QWRF-Net: A Quantum-Wavelet Framework with Rectified Flow for Short-Term Precipitation Nowcasting
2026-08-03 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors developed a new method called QWRF-Net to improve short-term rainfall predictions, which are important for early warnings about floods. Their method breaks down complex rain patterns into different scales using wavelets and applies a quantum-inspired approach to better capture these features. They then use a special technique called rectified flow to predict future rainfall more accurately without errors piling up over time. Tests on real radar data showed their method works well, especially for heavy rain and preserving detailed rain structures. This approach could help improve flood warnings and related weather applications.
short-term precipitation nowcastingwavelet decompositionquantum-inspired modulationrectified flowradar precipitation datamulti-scale representationnon-autoregressive decoderhydrometeorological early warningconvective rainfallflash floods
Authors
Zhuo Wang, Chaorong Li, Wenjie Luo, Chuanhu Deng
Abstract
Short-term precipitation nowcasting is important for hydrometeorological early warning, especially when intense convective rainfall may trigger urban flooding, flash floods, and other high-impact hazards. A key challenge in warning-oriented nowcasting is that radar precipitation fields contain strongly coupled multi-scale structures, while forecast quality often degrades at later lead times, making it difficult to preserve intense precipitation cores and their spatial organization over the full warning-relevant horizon. To address this problem, we propose QWRF-Net, a quantum-wavelet framework with rectified flow for short-term precipitation nowcasting. The core idea is to improve the conditional representation of precipitation by explicitly decomposing latent features into wavelet sub-bands and then performing differentiated quantum-inspired modulation in the decomposed latent space, before generating future sequences through a rectified-flow-based non-autoregressive decoder. Experiments on the KNMI radar and SEVIR benchmarks under a unified evaluation protocol show that QWRF-Net achieves favorable overall performance, with relatively consistent gains at medium-to-high precipitation thresholds, on an extreme-event subset, and in preserving intense precipitation cores and fine-scale structures. Ablation results further indicate that wavelet-based scale disentanglement, differentiated sub-band modulation, and flow-based generation provide complementary benefits within the proposed framework. Overall, these results suggest that jointly enhancing multi-scale precipitation representation and stable multi-step generation is a promising direction for warning-oriented short-term precipitation nowcasting. The observed improvements may also provide a more useful precipitation basis for downstream hydrological and warning-related applications.