WRF-JAX v1.1.28: a differentiable regional weather model with fully coupled physics, numerically consistent with WRF and matching 128-process CPU performance on a single GPU
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Shijin Yuan, Tianyu Shen, Bin Mu, Zhen Wei, Bo Qin, Guokun Dai · arXiv: 2610.35095v1 · Summary page
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A weather model that can work backward
Weather models usually calculate how an initial atmospheric state evolves. A differentiable model can also estimate how changing that starting state would change a later result, a capability useful for optimization and sensitivity analysis. A preprint introduces WRF-JAX v1.1.28, a JAX reimplementation of the WRF 4.5 dynamical core and complete CONUS physics suite. Across 27 ten-day integrations, its numerical differences evolved similarly to an internal WRF comparison baseline, while a single NVIDIA A800 delivered performance comparable to 128-process CPU runs.
The researchers also backpropagated through 100 steps with the physics coupled, calculating gradients for the initial wind, temperature and moisture fields. Random-direction checks assessed those gradients against perturbation-based estimates. This brings a widely used style of regional weather simulation closer to end-to-end optimization, but the gradients are approximate, and comparable numerical behavior should not be confused with a demonstrated improvement in weather forecasts.
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