Machine learning cloud microphysics parameterization achieves stable decade-long online coupling in ICON with performance comparable to the classical graupel scheme while eliminating two tuning parameters.
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The work supplies an end-to-end differentiable PyTorch interface for PDE solvers and demonstrates it on RANS closure correction for two compressible-flow test cases.
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From stable online coupling to decade-long climate simulations: A machine learning parameterization for cloud microphysics in ICON
Machine learning cloud microphysics parameterization achieves stable decade-long online coupling in ICON with performance comparable to the classical graupel scheme while eliminating two tuning parameters.