Prediction Beyond the Medium Range With an Atmosphere‐Ocean Model That Combines Physics‐Based Modeling and Machine Learning

This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics‐based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022, https://doi.org/10.1029/2021ms002712), which tested the approach...

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Bibliographic Details
Published in:	Journal of advances in modeling earth systems Vol. 17; no. 4
Main Authors:	Patel, Dhruvit, Arcomano, Troy, Hunt, Brian, Szunyogh, Istvan, Ott, Edward
Format:	Journal Article
Language:	English
Published:	Washington John Wiley & Sons, Inc 01.04.2025
Subjects:	Atmospheric circulation Atmospheric circulation models Climate models Deep layer El Nino El Nino phenomena forecasting ENSO and teleconnections forecasting global precipitation Forecasting models General circulation models Heat content hybrid modeling Machine learning Madden-Julian oscillation Modelling Ocean models Oceans Parameterization Physics Planetary waves Precipitation Rossby waves Sea surface temperature subseasonal‐to‐seasonal Surface temperature Variables Weather forecasting weather forecasting beyond the medium range
ISSN:	1942-2466, 1942-2466
Online Access:	Get full text
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