Skillful high-resolution weather forecasting independent of physical models
Accurate and timely weather forecasts are critical for high-impact decisions in modern society. Machine-learning-based weather prediction is emerging as an alternative for producing initial conditions, forecasts, and even both in end-to-end systems. These methods deliver predictions faster and often with higher skill than traditional numerical weather prediction (NWP). However, even end-to-end models typically rely on NWP-generated reanalyses for supervision, thereby inheriting the biases and resolution limitations of those NWPs, and limiting adaptation to settings where suitable reanalysis products are unavailable, infrequently updated, or expensive to produce. Here we introduce ObsCast,...
| Authors | Pengcheng Zhao, Siqi Xiang, Weixin Jin et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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