MZ-Rain: Moisture-Budget-Guided Zero-Inflated Model for Station-Level Precipitation Nowcasting

📄 arXiv: 2609.04864v1 📥 PDF

作者: Yifang Zhang, Shengwu Xiong, Henan Wang, Wenjie Yin, Yuqiang Zhang, Chen Zhou, Hua Chen, Qile Zhao, Pengfei Duan

分类: cs.AI

发布日期: 2026-09-04

备注: 16 pages, 6 figures

💡 一句话要点

提出MZ-Rain以解决站级降水短期预报中的物理建模与零膨胀问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)

关键词: 降水预报 时间序列预测 深度学习 物理建模 湿度预算 零膨胀 sLSTM 气象预测

📋 核心要点

  1. 现有方法在站级降水短期预报中缺乏物理指导，导致预测结果与实际物理过程不符。
  2. MZ-Rain通过湿度预算方程分解降水形成过程，并引入自适应Tweedie建模策略，解决了零膨胀问题。
  3. 实验结果表明，MZ-Rain在多个评估指标上超越了强基线，尤其在重降水事件的预测上表现优异。

📝 摘要（中文）

准确的站级降水短期预报对农业、水资源管理和灾害预防至关重要，通常被视为时间序列预测问题。然而，传统的时间序列建模技术在站级降水预报中面临两个主要挑战：一是缺乏物理指导建模，导致预测偏离降水形成的物理过程；二是降水数据中干旱间隔占主导，造成零膨胀现象，掩盖了有意义的降水模式。为了解决这些问题，本文提出了MZ-Rain，一个基于湿度预算的零膨胀sLSTM框架，通过湿度预算方程分解降水形成过程，并引入自适应Tweedie建模策略，显著提升了降水预测的准确性。

🔬 方法详解

问题定义：本文旨在解决站级降水短期预报中的物理建模不足和数据零膨胀问题。现有方法未能充分考虑气象变量在降水形成中的不同作用，导致预测偏差，同时干旱间隔的主导地位使得降水模式难以识别。

核心思路：MZ-Rain通过湿度预算方程引导模型设计，分解降水形成过程为多个特定路径，利用sLSTM网络分别捕捉这些路径的时间演变，从而实现更准确的降水预测。

技术框架：MZ-Rain的整体架构包括多个sLSTM分支，分别对应湿度存储、湿度运输、表面蒸发和降水持续性。同时，模型引入自适应Tweedie策略，平衡干湿区分和降水量估计。

关键创新：MZ-Rain的主要创新在于将物理过程与深度学习相结合，通过湿度预算方程指导模型设计，显著提高了降水预测的物理一致性和准确性。

关键设计：模型采用自适应Tweedie损失函数，动态调整降水均值，同时将降水发生作为辅助任务进行联合学习，优化了干湿区分和降水量的预测能力。网络结构上，sLSTM分支的设计使得不同降水过程的特征能够被有效捕捉。

🖼️ 关键图片

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📊 实验亮点

MZ-Rain在多个评估指标上均优于强基线，特别是在CSI、FAR、MSE和MAE等指标上表现突出。尤其在重降水事件的预测中，模型展现出显著的技能提升，证明了其在实际应用中的有效性。

🎯 应用场景

该研究在农业、水资源管理和气象预报等领域具有广泛的应用潜力。通过提高降水预测的准确性，MZ-Rain能够为农作物灌溉、洪水预警和干旱监测提供更可靠的数据支持，进而增强应对气候变化的能力。

📄 摘要（原文）

Accurate station-level precipitation nowcasting is critical for agriculture, water resource management, and disaster prevention, which typically is formulated as a time series forecasting problem. However, conventional time-series modeling techniques face two major challenges in addressing station-level precipitation nowcasting: (1) Lack of Physics-Guided Modeling}, where meteorological variables are treated as a homogeneous set without accounting for their distinct roles in precipitation formation, leads to predictions that deviate from the physical processes governing precipitation. (2) Severe zero inflation in precipitation, where dry intervals dominate the dataset, obscuring meaningful precipitation patterns and complicating the predictive modeling. To address these challenges, we propose \textbf{MZ-Rain}, a moisture-budget-guided zero-inflated sLSTM framework for station-level precipitation nowcasting. Guided by the moisture budget equation, MZ-Rain decomposes the precipitation formation process into process-specific pathways corresponding to moisture storage, moisture transport, surface evaporation, and precipitation persistence, and captures their temporal evolution through dedicated sLSTM branches. To account for the zero-inflated nature of precipitation, MZ-Rain introduces an adaptive Tweedie modeling strategy that adaptively modulates the rainfall mean while jointly learning precipitation occurrence as an auxiliary task, enabling the model to better balance dry-wet discrimination and quantitative precipitation estimation. Extensive experiments across diverse geographical and climatic regimes demonstrate that MZ-Rain consistently outperforms strong baselines on multiple evaluation metrics, including CSI, FAR, MSE, and MAE. In particular, the model exhibits superior skill in forecasting heavy precipitation events, while benefiting from physically grounded process modeling.