1 citations · 1 across the 3 of their papers we have counts for
4 papers
Reconstructing the Aerosol State from Partial Observations with Generative Modeling
E. Saleh, S. Ghaffari, J. H. Curtis +4
Key aerosol properties that shape climate -- such as CCN activity, scattering and absorption, and ice nucleation efficiency -- are difficult to infer from measurements that typical…
Uncertainty Quantification in Reduced-Order Gas-Phase Atmospheric Chemistry Modeling using Ensemble SINDy
Lin Guo, Xiaokai Yang, Zhonghua Zheng +2
Uncertainty quantification during atmospheric chemistry modeling is computationally expensive as it typically requires a large number of simulations using complex models. As large-…
Atmospheric chemistry surrogate modeling with sparse identification of nonlinear dynamics
Xiaokai Yang, Lin Guo, Zhonghua Zheng +2
Modeling atmospheric chemistry is computationally expensive and limits the widespread use of atmospheric chemical transport models. This computational cost arises from solving high…
Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields
Manho Park, Zhonghua Zheng, Nicole Riemer +1
We developed and applied a machine-learned discretization for one-dimensional (1-D) horizontal passive scalar advection, which is an operator component common to all chemical trans…