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Keyword: global storm-resolving models ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Global climate modeling with improved precipitation characteristics by learning physics (GRIST-MPS v1.0) from global storm-resolving modeling

   Wang, Zhang, Han et al. · Geoscientific model development · 2026

   The authors trained machine-learning physics on 5 km storm-resolving data to replace all physics in a 120 km climate model, reporting stable runs and improved precipitation against satellite data.

   Unchecked3 claims
   Show 3 claims

   1. UncheckedA machine-learning-enhanced climate model ran stable 6-year AMIP-type simulations with a realistic climatology comparable to a skilful conventional model.“This ML-enhanced GCM sustains stable 6-year Atmospheric Model Intercomparison Project (AMIP) type simulations and produces a realistic climatology comparable to that of a skillful GCM.”
   2. UncheckedThe machine-learning-enhanced climate model reduces overly strong rainbands and an overly wide ITCZ seen in the conventional setup, judged against GPM data.“It effectively mitigates the biases of excessively strong rainbands and an overly wide ITCZ in the conventional configuration, when compared with the Global Precipitation Measurement (GPM) data.”
   3. UncheckedA hybrid machine-learning climate model simulates how often it rains more closely, with less excess light tropical rain than the conventional version.“Moreover, the hybrid ML-GCM better captures precipitation frequency, notably mitigating the overproduction of light tropical rainfall.”