Hybrid physics-ML & parameterization★ Highlight
Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations
Why it matters. Shows ML corrections learned from 3 km X-SHiELD improve a 200 km model's forecasts and precipitation, a key step toward ML-corrected climate models.
In one sentence. Machine-learned nudging tendencies correct a 200-km FV3GFS model toward 3-km X-SHiELD, gaining 18 hours of 3-7 day skill and 30% better precipitation.
Abstract
Abstract Global atmospheric “ storm‐resolving ” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse‐grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real‐geography coarse‐grid model (FV3GFS with a 200 km grid) to be closer to those of a 40‐day reference simulation using X‐SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea‐surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse‐grid model without machine learning corrections has too few clouds, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine‐grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hr of skill at 3–7 days leads and time‐mean precipitation patterns are improved 30% by applying the ML correction. Adding machine‐learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time‐mean upper tropospheric temperature and zonal wind patterns thereafter.
Labels
- Topic
- Hybrid physics-ML & parameterization, Global medium-range NWP
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 3–15 days
- Domain
- Global
- Data
- km-scale or high-res simulation
- Status
- Research
- Identifiers
- DOI:10.1029/2021ms002794 OpenAlex:W4206971393
- Added
- 2026-10-22
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