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Status: Unchecked ×Keyword: stratiform precipitation ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Observation‐Informed Machine Learning Convective Scheme: A New Training Strategy

   Huang, Li, Liao et al. · Geophysical Research Letters · 2026

   The authors propose a new way to train a machine-learning convection scheme using nudged simulations as targets, and report better stability, precipitation and temperature in a low-resolution hybrid model.

   Unchecked3 claims
   Show 3 claims

   1. UncheckedA hybrid machine-learning and physics climate model reduces the long-standing overestimate of the share of tropical rain that is convective.“In addition, the resulting hybrid model alleviates the long‐standing overestimation of the tropical convective‐to‐total precipitation ratio.”
   2. UncheckedThe hybrid machine-learning and physics climate model, using the new training strategy, ran stably for more than a year in this study.“Lastly, the hybrid model can run stably for over a year, highlighting the effectiveness and generalizability of the new training strategy.”
   3. UncheckedIn a low-resolution hybrid ML-physics model, the new ML convection scheme strengthens heavy stratiform and total rainfall and reduces temperature bias.“The results from the low‐resolution hybrid ML‐physics model show that the new ML scheme performs substantially better, as it strengthens heavy stratiform and total precipitation and reduces temperature bias, acting as an effective online bias correction mechanism.”