Models struggle to represent the coupled microphysical, turbulent and radiative processes within widespread, long-lived marine cold-air outbreak (CAO) cloud fields, contributing to forecast and climate biases. Here we combine ground-based and satellite measurements to initialize and constrain large-eddy simulations (...
T. Juliano, F. Tornow, A. Fridlind et al.· Atmospheric Chemistry and Ph...· 1 citation
We propose a fully automated, structural error-aware, interpretable climate model parameter estimation method that leverages Perturbed Parameter Ensembles (PPEs). It is based on history matching and aligns with an increasingly-used iterative simulation-emulation-calibration methodology. The method is motivated by the n...
Qingyuan Yang, A. Semie, Brian Medeiros et al.· 0 citations
An explainable contrastive learning model that maps 5 monthly cloud and radiation fields into a shared representation space and demonstrates that explainable representations of climate fields can attribute model differences to specific variables, regions, seasons, and physical parameters.
Da Fan, D. J. Gagne, G. Elsaesser et al.· 0 citations
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