ML4WX

Hybrid physics-ML & parameterization

Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity

Ryan Lagerquist, David D. Turner, Imme Ebert‐Uphoff, Jebb Q. Stewart

Journal of Atmospheric and Oceanic Technology · · NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), Colorado State University (incl. CIRA)

Paper

In one sentence. U-Net++ and U-Net3+ networks emulate full shortwave and longwave RRTM radiation in GFSv16, running 7510 and 90 times faster with reliable heating rates.

Labels

Topic
Hybrid physics-ML & parameterization
Type
New model or method
Method
CNN / U-Net
Domain
Global
Data
NWP analysis or forecasts (IFS, GFS, ...), ERA5 / reanalysis
Status
Pre-operational / experimental
Identifiers
DOI:10.1175/jtech-d-23-0012.1 OpenAlex:W4386325634
Added
2026-10-22

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