Hybrid physics-ML & parameterization

Modeling Noise: Paths toward AI-Enabled Stochastic Earth System Models and Parameterizations

S. Hagos, A. Subramanian, L. Leung, Peter A. G. Watson, P. Dueben, Chi-Dong Zhang, F. Judt, Judith Berner, H. Christensen, W. Chapman

Preprint · 09 March 2021 · ECMWF, NSF NCAR / UCAR, NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), University of Oxford

Paper

In one sentence. A DOE perspective argues for AI-enabled stochastic parameterizations that account for atmospheric state, replacing hand-tuned noise in Earth system models.

Labels

Topic

Hybrid physics-ML & parameterization, Probabilistic & ensemble forecasting

Type

Position or perspective paper

Domain

Global

Data

Other

Status

Research

Identifiers

DOI:10.2172/1769749 OpenAlex:W3153421128

Added

2026-10-22

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