been developed for Earth
System Models (ESMs) with the goal to better represent subgrid‐scale processes or to …
System Models (ESMs) with the goal to better represent subgrid‐scale processes or to …
Exploring the Potential of Hybrid Machine-Learning/Physics-Based Modeling for Atmospheric/Oceanic Prediction Beyond the Medium Range
This paper explores the potential of a hybrid modeling approach that combines machine
learning (ML) with conventional physics-based modeling for weather prediction beyond the …
learning (ML) with conventional physics-based modeling for weather prediction beyond 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 …
forecasting. It is an open question if this success can extend to climate modeling, where long …
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 …
fields, and coarse‐grained cloud fields from a fine‐grid reference model are a natural target …