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Status: Unchecked ×Keyword: relative humidity ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation

   Watt‐Meyer, Brenowitz, Clark et al. · Journal of Advances in Modeling Earth Systems · 2024

   Researchers trained a neural network on coarse-grained storm-resolving simulation data to replace heating and moistening parameterizations in a 200 km global model, with stable online runs but some unrealistic features.

   Unchecked3 claims
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   1. UncheckedA neural network's offline predictions of heating and moistening rates have R² of 0.4 to 0.8 at most heights and latitudes.“The coefficient of determination R 2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes.”
   2. Unchecked“Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations.”
   3. UncheckedIn this coarse-grid model run with neural-network physics, tropical circulation and upper-troposphere relative humidity came out unrealistic.“However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic.”