发表机构
School of Computer Science and Technology (School of Artificial Intelligence), Yibin University; College of Computer Science and Engineering, Chongqing University of Technology（宜宾大学计算机科学与技术学院（人工智能学院）; 重庆理工大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对短期降水临近预报中多尺度结构耦合及后期预报质量下降的问题，提出带整流流的量子-小波框架QWRF-Net，在多基准测试中表现优异，为相关应用提供了更可靠的降水预报基础。
AI 中文摘要
短期降水临近预报对于水文气象预警至关重要，尤其是强对流降雨可能引发城市内涝、山洪等高影响灾害时。面向预警的临近预报面临的关键挑战是雷达降水场包含强耦合的多尺度结构，而预报质量往往在后期预报时效下降，难以在整个预警相关的时间范围内保留强降水核心及其空间组织。为解决该问题，本文提出QWRF-Net，一种用于短期降水临近预报的带整流流的量子-小波框架。核心思路是通过将潜在特征显式分解为小波子带，在分解后的潜在空间中执行差异化的类量子调制，再通过基于整流流的非自回归解码器生成未来序列，以改进降水的条件表示。在KNMI雷达和SEVIR基准上，采用统一评估协议的实验表明，QWRF-Net在整体性能上表现优异，在中高降水阈值、极端事件子集以及保留强降水核心和精细结构方面均有相对稳定的提升。消融实验进一步表明，基于小波的尺度解耦、差异化子带调制和基于流的生成在该框架内提供了互补的增益。总体而言，这些结果表明，联合增强多尺度降水表示和稳定多步生成是面向预警的短期降水临近预报的有前景方向，观测到的改进也可能为下游水文和预警相关应用提供更有用的降水基础。
英文摘要
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.
