Papers › Stable Emulation of an Entire Suite of Model Physics in a State-of-the-Art GCM using a...
Stable Emulation of an Entire Suite of Model Physics in a State-of-the-Art GCM using a Neural Network
Alexei Belochitski, Vladimir Krasnopolsky
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
There has been a lot of recent interest in developing hybrid models that couple deterministic numerical model components to statistical model components derived using machine learning techniques. One approach that we follow in this pilot study is to replace an existing computationally expensive deterministic model component with its fast machine-learning-based emulator, leading to the model speed-up and/or improvement. We developed a shallow neural network-based emulator of a complete suite of atmospheric physics parameterizations in NCEP Global Forecast System (GFS) general circulation model (GCM). The suite emulated by a single NN includes radiative transfer, cloud macro- and micro-physics, shallow and deep convection, boundary layer processes, gravity wave drag, land model, etc. NCEP GFS with the neural network replacing the original suite of atmospheric parameterizations produces stable and realistic medium range weather forecasts for 24 initial conditions spanning all months of 2018. It also remains stable in a year-long AMIP-like experiment and in the run with a quadrupled horizontal resolution. We present preliminary results of parallel runs, evaluating the accuracy and speedup of the resulting hybrid GCM.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections