©2018. The Authors. The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high-resolution model output, but it remains poorly understood how such parameterizations behave when fully coupled in a general circulation model (GCM) and whether they are useful for simulations of climate change or extreme events. Here we focus on these issues using idealized tests in which an ML-based parameterization is trained on output from a conventional parameterization and its performance is assessed in simulations with a GCM. We use an ensemble of decision trees (random forest) as the ML algorithm, and this has t...
The representation of nonlinear subgrid processes, especially clouds, has been a major source of unc...
The representation of nonlinear subgrid processes, especially clouds, has been a major source of unc...
Artificial intelligence (AI) has been sparked by significant advancements in Graphic Processing Unit...
©2018. The Authors. The parameterization of moist convection contributes to uncertainty in climate m...
Data and code for a random-forest convection scheme associated with the paper: "Using machine learn...
Data and code for a random-forest convection scheme associated with the paper: "Using machine learn...
Abstract Deficiencies in convection trigger functions, used in deep convection parameterizations in ...
Thesis (Ph.D.)--University of Washington, 2019The primary result of this work is that concepts from ...
Abstract Current moist physics parameterization schemes in general circulation models (GCMs) are the...
Global climate models represent small-scale processes such as convection using subgrid models known ...
Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric mo...
Convection-permitting weather forecasting models allow for prediction of rainfall events with increa...
Dynamical weather and climate prediction models underpin many studies of the Earth system and hold t...
Dynamical weather and climate prediction models underpin many studies of the Earth system and hold t...
© 2018 Royal Meteorological Society Global climate models (GCMs) are useful tools for assessing clim...
The representation of nonlinear subgrid processes, especially clouds, has been a major source of unc...
The representation of nonlinear subgrid processes, especially clouds, has been a major source of unc...
Artificial intelligence (AI) has been sparked by significant advancements in Graphic Processing Unit...
©2018. The Authors. The parameterization of moist convection contributes to uncertainty in climate m...
Data and code for a random-forest convection scheme associated with the paper: "Using machine learn...
Data and code for a random-forest convection scheme associated with the paper: "Using machine learn...
Abstract Deficiencies in convection trigger functions, used in deep convection parameterizations in ...
Thesis (Ph.D.)--University of Washington, 2019The primary result of this work is that concepts from ...
Abstract Current moist physics parameterization schemes in general circulation models (GCMs) are the...
Global climate models represent small-scale processes such as convection using subgrid models known ...
Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric mo...
Convection-permitting weather forecasting models allow for prediction of rainfall events with increa...
Dynamical weather and climate prediction models underpin many studies of the Earth system and hold t...
Dynamical weather and climate prediction models underpin many studies of the Earth system and hold t...
© 2018 Royal Meteorological Society Global climate models (GCMs) are useful tools for assessing clim...
The representation of nonlinear subgrid processes, especially clouds, has been a major source of unc...
The representation of nonlinear subgrid processes, especially clouds, has been a major source of unc...
Artificial intelligence (AI) has been sparked by significant advancements in Graphic Processing Unit...