2 citations · 2 across the 3 of their papers we have counts for
5 papers · 1 filter
Extremes on Rewind: Generating 1,000-Member Ensembles Initialized at a Final Condition
Jerry Lin, Mu-Ting Chien, Mansi Sakarvadia +1
Scenario planning for rare, high-impact events often requires massive ensembles to stochastically sample relevant trajectories. Although autoregressive weather emulators can effici…
Crowdsourcing the Frontier: Advancing Hybrid Physics-ML Climate Simulation via a $50,000 Kaggle Competition
Jerry Lin, Zeyuan Hu, Tom Beucler +24
Subgrid machine-learning (ML) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without…
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations
Zeyuan Hu, Akshay Subramaniam, Zhiming Kuang +6
Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid proces…
Stress-testing the coupled behavior of hybrid physics-machine learning climate simulations on an unseen, warmer climate
Jerry Lin, Mohamed Aziz Bhouri, Tom Beucler +2
Accurate and computationally-viable representations of clouds and turbulence are a long-standing challenge for climate model development. Traditional parameterizations that crudely…
Navigating the Noise: Bringing Clarity to ML Parameterization Design with O(100) Ensembles
Jerry Lin, Sungduk Yu, Liran Peng +6
Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-re…