3 papers
physics.ao-ph2020
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…
physics.soc-ph2020
Optimizing testing policies for detecting COVID-19 outbreaks
Janni Yuval, Mor Nitzan, Neta Ravid Tannenbaum +1
The COVID-19 pandemic poses challenges for continuing economic activity while reducing health risks. While these challenges can be mitigated through testing, testing budget is ofte…
physics.ao-ph2020
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…