19 citations · 31 across the 5 of their papers we have counts for
5 papers
Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates
Aniruddha Bora, Shixuan Zhang, Khemraj Shukla +3
Coarse resolution, imperfect parameterizations, and uncertain initial states and forcings limit Earth-system model (ESM) predictions. Traditional bias correction via data assimilat…
A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics
Benedikt Barthel Sorensen, Alexis Charalampopoulos, Shixuan Zhang +3
Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully-resolved climate simulations remain com…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Statistics of extreme events in coarse-scale climate simulations via machine learning correction operators trained on nudged datasets
Alexis-Tzianni Charalampopoulos, Shixuan Zhang, Bryce Harrop +2
This work presents a systematic framework for improving the predictions of statistical quantities for turbulent systems, with a focus on correcting climate simulations obtained by…
Learning bias corrections for climate models using deep neural operators
Aniruddha Bora, Khemraj Shukla, Shixuan Zhang +3
Numerical simulation for climate modeling resolving all important scales is a computationally taxing process. Therefore, to circumvent this issue a low resolution simulation is per…