Conditional Flow Matching for Probabilistic Downscaling of Maximum 3-day Snowfall in Alaska
Precipitation in complex terrain is governed by orographic processes operating at scales of a few kilometers, yet climate models typically run at resolutions of 50--100~km where this topographic detail is absent. Dynamical downscaling with high-resolution regional models such as WRF can resolve these processes, but the computational cost -- months of wall-clock time per scenario -- precludes the large ensembles needed for uncertainty quantification. We present WxFlow, a conditional generative model based on flow matching that learns to map coarse-resolution climate model output and high-resolution topography to calibrated probabilistic ensembles of fine-scale precipitation fields. Applied...
| Authors | Douglas Brinkerhoff, Elizabeth Fischer |
|---|---|
| Primary category | physics.comp-ph |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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