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Keyword: atmospheric boundary layer ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

   Behrens, Beucler, Iglesias‐Suarez et al. · Journal of Advances in Modeling Earth Systems · 2025

   The authors built stochastic deep learning parameterizations of subgrid processes from a superparameterized Earth System Model, tested them with uncertainty quantification, and ran hybrid simulations with partial coupling.

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   1. UncheckedCombining several neural networks into a multi-member parameterization represents convection, especially in the boundary layer, better than single networks do.“We show that the multi-member (MM) parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs.”
   2. Unchecked“The respective uncertainty quantification illustrates that methods 2) and 3) are advantageous compared to a dropout-based DNN parameterization regarding the spread of convective processes.”
   3. UncheckedIn hybrid simulations, multi-member machine learning parameterizations reproduced tropical extreme precipitation better than a traditional convection scheme.“Despite this, the MM parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization.”