FlowDA: Accurate, Low-Latency Weather Data Assimilation via Flow Matching

• URL: http://arxiv.org/abs/2602.06800v1
• Date: Fri, 06 Feb 2026 15:53:29 GMT
• Title: FlowDA: Accurate, Low-Latency Weather Data Assimilation via Flow Matching
• Authors: Ran Cheng, Lailai Zhu,
• Abstract summary: FlowDA is a low-latency weather-scale generative DA framework based on flow matching.<n>Experiments across observation rates decreasing from $3.9%$ to $0.1%$ demonstrate superior performance of FlowDA.
• Score: 7.337861034978726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data assimilation (DA) is a fundamental component of modern weather prediction, yet it remains a major computational bottleneck in machine learning (ML)-based forecasting pipelines due to reliance on traditional variational methods. Recent generative ML-based DA methods offer a promising alternative but typically require many sampling steps and suffer from error accumulation under long-horizon auto-regressive rollouts with cycling assimilation. We propose FlowDA, a low-latency weather-scale generative DA framework based on flow matching. FlowDA conditions on observations through a SetConv-based embedding and fine-tunes the Aurora foundation model to deliver accurate, efficient, and robust analyses. Experiments across observation rates decreasing from $3.9\%$ to $0.1\%$ demonstrate superior performance of FlowDA over strong baselines with similar tunable-parameter size. FlowDA further shows robustness to observational noise and stable performance in long-horizon auto-regressive cycling DA. Overall, FlowDA points to an efficient and scalable direction for data-driven DA.

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